Bayesian approach for spare parts replenishment policies under uncertaintiesIJERD Editor
The legislative constraints, the need to optimize the dismantling process, the introduction of recycled
parts on the spare parts market are reinforced since the systems in end-of-life phase have become increasingly
profitable. There are few works that treat recycled spare parts integration problem in economic models of
inventory control. These works do not consider uncertainty. In order to manage more realistically the inventory
control of spare parts, we propose a probabilistic model formalized by a Bayesian network. The model is used to
identify the best purchase policy. More precisely, it allows choosing the best proportions between new spare
parts (NSP) and recycled spare parts (RSP) by taking into account the traditional criteria of inventory control
and the availability of the spare parts on the market. The proposed method provides a decision-making tool for
manufacturers who are interested both in reducing the costs of stocks and guaranteeing a minimal availability in
an uncertain environment.
A MODEL FOR PRICING USED PRODUCTS AND REPLACEMENT PARTS IN REVERSE LOGISTICSijmvsc
A unique specification in remanufacturing is the uncertainty of returned flows. This makes the coordination
between supply and demand difficult for the firm. As a result, remanufacturers typically use pricing tools to
control the return flow of used products.
In this study, a model is presented for optimal quantity and price of used products and the price of used
products with replacement parts after collection and consolidation based on their quality levels. This model
was developed from the perspective of the remanufacturer and the consolidation center. When the
consolidation center receives the remanufacturer's demand, the consolidation center and the
remanufacturer use the proposed model for evaluating the optimal quantity and the acquisition price of
used products as well as the price provided by the remanufacturer to the consolidation center so that they
both reach maximum profit. The supply of used products is random. The presented model is an integer
nonlinear programming (INLP) model. Consequently, due to the complexity of the problem, The SA and GA
metaheuristic methods are used to solve the model
Unit-Operation Nonlinear Modeling for Planning and Scheduling ApplicationsAlkis Vazacopoulos
The focus of this chapter is to detail the quantity and quality modeling aspects of production flowsheets found in all process industries. Production flowsheets are typically at a higher-level than process flowsheets given that in many cases more direct business or economic related decisions are being made such as maximizing profit and performance for the overall plant and/or for several integrated plants together with shared resources. These decisions are usually planning and scheduling related, often referred to as production control, which require a larger spatial and temporal scope compared to more myopic process flowsheets which detail the steady or unsteady-state material, energy and momentum balances of a particular process unit-operation over a relatively short time horizon. This implies that simpler but still representative mathematical models of the individual processes are necessary in order to solve the multi time-period nonlinear system using nonlinear optimizers such as successive linear programming (SLP) and sequential quadratic programming (SQP). In this chapter we describe six types of unit-operation models which can be used as fundamental building blocks or objects to formulate large production flowsheets. In addition, we articulate the differences between continuous and batch processes while also discussing several other important implementation issues regarding the use of these unit-operation models within a decision-making system. It is useful to also note that the quantity and quality modeling system described in this chapter complements the quantity and logic modeling used to describe production and inventory systems outlined in Zyngier and Kelly (2009).
Automation of Material Handling: Design & Analysis of Bucket System on an Inc...IJRES Journal
Material handling systems are widely used in the industry for transportation of raw material, finished
product, storage and retrieval of materials. The increase cost of labor and manual work or operation is now
replaced by semiautomatic or automatic system. These low cost systems are not only cost efficient but also
enhance productivity & address the issues related to labor problem. Material handling equipment is designed
such that they make simple, economical, fast and safe loading and unloading with less human intervention. The
research work is related to develop an automated material Handling system MHS in the chemical Industry
where the bulk material transportation is done manually. The successful completion of this research work has
generated design and analysis of an automated bucket on inclined rail system for the chemical industry, which is
quick, efficient and safety to labor.
Bayesian approach for spare parts replenishment policies under uncertaintiesIJERD Editor
The legislative constraints, the need to optimize the dismantling process, the introduction of recycled
parts on the spare parts market are reinforced since the systems in end-of-life phase have become increasingly
profitable. There are few works that treat recycled spare parts integration problem in economic models of
inventory control. These works do not consider uncertainty. In order to manage more realistically the inventory
control of spare parts, we propose a probabilistic model formalized by a Bayesian network. The model is used to
identify the best purchase policy. More precisely, it allows choosing the best proportions between new spare
parts (NSP) and recycled spare parts (RSP) by taking into account the traditional criteria of inventory control
and the availability of the spare parts on the market. The proposed method provides a decision-making tool for
manufacturers who are interested both in reducing the costs of stocks and guaranteeing a minimal availability in
an uncertain environment.
A MODEL FOR PRICING USED PRODUCTS AND REPLACEMENT PARTS IN REVERSE LOGISTICSijmvsc
A unique specification in remanufacturing is the uncertainty of returned flows. This makes the coordination
between supply and demand difficult for the firm. As a result, remanufacturers typically use pricing tools to
control the return flow of used products.
In this study, a model is presented for optimal quantity and price of used products and the price of used
products with replacement parts after collection and consolidation based on their quality levels. This model
was developed from the perspective of the remanufacturer and the consolidation center. When the
consolidation center receives the remanufacturer's demand, the consolidation center and the
remanufacturer use the proposed model for evaluating the optimal quantity and the acquisition price of
used products as well as the price provided by the remanufacturer to the consolidation center so that they
both reach maximum profit. The supply of used products is random. The presented model is an integer
nonlinear programming (INLP) model. Consequently, due to the complexity of the problem, The SA and GA
metaheuristic methods are used to solve the model
Unit-Operation Nonlinear Modeling for Planning and Scheduling ApplicationsAlkis Vazacopoulos
The focus of this chapter is to detail the quantity and quality modeling aspects of production flowsheets found in all process industries. Production flowsheets are typically at a higher-level than process flowsheets given that in many cases more direct business or economic related decisions are being made such as maximizing profit and performance for the overall plant and/or for several integrated plants together with shared resources. These decisions are usually planning and scheduling related, often referred to as production control, which require a larger spatial and temporal scope compared to more myopic process flowsheets which detail the steady or unsteady-state material, energy and momentum balances of a particular process unit-operation over a relatively short time horizon. This implies that simpler but still representative mathematical models of the individual processes are necessary in order to solve the multi time-period nonlinear system using nonlinear optimizers such as successive linear programming (SLP) and sequential quadratic programming (SQP). In this chapter we describe six types of unit-operation models which can be used as fundamental building blocks or objects to formulate large production flowsheets. In addition, we articulate the differences between continuous and batch processes while also discussing several other important implementation issues regarding the use of these unit-operation models within a decision-making system. It is useful to also note that the quantity and quality modeling system described in this chapter complements the quantity and logic modeling used to describe production and inventory systems outlined in Zyngier and Kelly (2009).
Automation of Material Handling: Design & Analysis of Bucket System on an Inc...IJRES Journal
Material handling systems are widely used in the industry for transportation of raw material, finished
product, storage and retrieval of materials. The increase cost of labor and manual work or operation is now
replaced by semiautomatic or automatic system. These low cost systems are not only cost efficient but also
enhance productivity & address the issues related to labor problem. Material handling equipment is designed
such that they make simple, economical, fast and safe loading and unloading with less human intervention. The
research work is related to develop an automated material Handling system MHS in the chemical Industry
where the bulk material transportation is done manually. The successful completion of this research work has
generated design and analysis of an automated bucket on inclined rail system for the chemical industry, which is
quick, efficient and safety to labor.
The Disassembly Line: Balancing and Modeling provides in-depth information on this complex process essential to remanufacturing, recycling, and environmentally conscious manufacturing. This pioneering work offers efficient techniques required to solve problems involving the number of workstations required and the disassembly sequencing of end-of-life products on the disassembly line.
On August 29, 2005, Hurricane Katrina devastated New Orleans and t.docxhopeaustin33688
On August 29, 2005, Hurricane Katrina devastated New Orleans and the Gulf coast. Proctor & Gamble coffee manufacturing, with brands such as Folgers that get over half of their supply from sites in New Orleans, was severely impacted by the hurricane. Six months later, there were, as a P&G executive told the New York Times “still holes on the shelves” where P&G’s brands should be. Given your new insights from supply chain management, how would you solve/avoid a situation like this?
Module 2 - Case
Supply Chain Design
Case Assignment
Welcome to the second case study for this course.
Assignment: Please read the article below (available through ProQuest), then in a 3-4 page paper discuss the article and integrated supply chains.
Assignment Expectations
The authors of the article do a pretty good job of explaining the concept but I would like you to tell me what they mean in your own words. What is an integrated supply chain? What are the key elements/challenges in an integrated supply chain? What are the specific benefits to firms that implement superior supply chain management?Write a 3-4 page paper, using the same format as module one.
Integrated supply chains to be exploredAlan Johnson. Manufacturers' Monthly. Sydney: May 2007. It is attached
Abstract:
"The key challenge is to integrate supply chain capabilities to provide a seamless solution from potential design through to end delivery. End users are looking for a complete supply chain where there is single accountability and responsibility for delivery," said O'[Brien].
Module 2 - Background
Supply Chain Design
The following information will give you a good background on the importance of having a properly designed supply chain. Please review the information presented below to assist you with the assignments. I encourage you to surf the internet for more information on supply chain design.
Required Materials
You are not required to read all of these articles, but you may if you wish to choose to read several to further your knowledge. You are encouraged to surf the internet to gather additional resources in order to research your topic more thoroughly.
Start off this module by reviewing the article below on Supply Chain Design.
Beamon, B. M. (1998). Supply Chain Design and Analysis: Models and Methods. International Journal of Production Economics, 55(3), 281-294. Accessed August 10, 2009, at http://www.sclgme.org
The ProQuestdata base provides the articles below concerning changing supply chains and supply chain security.
Johnson, A., (2007). Integrated supply chains to be explored. Manufacturers' Monthly. Sydney. Available in the Trident Online Library.
Rogers, D., Lockman, D., Schwerdt, G., O'Donnell, B., & Huff, R., (2004). Supply Chain Security. Material Handling Management, 59(2), 15-18. Available in the Trident Online Library.
Supply Chain Design and Analysis:
Models and Methods
Benita M. Beamon
University of Washington
Industrial Engineering
Box 352650
Seattle, WA 98195.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Abstract- In this article the authors, based on the VisiMix Software, the experience of VisiMix users and personal knowledge from more than ten years of experience using VisiMix for API, Fine Chemicals and others, processes simulation, show a Method for Scale Down – Scale Up of Batch – Semi Batch operations built under Hydrodynamics study of the Mixing procedure in the reactor system. The use of the recommended method will offer the user the possibility to achieve the best results during production stage with saving among time and currency, and at the same time increasing the knowledge of the performed process. Several examples at the end of the article show the benefits of the proposed VisiMix Method Loops for Scale Down - Scale Up and Hydrodynamics Considerations.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Chemical production is a result of several chemical reactions and purification steps. Purification steps and processes yield are a direct function of the level of understanding of the reaction system. Reaction quality results have a tremendous impact in separation technology.
Chemical production is frequently performed on stirred vessels that are operated at batch or semi-batch configuration. The choice process configuration is determined at the development stage of the project. Therefore, if the chemical reaction and mixing are not well understood, wrong selections will be adopted in the process development
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
The present paper developed an integrated closed-loop supply chain model by considering social responsibility. The novelty of this research is considering social responsibility in the model. In order to achieve this goal, a three-objective mathematical model was presented with the following aims: 1) Minimizing the costs, 2) Maximizing social responsibility or social benefits of the model, and 3) Minimizing the adverse environmental effects. The mathematical method which is applied proves the validity of the model.
A MULTIPLE PRODUCTION SETUPS INVENTORY MODEL FOR IMPERFECT ITEMS CONSIDERING ...orajjournal
This paper presents an imperfect manufacturing system in which production ability can produce items in m
production setups and rework the imperfect quality items in one rework setup. Rework is one of the main
issues in reverse logistic and green supply chain to reduce production cost and environmental problem.
The aim of this research is to minimize the total inventory cost by determining the optimal cycle time and
the optimal number of production setups. The convexity of the inventory model is derived by using
mathematical software. The result is illustrated with numerical example for the model. The effects of the
problem parameters upon the optimal solution are examined numerically. This model can be applied to
optimizing the total inventory cost for the business enterprises where production rate and demand rate are
time dependent and salvage value is incorporated to the deteriorated items.
IMPORTANCE OF PACKAGING WASTE RECYCLING PLANTS IN REVERSE LOGISTICS AND AN AS...IAEME Publication
Reverse logistics is one of the adopted supply chain processes and becomes more important because of economic and ecological conditions, administrative and social responsibilities, sustainable development, environment protection laws, and the aim of material and resource use. Recycling is a term that means recyclable waste materials are processed with various recycling methods and prepared to use in manufacturing as raw materials. Especially, used materials such as paper, glass, plastics and metals which are called 'packaging waste' are revalued in favour of economic. In most of the countries, local authorities are responsible for waste handling issues such as collection, transportation and disposal. In relation to waste management, the whole cycle of generation of wastes, their storage, collection and transport, and their eventual treatment and disposal are taken into condsideration.
CS672 – System Engineering and Analysis Discussion 8 - 1123201.docxmydrynan
CS672 – System Engineering and Analysis
Discussion 8 - 11/23/2018
Samson kamal Victor
Chapter 15, Question 1:
Logistics:
Logistics as set of activities to satisfy the physical need from producer to manufacture a product that meet the customer demand, also the product shipment, storage and maintenance in efficient and effective manner in less time (Logistics-management, n.d.)
According to Manoj (2015) study found the following elements in logistics:
1. Logistics and maintenance support planning: proper initial planning and ensure all the activities are properly coordinate throughout the life cycle
2. Logistics and maintenance personnel: depends on the personnel skills, error rates, attrition rate, maintenance labor hour
3. Training and training support: provide training to personnel understanding of logistics and enough materials for training
4. Supply support: provide support to supply chain such as MTBR, spares / repair demand rate and process time, inventory of items in time, probability of spare availability
5. Software reliability and maintainability, complexity, modules, cost
6. Documentation of Information system that contain technical information such as access time, size, number of data, processing and implementation time
7. Maintenance facilities for items processed, time, and utilities
8. Packaging, handling, storage, and transportation
9. Test, measurement, handling and support equipment: all equipments like monitoring tools, special test equipment, operational support, level of maintenance
10. Logistics information throughout the organization with all the activities in timely and secured manner
Chapter 15, Question 3:
Supply chain:
It is an activity of shifting the product from producer to consumer, also there lots of people involved in supply chain manufacturer, distributer, and consumer (Investopedia, 2003)
Function and activities:
Collecting the product from the manufacture, distributing the product to the dealer or third party dealers and consumer is the end user who is going make use of the product. The activities in the each layer to verify the product quality, expiration period, directing to the location of customer need. If the product has any issue send back to the manufacturer
Supply chain management:
It manages the flow of the product movement from one hand to other hand. It has the planning to design and manages the raw material supply to manufacturer and once the product developed, shipping the product to the dealers, the reason is to build the competitive systems with help of logistics system to move the items to the desired location to satisfy the demand and measure the performance of supply chain activities (Investopedia, 2003)
Function:
- Aligning flows: cost, equipments, and data passed between customers and suppliers.
- Integrating functions: it integrates the functionality of logistics to ensure the SCM goals satisfied
- Coordinating processes: used to plan, source, make, deliver, and re.
Modelling of repairable items for production inventory with random deteriorationiosrjce
Keeping in view the concern about environmental protection, the study incorporate the concept of repairing in a production inventory model consisting of production system and repairing system over infinite
planning horizon. This study presents a forward production and reverse repairing system inventory model with a time dependent random deterioration function and increasing exponentially demand with the finite production rate is proportional to the demand rate at any instant. The shortages allow and excess demand is backlogged. Expressions for optimal parameter are obtained .We also obtained Production and repairing scheduling period, maximum inventory level and total average cost. Using calculus, optimum production policy is derived, which minimizes the total cost incurred
An Ex-Ante Evaluation for Solid Waste Treatment Facilities using LCCAcivejjour
The application of Life Cycle Cost Analysis (LCCA) in infrastructure facilities projects has been marginalised so far especially in real-life projects. In many cases, the significance of this tool is not the end result by itself but the improvements that can be made to the infrastructure facility design during and as a result of the LCCA model development. This paper presents lessons-learnt from analysing and developing a LCCA model for an actual integrated municipal solid waste management infrastructure facility using the anaerobic treatment technology and recycling. The development of the LCCA model for the facility involved several distinctive steps such as system analysis and disintegration, maintenance and operation cost data acquisition, identifying relevant performance indicators for each operation that can be utilized in tandem with the LCCA model, setting up serviceability threshold for each operation. In addition to model development description, the paper highlights the requirements needed and the impediments that may be encountered when developing LCCA model for solid waste management facilities. At the end, the paper concludes with providing recommendations for decision makers and researchers in this field based on the experience gained from the model development.
An Ex-Ante Evaluation for Solid Waste Treatment Facilities using LCCA civej
The application of Life Cycle Cost Analysis (LCCA) in infrastructure facilities projects has been marginalised so far especially in real-life projects. In many cases, the significance of this tool is not the end result by itself but the improvements that can be made to the infrastructure facility design during and as a result of the LCCA model development. This paper presents lessons-learnt from analysing and developing a LCCA model for an actual integrated municipal solid waste management infrastructure facility using the anaerobic treatment technology and recycling. The development of the LCCA model for the facility involved several distinctive steps such as system analysis and disintegration, maintenance and operation cost data acquisition, identifying relevant performance indicators for each operation that can be utilized in tandem with the LCCA model, setting up serviceability threshold for each operation. In addition to model development description, the paper highlights the requirements needed and the impediments that may be encountered when developing LCCA model for solid waste management facilities. At the end, the paper concludes with providing recommendations for decision makers and researchers in this field based on the experience gained from the model development.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
Measuring Environmental Performance of Supply Chaintheijes
Environmental performance is a hot topic for researchers in management science. It is also one of the major concerns of supply chain leaders. To assess this performance, there are increasingly many management tools. It is then appropriate to wonder the role of these tools in supply chain: are these tools meet real organizational needs? Or they are used to promote supply chain image face institutional constraints increasingly strong? In this context, many modules and methodologies have been established in literature in order to evaluate environmental performance of supply chain, since it has become an important issue for society. However, few of them analyze environmental impacts. So, this work presents an integrated methodology to perform this evaluation, based on issues which significantly affect the environment. We purpose a module which will allow the assessment of this performance.This module was tested in an automotive supply chain in north of Morocco.
Improving Supply Chain Activity using Simulationijsrd.com
The discovery through computational modeling and simulation has become the third pillar of science, alongside theory and experimentation. As computational power increases, simulation has gained in importance and has become a major research area, where highly parallel computation is utilized. In this dissertation, we have performed the simulation by selecting a single machine which is involved in manufacturing the highest number of products. Data are collected for all the processes involved in the manufacturing processes and an input modelling analysis is been done for the data collected. After the analysis is completed, a simulation model is constructed using ARENA which involved all the manufacturing process using the simulation tools. With the help of the simulation tools we will be able to identify activities causing the bottlenecks and delays in the entire manufacturing processes. Similarly, this simulation can be carried out for each and every machine of the company so that we can identify the bottlenecks and delays. As a result, the bottlenecks and delays can be reduced and the entire supply chain can be improved. This paper aims at combining supply chain management and simulation - to give an overview of both areas and shows how supply chain management can profit from simulation and also to identify the delays and bottlenecks in the overall manufacturing process. Lastly, a sample of how a supply chain can be optimized, in the simulation development suite ARENA.
The Disassembly Line: Balancing and Modeling provides in-depth information on this complex process essential to remanufacturing, recycling, and environmentally conscious manufacturing. This pioneering work offers efficient techniques required to solve problems involving the number of workstations required and the disassembly sequencing of end-of-life products on the disassembly line.
On August 29, 2005, Hurricane Katrina devastated New Orleans and t.docxhopeaustin33688
On August 29, 2005, Hurricane Katrina devastated New Orleans and the Gulf coast. Proctor & Gamble coffee manufacturing, with brands such as Folgers that get over half of their supply from sites in New Orleans, was severely impacted by the hurricane. Six months later, there were, as a P&G executive told the New York Times “still holes on the shelves” where P&G’s brands should be. Given your new insights from supply chain management, how would you solve/avoid a situation like this?
Module 2 - Case
Supply Chain Design
Case Assignment
Welcome to the second case study for this course.
Assignment: Please read the article below (available through ProQuest), then in a 3-4 page paper discuss the article and integrated supply chains.
Assignment Expectations
The authors of the article do a pretty good job of explaining the concept but I would like you to tell me what they mean in your own words. What is an integrated supply chain? What are the key elements/challenges in an integrated supply chain? What are the specific benefits to firms that implement superior supply chain management?Write a 3-4 page paper, using the same format as module one.
Integrated supply chains to be exploredAlan Johnson. Manufacturers' Monthly. Sydney: May 2007. It is attached
Abstract:
"The key challenge is to integrate supply chain capabilities to provide a seamless solution from potential design through to end delivery. End users are looking for a complete supply chain where there is single accountability and responsibility for delivery," said O'[Brien].
Module 2 - Background
Supply Chain Design
The following information will give you a good background on the importance of having a properly designed supply chain. Please review the information presented below to assist you with the assignments. I encourage you to surf the internet for more information on supply chain design.
Required Materials
You are not required to read all of these articles, but you may if you wish to choose to read several to further your knowledge. You are encouraged to surf the internet to gather additional resources in order to research your topic more thoroughly.
Start off this module by reviewing the article below on Supply Chain Design.
Beamon, B. M. (1998). Supply Chain Design and Analysis: Models and Methods. International Journal of Production Economics, 55(3), 281-294. Accessed August 10, 2009, at http://www.sclgme.org
The ProQuestdata base provides the articles below concerning changing supply chains and supply chain security.
Johnson, A., (2007). Integrated supply chains to be explored. Manufacturers' Monthly. Sydney. Available in the Trident Online Library.
Rogers, D., Lockman, D., Schwerdt, G., O'Donnell, B., & Huff, R., (2004). Supply Chain Security. Material Handling Management, 59(2), 15-18. Available in the Trident Online Library.
Supply Chain Design and Analysis:
Models and Methods
Benita M. Beamon
University of Washington
Industrial Engineering
Box 352650
Seattle, WA 98195.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Abstract- In this article the authors, based on the VisiMix Software, the experience of VisiMix users and personal knowledge from more than ten years of experience using VisiMix for API, Fine Chemicals and others, processes simulation, show a Method for Scale Down – Scale Up of Batch – Semi Batch operations built under Hydrodynamics study of the Mixing procedure in the reactor system. The use of the recommended method will offer the user the possibility to achieve the best results during production stage with saving among time and currency, and at the same time increasing the knowledge of the performed process. Several examples at the end of the article show the benefits of the proposed VisiMix Method Loops for Scale Down - Scale Up and Hydrodynamics Considerations.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Chemical production is a result of several chemical reactions and purification steps. Purification steps and processes yield are a direct function of the level of understanding of the reaction system. Reaction quality results have a tremendous impact in separation technology.
Chemical production is frequently performed on stirred vessels that are operated at batch or semi-batch configuration. The choice process configuration is determined at the development stage of the project. Therefore, if the chemical reaction and mixing are not well understood, wrong selections will be adopted in the process development
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
The present paper developed an integrated closed-loop supply chain model by considering social responsibility. The novelty of this research is considering social responsibility in the model. In order to achieve this goal, a three-objective mathematical model was presented with the following aims: 1) Minimizing the costs, 2) Maximizing social responsibility or social benefits of the model, and 3) Minimizing the adverse environmental effects. The mathematical method which is applied proves the validity of the model.
A MULTIPLE PRODUCTION SETUPS INVENTORY MODEL FOR IMPERFECT ITEMS CONSIDERING ...orajjournal
This paper presents an imperfect manufacturing system in which production ability can produce items in m
production setups and rework the imperfect quality items in one rework setup. Rework is one of the main
issues in reverse logistic and green supply chain to reduce production cost and environmental problem.
The aim of this research is to minimize the total inventory cost by determining the optimal cycle time and
the optimal number of production setups. The convexity of the inventory model is derived by using
mathematical software. The result is illustrated with numerical example for the model. The effects of the
problem parameters upon the optimal solution are examined numerically. This model can be applied to
optimizing the total inventory cost for the business enterprises where production rate and demand rate are
time dependent and salvage value is incorporated to the deteriorated items.
IMPORTANCE OF PACKAGING WASTE RECYCLING PLANTS IN REVERSE LOGISTICS AND AN AS...IAEME Publication
Reverse logistics is one of the adopted supply chain processes and becomes more important because of economic and ecological conditions, administrative and social responsibilities, sustainable development, environment protection laws, and the aim of material and resource use. Recycling is a term that means recyclable waste materials are processed with various recycling methods and prepared to use in manufacturing as raw materials. Especially, used materials such as paper, glass, plastics and metals which are called 'packaging waste' are revalued in favour of economic. In most of the countries, local authorities are responsible for waste handling issues such as collection, transportation and disposal. In relation to waste management, the whole cycle of generation of wastes, their storage, collection and transport, and their eventual treatment and disposal are taken into condsideration.
CS672 – System Engineering and Analysis Discussion 8 - 1123201.docxmydrynan
CS672 – System Engineering and Analysis
Discussion 8 - 11/23/2018
Samson kamal Victor
Chapter 15, Question 1:
Logistics:
Logistics as set of activities to satisfy the physical need from producer to manufacture a product that meet the customer demand, also the product shipment, storage and maintenance in efficient and effective manner in less time (Logistics-management, n.d.)
According to Manoj (2015) study found the following elements in logistics:
1. Logistics and maintenance support planning: proper initial planning and ensure all the activities are properly coordinate throughout the life cycle
2. Logistics and maintenance personnel: depends on the personnel skills, error rates, attrition rate, maintenance labor hour
3. Training and training support: provide training to personnel understanding of logistics and enough materials for training
4. Supply support: provide support to supply chain such as MTBR, spares / repair demand rate and process time, inventory of items in time, probability of spare availability
5. Software reliability and maintainability, complexity, modules, cost
6. Documentation of Information system that contain technical information such as access time, size, number of data, processing and implementation time
7. Maintenance facilities for items processed, time, and utilities
8. Packaging, handling, storage, and transportation
9. Test, measurement, handling and support equipment: all equipments like monitoring tools, special test equipment, operational support, level of maintenance
10. Logistics information throughout the organization with all the activities in timely and secured manner
Chapter 15, Question 3:
Supply chain:
It is an activity of shifting the product from producer to consumer, also there lots of people involved in supply chain manufacturer, distributer, and consumer (Investopedia, 2003)
Function and activities:
Collecting the product from the manufacture, distributing the product to the dealer or third party dealers and consumer is the end user who is going make use of the product. The activities in the each layer to verify the product quality, expiration period, directing to the location of customer need. If the product has any issue send back to the manufacturer
Supply chain management:
It manages the flow of the product movement from one hand to other hand. It has the planning to design and manages the raw material supply to manufacturer and once the product developed, shipping the product to the dealers, the reason is to build the competitive systems with help of logistics system to move the items to the desired location to satisfy the demand and measure the performance of supply chain activities (Investopedia, 2003)
Function:
- Aligning flows: cost, equipments, and data passed between customers and suppliers.
- Integrating functions: it integrates the functionality of logistics to ensure the SCM goals satisfied
- Coordinating processes: used to plan, source, make, deliver, and re.
Modelling of repairable items for production inventory with random deteriorationiosrjce
Keeping in view the concern about environmental protection, the study incorporate the concept of repairing in a production inventory model consisting of production system and repairing system over infinite
planning horizon. This study presents a forward production and reverse repairing system inventory model with a time dependent random deterioration function and increasing exponentially demand with the finite production rate is proportional to the demand rate at any instant. The shortages allow and excess demand is backlogged. Expressions for optimal parameter are obtained .We also obtained Production and repairing scheduling period, maximum inventory level and total average cost. Using calculus, optimum production policy is derived, which minimizes the total cost incurred
An Ex-Ante Evaluation for Solid Waste Treatment Facilities using LCCAcivejjour
The application of Life Cycle Cost Analysis (LCCA) in infrastructure facilities projects has been marginalised so far especially in real-life projects. In many cases, the significance of this tool is not the end result by itself but the improvements that can be made to the infrastructure facility design during and as a result of the LCCA model development. This paper presents lessons-learnt from analysing and developing a LCCA model for an actual integrated municipal solid waste management infrastructure facility using the anaerobic treatment technology and recycling. The development of the LCCA model for the facility involved several distinctive steps such as system analysis and disintegration, maintenance and operation cost data acquisition, identifying relevant performance indicators for each operation that can be utilized in tandem with the LCCA model, setting up serviceability threshold for each operation. In addition to model development description, the paper highlights the requirements needed and the impediments that may be encountered when developing LCCA model for solid waste management facilities. At the end, the paper concludes with providing recommendations for decision makers and researchers in this field based on the experience gained from the model development.
An Ex-Ante Evaluation for Solid Waste Treatment Facilities using LCCA civej
The application of Life Cycle Cost Analysis (LCCA) in infrastructure facilities projects has been marginalised so far especially in real-life projects. In many cases, the significance of this tool is not the end result by itself but the improvements that can be made to the infrastructure facility design during and as a result of the LCCA model development. This paper presents lessons-learnt from analysing and developing a LCCA model for an actual integrated municipal solid waste management infrastructure facility using the anaerobic treatment technology and recycling. The development of the LCCA model for the facility involved several distinctive steps such as system analysis and disintegration, maintenance and operation cost data acquisition, identifying relevant performance indicators for each operation that can be utilized in tandem with the LCCA model, setting up serviceability threshold for each operation. In addition to model development description, the paper highlights the requirements needed and the impediments that may be encountered when developing LCCA model for solid waste management facilities. At the end, the paper concludes with providing recommendations for decision makers and researchers in this field based on the experience gained from the model development.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
Measuring Environmental Performance of Supply Chaintheijes
Environmental performance is a hot topic for researchers in management science. It is also one of the major concerns of supply chain leaders. To assess this performance, there are increasingly many management tools. It is then appropriate to wonder the role of these tools in supply chain: are these tools meet real organizational needs? Or they are used to promote supply chain image face institutional constraints increasingly strong? In this context, many modules and methodologies have been established in literature in order to evaluate environmental performance of supply chain, since it has become an important issue for society. However, few of them analyze environmental impacts. So, this work presents an integrated methodology to perform this evaluation, based on issues which significantly affect the environment. We purpose a module which will allow the assessment of this performance.This module was tested in an automotive supply chain in north of Morocco.
Improving Supply Chain Activity using Simulationijsrd.com
The discovery through computational modeling and simulation has become the third pillar of science, alongside theory and experimentation. As computational power increases, simulation has gained in importance and has become a major research area, where highly parallel computation is utilized. In this dissertation, we have performed the simulation by selecting a single machine which is involved in manufacturing the highest number of products. Data are collected for all the processes involved in the manufacturing processes and an input modelling analysis is been done for the data collected. After the analysis is completed, a simulation model is constructed using ARENA which involved all the manufacturing process using the simulation tools. With the help of the simulation tools we will be able to identify activities causing the bottlenecks and delays in the entire manufacturing processes. Similarly, this simulation can be carried out for each and every machine of the company so that we can identify the bottlenecks and delays. As a result, the bottlenecks and delays can be reduced and the entire supply chain can be improved. This paper aims at combining supply chain management and simulation - to give an overview of both areas and shows how supply chain management can profit from simulation and also to identify the delays and bottlenecks in the overall manufacturing process. Lastly, a sample of how a supply chain can be optimized, in the simulation development suite ARENA.
Similar to Planning of the production activities of a manufacturing system in the context of a closed-loop supply chain..pdf (20)
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
Planning of the production activities of a manufacturing system in the context of a closed-loop supply chain..pdf
1. Is hereby this certificate to
INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY AND CURRENT
EDUCATIONAL RESEARCH (IJMCER)
www.ijmcer.com ISSN : 2581-7027
Planning of the production activities of a manufacturing system
in the context of a closed-loop supply chain
In recognition publication of the paper entitled
In IJMCER Journal Volume 4 Issue 6 November –
December 2022
Email:
ijmcer.research@gmail.com
Editor-In-Chief
IJMCER
Kibouka G. Richard
2. International Journal of Multidisciplinary and Current
Educational Research (IJMCER)
ISSN: 2581-7027 ||Volume|| 4 ||Issue|| 6 ||Pages 32-45 ||2022||
| Volume 4 | Issue 6| www.ijmcer.com | 32 |
Planning of the production activities of a manufacturing system in
the context of a closed-loop supply chain.
Kibouka G. Richard1
, Mandatsy Moungomo J. Brice2
, Ndong Mezui J.M
Lauhic3
1,2,3,
Department of Mechanical Engineering of ENSET, Systems Technology Research Laboratory
210 Avenue des Grandes Écoles, 3989 Libreville, Gabon
ABSTRACT : This article presents a problem of planning production activities, equipment maintenance,
supply and industrial logistics in a closed loop, for a production system evolving in a stochastic context. The
hybrid production/reuse system considered in this study consists of two machines. These machines, designated
M1 and M2 are assigned respectively to production (manufactured) and reconditioning (remanufactured). We
therefore consider a hybrid system based on the recovery of a single type of products where requests can be met
from an inventory of finished products. This inventory can be replenished by the production and repackaging of
end-of-life products (used and returned). Another inventory is available for the preservation of end-of-life
products returned prior to the repackaging process. Returned products may be repackaged, disposed of or kept in
stock for later repackaging. The objective of the study is to develop a dynamic stochastic model to optimize the
performance of a closed-loop logistics network. The reverse logistics network considered will have to establish a
relationship between the market that releases used products and the market for "new" products. The main
contribution of this study is to develop production policies in the different modes of the system. We have
developed new optimality conditions in terms of modified Hamilton-Jacobi-Bellman equations (HJB) and
recursive numerical methods applied to solve such equations. A numerical example and sensitivity analysis made
it possible to determine the structure of the optimal policies and to show the usefulness and robustness of the
results obtained.
KEYWORDS : Stochastic process, reverse logistics, numerical methods, quality, production/reuse.
I. INTRODUCTION
Manufacturing industries are faced with the challenges of optimizing their overall production supply chain.
Production planning problems become more complex when environmental constraints require the optimization of
manufacturing processes and the reuse, in manufacturing, of parts returned by consumers after use (reverse
logistics). Compared to a situation where customer demand is only satisfied by the parts in the direct line
(production from raw materials), the simultaneous control of production and reuse (remanufacturing) is very
complex (Kiesmüller, [1]). Reuse problems are found in the fields of mechanical parts manufacturing, aluminum
processing, vehicle and aircraft assembly lines, computers, photocopiers, etc. So, it's a question of how to plan
production in a way that meets demand and minimizes total cost. Reverse logistics has several synonyms,
although there are similarities between the terms, but they do not say the same thing (Lambert and Riopel, [2]).
Stock [3] uses terms like recycling, waste destruction, and hazardous materials management to define reverse
logistics.
Later, Fleischmann [4] proposed a new definition of reverse logistics as the process of planning, executing and
controlling the flow of collected products (returns) in a supply chain (production) with the aim of putting them
back on the market. Lambert and Riopel [2] mention that reverse logistics is the process of planning,
implementing, and controlling the efficiency, profitability of raw materials, work-in-progress, finished products,
and relevant information from the point of use to the point of origin in order to take back or generate value or to
dispose of it in the right way while ensuring an efficient and environmental use of the resources put in place.
work. While for Bennekrouf and al. [5], reverse logistics includes several profiles, namely: the return of
products (following the non-satisfaction of a criterion), the reuse of certain products (such as packaging and
containers), reprocessing (remanufacture) and cannibalization (dismantling of a product to reuse its parts). The
latter depends on the quality index of the recovered products. Carter and Ellram [6] present reverse distribution
as return, the counter-current movement of a product or material resulting from reuse, recycling or disposal.
3. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 33 |
According to Abdessalem and al. [7], reusing a product means that the product is used immediately in the same
or another context, following a minor additional operation such as cleaning, maintenance. It can also mean the
reuse of the parts that compose it as spare parts or raw materials. For Abdessalem and al. [7], recycling is
defined as the action of collecting and disassembling a product at the end of its life cycle for material recovery.
Remanufacturing as a process of disassembling used products, inspecting, repairing/replacing components and
using them to manufacture a new product The reverse supply chain is characterized by a series of activities,
according to (Bennekrouf and al. [5], the objective is the recovery of end-of-life products or components. With
regard to the diversity of parameters, the volume of data and the different levels of decision-making (Hajji, [8])
shows that there is no universal approach to modelling. But for Min and Zhou [9], there are four approaches to
Modeling namely; deterministic, stochastic, hybrid and models based on information technology. Hybrid
production/reuse systems have expanded over the last thirty years with respect for the environment and
environmental legislation (Mahadevan and al., [10]. Reverse logistics establishes the relationship between the
market for used and returned products and the market for new products. When the two markets coincide, we
speak of a closed-loop network, if not an open loop (Salema and al., [11]).
Several authors have worked in the context of hybrid production/reuse systems. Van der Laan and Salomon [12]).
(1997) studied the push-disposal strategy, which consists of destroying all collected products when the stock
level of finished products has reached the set threshold. They also studied the principle of pull-disposal which
consists of destroying all collected products when the stock of returns has already reached the set threshold. They
demonstrated that when the flow of returned products is less than the flow of demand, the total cost is less than
the total cost of a system that does not account for destroyed returns. In Kiesmüller's approach [1], the
remanufacturing process is only initiated when one wants to satisfy a demand (Pull Policy principle). Mahadevan
and al. [10]) have studied the principle of Push Policy according to which all returned items are directly
remanufactured; therefore, no stock of returns.For (Gershwin, [13]), the system consists of two production
centers subject to random phenomena: breakdowns, repairs, returns and variations in demand. According to (Van
der Laan and Salomon, [12]), customer demand is satisfied by the stock of finished products. The return of
products is composed of products at the end of their life cycle or already used. Returns that do not meet
remanufacturing standards are not stored; they are destroyed. As extensions, the work of Gharbi and al [14],
Berthaut and al. [15] and Pellerin and al. [16] have planned the production of a remanufacturing system that
incorporates the case of unavailability of replacement parts. They assumed that the production system meets
demand.
These authors framed their problem as a multi-level ordering policy based on critical stock thresholds. Their main
maintenance and remanufacturing planning objective were to keep the stock of finished products at the optimal
threshold. However, the authors only deal with remanufacturing without taking into account the direct line, i.e.,
manufacturing. The systematic analysis of the similarities and differences between the manufacturing,
remanufacturing and repair process was done by Tongzhu and al. [17]. Kenne and al. [18] addressed production
planning of a hybrid manufacturing/remanufacturing system in a closed-loop reverse logistics network. The
objective was to propose a manufacturing and remanufacturing policy that minimizes the costs of stocking
finished products and shortages. Following the analysis of the previous section, it emerges that the article by
Kenne et al. [18] represents the first attempt in the study of hybrid manufacturing/remanufacturing systems with
machines subject to random breakdowns and repairs.
The rest of the paper is organized as follows. In Section 2, we present the notations used and the assumptions of
the model. Section 3 presents a formulation of the problem and the technical background. Section 4 is devoted to
the development of the optimality conditions in the form of Hamilton-Jacobi-Bellman (HJB) equations and their
numerical solutions. An illustrative example and results analysis are presented in Section 5. A sensitivity analysis
and a comparative study are presented in Section 6 and 7, and the paper is concluded in Section 8.
II. NOTATION AND ASSUMPTION
This section presents the notations and assumptions used throughout this article. Table 1 highlights the notations
used in this article.
Table 1
Notation Désignation
u1 (.) Total production rate of Machine 1
4. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 34 |
u2 (.) Total production rate of Machine 2
U1max Max production rate on machine 1
U2max Max production rate on machine 2
d Request for finished products
(t) Stochastic process describing system dynamics
x1(t) Stock of finished products at time t
x2(t) Stock of products returned at time t
qij Transition rate from mode i to mode j
MTTF1 Average uptime of Machine 1
MTTR1 Machine 1 repair time
MTTF2 Average uptime of Machine 2
MTTR2 Machine 2 repair time
C1
+
Product inventory cost 1
C1
-
Shortage cost (Out of stock) of product 1
C2 Cost of inventorying returned products
Discount rate
The system studied incorporates the following aspects:
1. The breakdown and repair rates of M1 and M2 machines are assumed to be constant (Not dependent on
production).
2. Inventory costs corresponding to those produced for a positive inventory.
3. Shortage costs correspond to those associated with negative inventory.
4. The production rate of the production machine (M1) is higher than that of the reconditioning machine
(M2).
5. The reconditioning machine alone cannot meet the demand.
6. Demand can be met by new products (manufactured or remanufactured).
7. The production machine transforms the raw material into finished products (new products).
8. The reconditioning machine transforms the returned products into new products.
9. It is permissible to produce to make up for unmet demands.
10. Returned product stock cannot be subject to shortages.
III. PROBLEM FORMULATION
The manufacturing system considered represents a common problem in the mining industry. The system consists
of two machines designated M1 and M2 which are assigned respectively to production (manufactured) and
reconditioning (remanufactured). These machines are subject to breakdowns and random repair actions that can
generate stock-outs (Figure 1). The stochastic process resulting from this integration is then a rate-controlled
process (non-homogeneous Markovian process. The mode of the machine i M can be described by a stochastic
process ξ i (t), i =1,2 with value in B i = {1, 2}. Such a machine is available when it is operational (ξ i (t) = 1)
and unavailable when it is under repair (ξi (t) = 2).
Fig 1. Structure of the production system
5. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 35 |
The transition diagrams, which describes the dynamics of the considered manufacturing system, are presented in
Figure 2, 3 and 4. We then have ξ (t )∈B = {1, 2,3, 4} . With λαβ denoting a jump rate of the system from state α
to state β, we can describe ξ (t) statistically by the following state probabilities:
=
Machine 1
Fig 2. Machine State transition 1
Machine 2
Fig 3. Machine State transition 1
With: ; ; ;
By combining the two machines M1 and M2, we have a complete system:
Fig 4. State transition of the system
With:
q1
12 = 13 = 24 (failure rate of M1) ; q1
21 = 31 = 42 (corrective maintenance rate of M1)
q2
12 = 12 = 34 (failure rate of M2) ; q2
21 = 21 = 43 (corrective maintenance rate of M2 )
6. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 36 |
The operational mode of the manufacturing system can be described by the random vector ξ(t) = ξ1(t), ξ2(t)).
Given that the dynamics of each machine is described by a 2-state stochastic process, the set of possible values of
the process ξ(t) can be determined from the values of ξ1(t) and ξ2(t), as illustrated in Table 2, with :
Mode 1: M1 and M2 are operational
Mode 2: M1 is operational and M2 is under repair
Mode 3: M1 is under repair and M2 is operational
Mode 4: M1 and M2 are under repair.
The dynamics of the system is described by a discrete element, namely ξ(t) and a continuous element x(t). The
discrete element represents the status of the machines and the continuous one represents that of the stock level. It
can be positive for an inventory or negative for a backlog.
Table 2 Modes of a two-machine manufacturing system
A. ξ1(t) B. 1 C. 1 D. 2 E. 2 F. Machine 1 G. Stochastic process
H. ξ2(t) I. 1 J. 2 K. 1 L. 2 M. Machine 2 N. Stochastic process
O. ξ(t) P. 1 Q. 2 R. 3 S. 4 T. Manufacturing system U. Stochastic process
We assume that the failure rate of M1 depends on its productivity, and is defined by:
q1
12 =
Hence, ξ(t) is described by the following matrix :
Ԛ =
1=
2=
With: 1 = 13 = 24 ; 2 = 13 = 24
The continuous part of the system dynamics is described by the following differential equation:
Where and d are the given initial stock level and demand rate, respectively.
Where x20, r, et disp are respectively the initial s tock of returned products, the return rate, and the rejection rate
of returned products.
The set of the feasible control policies , including (⋅) and (⋅), is given by:
where (⋅) and (⋅) are known as control variables, and constitute the control policies of the problem under
study. The maximal productivities of the main machine and the second machine are denoted by , and
, respectively
Let g(.) be the cost rate defined as follows (equation 6) :
7. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 37 |
The production planning problem considered in this paper involves the determination of the
optimal control policies ( t) and t)) minimizing the expected discounted cost J (⋅) given by :
where
The properties of the value function and the manner in which the Hamilton-Jacobi-Bellman (HJB) equations are
obtained can be found in Kenné et al. [19], with a constant failure rate.
II. OPTIMALITY CONDITIONS
By successive developments, we arrive at the equation of HJB below like that presented in A. F. Koueudeu and
J.P. Kenne [20] :
In order to solve the problem numerically, the partial derived terms according to x 1 and x2 are approximated
according to Kushner's approach as follows:
Combining equations 10 and 11 in equation 9, we have the following equation 12:
The discretized equation (12) translates into four equations, (13, 14, 15 and 16) expressing the value functions of
the production system composed of two machines subject to two states ((t) = 1, 2, 3 et 4).
For the 4 modes, we will get:
Mode 1
8. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 38 |
Mode 2
Mode 3
Mode 4
9. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 39 |
For the parameters of the problem, we considered the following:
The estate
Recovery rate: 50% of demand
"Disposal" rate: 10% of recovered products
The feasibility of the problem is tested according to the equation defined above.
Table of numerical data used
Table 3 : Parameters of numerical example
C1+ C1- C2 hx1 hx2 U1max U2max d 1 1 2 2
10 50 2 0.5 0.5 1.3 1.15 1.25 1/60 1/15 1/60 1/15 0.1
Thus the production policies obtained indicate for the different configurations of stock states x 1 and
x 2 and system states (1, 2 , 3 and 4), the production rates to be applied to the production machines M 1 and
remanufacturing M2. These policies are of the hedging point type. Below are the details of each of these policies.
Mode 1
Fig 5. Production policy M1 mode 1
Fig 6. Production policy M2 mode 1
10. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 40 |
In mode 1, the 2 machines M 1 and M2 produce to meet demand. As illustrated above, the machine M 1 can
produce in 3 thresholds according to the state of the stock x 1 defined by its critical thresholds Z13 and Z12:
Whith
The M2 machine produces in addition to M1 at its maximum rate or zero rate according to the policy below:
With
The value function is of the form below. The study presented will make it possible to evaluate sensitivity to
variation of the main parameters.
Fig 7. Value function at mode 1
Mode 2
Fig 8. Production policy M1 mode 2
11. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 41 |
In mode 2, the M1 machine is the only one to serve the demand, the M2 machine being in default. Its optimal
policy is also critical threshold according to the diagram below:
With
Below is the look of the value function in Mode 2.
Fig 9. Value function at mode 2
Mode 3
Fig 10. Production policy M2 mode 3
In mode 1, the M2 machine is the only one to serve the demand, the M1 machine being in default. Its optimal
policy is also critical threshold according to the diagram below :
12. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 42 |
Fig 11. Value function at mode 3
With
The production rate of M2 being lower than that of M1, it is consistent that we case of defect of M1, we begin to
produce more (Z1 = 2.5 against Z2 = 0.5 in mode 2).
Below is the look of the value function in Mode 3.
Mode 4
In mode 4, the M1 and M2 machines are out of order, with zero production in their respective productions. The
value function looks below.
Fig 12. Value function at mode 4
Sensitivity analysis (inventory costs, shortage costs, etc.) As several parameters influence the model and
therefore the optimal policies, it seems important to see how the latter would react in the event of a significant
change in the latter. To do this, we performed a sensitivity analysis on the 3 parameters below. We recall that as it
is rigorous for a sensitivity analysis, we varied only one parameter at a time and observed the evolution of the
thresholds triggering the production stages, for each of the machines and each of the modes.
Cost of placing in inventory of the main stock
13. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 43 |
Fig 13. Sensibility C+
We notice that in general, the production tipping thresholds (corresponding to values of x1) all fall when we
review upwards the cost of inventorying the main stock x1, which is completely consistent because with the
increase in the cost of stocking, we want to limit as much as possible the quantities stored and therefore start
producing as soon as possible. We also note that for a cost put in inventory less than 5 the machine M2 remains
in permanent production whatever the values of stock x1 and x2. Only the Um1 production level of M1 is quite
difficult to cross because M2 already satisfies demand in the majority of cases.
Main stock shortage cost
Fig 14. Sensibility C-
We note here that, as a general rule, the tipping thresholds of the different levels increase when the cost of stock-
outs is increased, which is once again consistent because the model will want to limit as much as possible the
additional costs related to the shortage. This is even more noticeable in mode 1 where is Z1 is the threshold for
putting the M2 machine into production.
Recovery rate (Recycling)
14. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 44 |
Fig 15. Sensitivity on recycling rate
We observe here that the tipping thresholds of the different levels of machine production are completely
insensitive to variations in the recycling rate in this area.
III. DISCUSSION ON POSSIBLE EXTENSIONS
At the end of this study aimed at understanding the behavior of this system of 2 machines integrating reverse
logistics, it seems interesting to ask what other aspects could be addressed downstream of this work or be
integrated into it to make this model even more robust.Indeed, this study gives us very interesting results on the
optimal management approach of our system for an optimized satisfaction of demand and effective cost control.
The next step could be to move to the experimental approach, for simulation of optimal policies obtained from
the mathematical model and validation of effectiveness in the field. This step effectively makes it possible to
answer the "what if?" In other words, what response will we have from the system if the optimal policy suggested
by the mathematical model is applied. This would be a kind of validation step. It may be that at the end of this
experiment, some adjustments must be made to the mathematical solution before implementation in the field.
The other important aspect that we could have integrated is the consideration of the maintenance policy. Indeed,
our model assumes that the failure and repair rates of machines are constant, which is not always the case, they
can vary according to several criteria such as the rate of production, the age of the machine and many others.
Thus, a model incorporating these considerations, although more complex, would be even more representative of
the reality on the ground. Similarly, since our study was established over an infinite horizon, we could finally
have assumed a context where the maximum production rates of machines decreased increasing with the age of
machines (which is a reality in some industrial contexts), and thus created two additional states corresponding to
the replacement of machines when a maximum production rate falls below a certain threshold. This hypothesis
would therefore add 2 additional states to our model and would make it more appropriate to this type of context.
IV. CONCLUSION
Current models for optimizing manufacturing systems that integrate the return of used products into their
production system have some shortcomings. Regular use of production units at full capacity has an impact on the
availability and reliability of the manufacturing system. In order to remedy these shortcomings, this article aimed
to propose pragmatic models to solve the problems of optimization of production/reuse systems in a stochastic
dynamic context.In this paper, our work has made a significant scientific contribution by reformulating existing
mathematical models to incorporate the failure rate dependent on the production rate in the context of hybrid
production/reuse systems subject to random breakdowns and repairs. The results of our work have been
confirmed through studies by modeling, numerical resolution and sensitivity analysis on cases of flexible
manufacturing systems.
This work confirmed that by integrating degradation based on the number of failures or productivity into a
manufacturing system; and by controlling maintenance operations, in addition to the reliability of the system that
15. Planning of the production activities of a manufacturing system in…
| Volume 4 | Issue 6| www.ijmcer.com | 45 |
is assured, the system becomes less vulnerable to changes in the costs of shortages, inventory and maintenance by
continuously meeting demand. Our work was concluded by validating the policies proposed to manufacturers of
printer ink cartridges. These contributions provide a solid basis for future work.
.
REFERENCES
[1] Kiesmüller, G. P. 2003. « A new approach for controlling a hybrid stochastic
manufacturing/remanufacturing system with inventories and different leadtimes ». European Journal of
Operational Research, vol. 147, no 1, p. 62-71.
[2] Lambert and Riopel, D. 2003. «Logistique inverse: revue de littérature ». Coll. Les cahiers du GERAD,
G-2003-61. Montréal: École Polytechnique de Montréal, 45 p.
[3] Stock, J.R. 1992. «Reverse Logistics». Council of Logistics Management, Oak Brook, IL.
[4] Fleischmann. 2001. « The Impact of Product Recovery on Logistics Network Design ». Production and
Operations Management, vol. 10, no 2, p. 156-173.
[5] Bennekrouf, L. Benyoucef and Sari, Z. 2010. «Problèmes de conception et pilotage des chaînes
logistiques inverses et globales:Etat de l’art». In 8e Conférence Internationale de MOdélisation et
SIMulation - (MOSIM’10). (Hammamet - Tunisie, Mai. 10-12 2010) : Évaluation et optimisation des
systèmes innovants de production de biens et de services..
[6] Carter, C. and Ellram. 1998. « Reverse logistics: A review of literature and framework for future
investigation ». Journal of of Business Logisticsy, vol. 19, no 1, p. 85-102.
[7] Abdessalem, D. Riopel and Hadj-Alouane, A. 2007. «Conception pour la logistique inverse : proposition
d'un cahier des charges». (CIRRELT-2007-26). Montréal: Centre interuniversitaire de recherche sur les
réseaux d'entreprise, la logistique et le transport.
[8] Hajji, A. 2007. «Stratégies de production manufacturière dans un environnement de chaîne
d'approvisionnement: Approche dynamique stochastique». Thèse de Doctorat en génie de la production
automatisée, Montréal, École de Technologie Superieure, 234p.
[9] Min, H. and Gengui, Z. 2002. « Supply chain modeling: Past, present and future ». Computers and
Industrial Engineering, vol. 43, p. 231-249.
[10] Mahadevan, B., David, F. P. and Moritz, F. 2003. « Periodic review, push inventory policies for
remanufacturing ». European Journal of Operational Research, vol. 151, no 3, p. 536-551.
[11] Salema, M. I., Gomes, A. P. B-P. and Augusto, Q. N. 2007. « An optimization model for the design of a
capacitated multi-product reverse logistics network with uncertainty ». European Journal of Operational
Research, vol. 179, no 3, p. 1063-1077.
[12] Van der Laan, E. and Marc, S. 1997. « Production planning and inventory control with remanufacturing
and disposal ». European Journal of Operational Research, vol. 102, no 2, p. 264-278.
[13] Gershwin, S. B. 1994. «Manufacturing systems engineering». Englewood Cliffs, NJ: prentice-Hall, New
Jersey.
[14] Gharbi, A., R. Pellerin and Sadr, J. 2008. « Production rate control for stochastic remanufacturing
systems ». International Journal of Production Economics, vol. 112, no 1, p. 37-47.
[15] Berthaut, F., R. Pellerin and Gharbi, A. 2009. «Control of a repair and overhaul system with
probabilistic parts availability». Production Planning and Control, vol. 20, no 1, p. 57-67.
[16] Pellerin, R., Sadr, J., Gharbi, A. and Malhamé, R. 2009. « A production rate control policy for stochastic
repair and remanufacturing systems ». International Journal of Production Economics, vol. 121, no 1, p.
39-48.
[17] Tongzhu, Z., W. Xueping, C. Jiangwei and Pengfei, C. 2010. « Remanufacturing mode and its reliability
for the design of automotive products ». 5th International Conference on Responsive Manufacturing -
Green Manufacturing (ICRM 2010). (Piscataway, NJ, USA, Jan. 11-13 2010) : IET.
[18] Kenné, J.-P., Dejax, P. and Gharbi, A. 2012. « Production planning of a hybrid manufacturing-
remanufacturing system under uncertainty within a closed-loop supply chain ». International Journal of
Production Economics, vol. 135, no 1, p. 81-93.
[19] Kenne, J. P., Boukas, E. K. and Gharbi, A. 2003. « Control of production and corrective maintenance
rates in a multiple-machine, multiple-product manufacturing system ». Mathematical and Computer
Modelling, vol. 38, p. 351-365.
[20] Kouedeu, A. F., Kenne, J-P., Dejax, P., Songmene, V. and Polotski, V. 2013a. « Analytical modelling
and control of a hybrid manufacturing/remanufacturing system ». 7th IFAC Conference on
Manufacturing Modelling, Management, and Control, (MIM 2013 - Proceedings), p.401-406.