This document summarizes an article that proposes using a heuristic procedure to optimize scheduling constraints in a manufacturing system with multiple products. The heuristic procedure arranges machines in an optimal sequence to minimize manufacturing lead time and maximize profit.
The document first discusses traditional optimization approaches using economic order quantities and Lagrange multipliers that are difficult to implement in practice. It then presents a simple heuristic rule for staggering product replenishments under an equal order interval method.
Finally, the document provides an example application of the heuristic procedure to determine optimal job sequences, machine sequences, waiting times, and layout costs for different manufacturing layouts like linear, loop and ladder layouts. Computational results demonstrate that the heuristic approach performs better than traditional optimization methods
A Comparative Reliability Analysis of Bulldozers arriving at workshop in East...IOSR Journals
Study of reliabilities of machinery used in any kind of production is of utmost necessity for optimum use of man power and resources to make the process cost effective and with minimum downtime. This is applicable for all large and small industries alike. But in small industries data is not accurately stored and it becomes difficult to estimate product reliabilities. This paper focuses on a case study to estimate the reliabilities of two competing machines, when the only available data is Time To Failure. The Weibull Parameters are calculated using Microsoft Excel 2010. The results show that after knowing the reliabilities of both the Bulldozers at different lengths of time, we can ascertain which of them is preferable to use at which time period.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
"Optimisation of closed loop supply chain decisions using integrated game theoretic particle swarm algorithm"
Kalpit Patne, Visiting Fellow, SMART Infrastructure Facility presented a summary of his research as part of the SMART Seminar Series on 8 July 2016.
For more information, visit the event page at: http://smart.uow.edu.au/events/UOW217694.
A Comparative Reliability Analysis of Bulldozers arriving at workshop in East...IOSR Journals
Study of reliabilities of machinery used in any kind of production is of utmost necessity for optimum use of man power and resources to make the process cost effective and with minimum downtime. This is applicable for all large and small industries alike. But in small industries data is not accurately stored and it becomes difficult to estimate product reliabilities. This paper focuses on a case study to estimate the reliabilities of two competing machines, when the only available data is Time To Failure. The Weibull Parameters are calculated using Microsoft Excel 2010. The results show that after knowing the reliabilities of both the Bulldozers at different lengths of time, we can ascertain which of them is preferable to use at which time period.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
"Optimisation of closed loop supply chain decisions using integrated game theoretic particle swarm algorithm"
Kalpit Patne, Visiting Fellow, SMART Infrastructure Facility presented a summary of his research as part of the SMART Seminar Series on 8 July 2016.
For more information, visit the event page at: http://smart.uow.edu.au/events/UOW217694.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
Economic load dispatch (ELD) and Unit Commitment (UC) are significant research applications in power systems that optimize the total production cost of the predicted load demand. The UC problem determines a turn-on and turn-off schedule for a given combination of generating units, thus satisfying a set of dynamic operational constraints. ELD optimizes the operation cost for all scheduled generating units with respect to the load demands of customers. The first phase in this project is to economically schedule the distribution of generating units using Gauss seidal and the second phase is to determine optimal load distribution for the scheduled units using dynamic programming method is applied to select and choose the combination of generating units that commit and de-commit during each hour. These precommitted schedules are optimized by dynamic programming method thus producing a global optimum solution with feasible and effective solution quality, minimal cost and time and higher precision. The effectiveness of the proposed techniques is investigated on two test systems consisting of five generating units and the experiments are carried out using MATLAB R2010b software. Experimental results prove that the proposed method is capable of yielding higher quality solution including mathematical simplicity, fast convergence, diversity maintenance, robustness and scalability for the complex ELD-UC problem.
Modified sub-gradient based combined objective technique and evolutionary pro...IJECEIAES
A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with 휆 as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Procurement Strategy for Manufacturing and JITIJERD Editor
Procurement involves the purchase of goods and services from the suppliers for the manufacturing units. Procuring the items in the most optimized way is very much desired to implement JIT effectively. Manufacturer-supplier relationship is very crucial for effective procurement policy. The present paper deals with various procurement strategies of manufacturing that include the optimization of manufacturing quantity along with the procurement of multiple input items. The procurement cycles for input items are considered in small lot sizes, which are essential for the effective implementation of JIT. The method of integer programming has been used to find the optimum integer values of number of orders for procuring the input items, which is usually found to be in fraction for a real manufacturing environment.
Balancing the line by using heuristic method based on cpm in salbp –a case studyeSAT Journals
Abstract
In mass production systems, line balancing plays a great role, but this is not easy even if it is a simple straight line. So, in order to
solve these problems Heuristic methods are very much desirable. It is also found that Heuristic methods play a great role in the
formation of metaheuristic methods.Therefore it is very much important to use more efficient heuristic methods. In this research
paper we presents a heuristic method that is based on critical path method for simple assembly line balancing. This research is
mainly concerned with objectives of minimizing the number of workstations, improvement of smoothness index, mean absolute
deviation (MAD) and increasing line efficiency.
Keywords-Heuristic methods,Assembly line balancing problem, Critical path method, Simple assembly line balancing.
Optimization of Economic Load Dispatch with Unit Commitment on Multi MachineIJAPEJOURNAL
Economic load dispatch (ELD) and Unit Commitment (UC) are significant research applications in power systems that optimize the total production cost of the predicted load demand. The UC problem determines a turn-on and turn-off schedule for a given combination of generating units, thus satisfying a set of dynamic operational constraints. ELD optimizes the operation cost for all scheduled generating units with respect to the load demands of customers. The first phase in this project is to economically schedule the distribution of generating units using Gauss seidal and the second phase is to determine optimal load distribution for the scheduled units using dynamic programming method is applied to select and choose the combination of generating units that commit and de-commit during each hour. These precommitted schedules are optimized by dynamic programming method thus producing a global optimum solution with feasible and effective solution quality, minimal cost and time and higher precision. The effectiveness of the proposed techniques is investigated on two test systems consisting of five generating units and the experiments are carried out using MATLAB R2010b software. Experimental results prove that the proposed method is capable of yielding higher quality solution including mathematical simplicity, fast convergence, diversity maintenance, robustness and scalability for the complex ELD-UC problem.
Modified sub-gradient based combined objective technique and evolutionary pro...IJECEIAES
A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with 휆 as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Procurement Strategy for Manufacturing and JITIJERD Editor
Procurement involves the purchase of goods and services from the suppliers for the manufacturing units. Procuring the items in the most optimized way is very much desired to implement JIT effectively. Manufacturer-supplier relationship is very crucial for effective procurement policy. The present paper deals with various procurement strategies of manufacturing that include the optimization of manufacturing quantity along with the procurement of multiple input items. The procurement cycles for input items are considered in small lot sizes, which are essential for the effective implementation of JIT. The method of integer programming has been used to find the optimum integer values of number of orders for procuring the input items, which is usually found to be in fraction for a real manufacturing environment.
Balancing the line by using heuristic method based on cpm in salbp –a case studyeSAT Journals
Abstract
In mass production systems, line balancing plays a great role, but this is not easy even if it is a simple straight line. So, in order to
solve these problems Heuristic methods are very much desirable. It is also found that Heuristic methods play a great role in the
formation of metaheuristic methods.Therefore it is very much important to use more efficient heuristic methods. In this research
paper we presents a heuristic method that is based on critical path method for simple assembly line balancing. This research is
mainly concerned with objectives of minimizing the number of workstations, improvement of smoothness index, mean absolute
deviation (MAD) and increasing line efficiency.
Keywords-Heuristic methods,Assembly line balancing problem, Critical path method, Simple assembly line balancing.
Planning of Supply when lead time uncertain: trade-off between holding cost a...ijsrd.com
A supply planning for two-level assembly systems under lead-time uncertainties is considered. It is supposed that the demand for the finished product and its due date are known. It is assumed also that the component lead-time at each level is a random discrete variable. The objective is to find the release dates for the components at level two in order to minimize the expected component holding costs and to maximize the customer service level for the finished pr
Optimization of Time Restriction in Construction Project Management Using Lin...IJERA Editor
This study is an attempt to identify the minimum time of a construction project using the critical path method
and linear programming model. A systematic analysis is attempted by developing a work breakdown structure
for entire project to establish work elements for quantifying various resources against time and cost. A network
is established taking into consideration all the predecessor and successor activities. The network is then
optimized through crashing of activities so as to obtain optimal solution and serves as a base for optimizing total
project cost. Finally, linear programming model is used to formulate the system of crashing network for
minimum time by LINGO model and Microsoft Excel. These models consider many considerations of project
thus reducing the duration of project. Ultimately, comparison of both the software outputs and the manual
calculations is done and the best verifier is determined.
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Artificial Neural Networks can achieve high degree of computation rates by
employing a massive number of simple processing elements with a high degree of
connectivity between elements. In this paper an attempt is made to present a Constraint
Satisfaction Adaptive Neural Network (CSANN) to solve the generalized job-shop
scheduling problem and it shows how to map a difficult constraint satisfaction job-shop
scheduling problem onto a simple neural net, where the number of neural processors equals
the number of operations, and the number of interconnections grows linearly with the total
number of operations. The proposed neural network can be easily constructed and can adjust
its weights of connections based on the sequence and resource constraints of the job-shop
scheduling problem during its processing. Simulation studies have shown that the proposed
neural network produces better solutions to job-shop scheduling problem.
The economic order quantity (EOQ) is a company's optimal order quantity that meets demand while minimizing its total costs related to ordering, receiving, and holding inventory. The EOQ formula is best applied in situations where demand, ordering, and holding costs remain constant over time.
Design of PID Controller with Inadequate Determination Dependent on Different...ijtsrd
To decide the ideal or close to ideal boundaries of PID regulator with fragmented determination, an original plan strategy dependent on differential advancement calculation is introduced. The regulator is called DE PID controller. To beat the impediments of the necessary exhibition rules in the recurrence space like IAE, ISE, and ITSE, another presentation measure in the time area is proposed. The streamlining methods utilizing the DE calculation to look through the ideal or close to ideal PID controller boundaries of a control framework are shown exhaustively. Three common control frameworks are picked to test and assess the variation and vigor of the proposed DE PID controller. The reproduction results show that the proposed approach has unrivalled provisions of simple execution, stable combination trademark, and great computational efficiency. Contrasted and the ZN, GA, and ASA, the proposed plan technique is without a doubt more efficient and strong in further developing the progression reaction of a control framework. N. Abinaya | V. Gomathi | V. Sudha "Design of PID Controller with Inadequate Determination Dependent on Differential Development Calculation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49270.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/49270/design-of-pid-controller-with-inadequate-determination-dependent-on-differential-development-calculation/n-abinaya
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
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
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
11.optimization by heuristic procedure of scheduling constraints in manufacturing system
1. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011
Optimization by Heuristic procedure of Scheduling
Constraints in Manufacturing System
Bharat Chede1*, Dr C.K.Jain2, Dr S.K.Jain3, Aparna Chede4
1. Reader, Department of Mechanical Engineering, Mahakal Institute of Technology and Management,
Ujjain
2. Ex Principal, Ujjain Engineering College, Ujjain
3. Principal, Ujjain Engineering College, Ujjain
4. Lecturer, Department of Mechanical Engineering, Mahakal Institute of Technology, Ujjain
* Email of the corresponding author : bharat_chede@rediffmail.com Phone 91-9827587234
Abstract
This article provides an insight for optimization the output considering combinations of scheduling
constraints by using simple heuristic for multi-product inventory system. Method so proposed is easy to
implement in real manufacturing situation by using heuristic procedure machines are arranged in optimal
sequence for multiple product to manufacture. This reduces manufacturing lead-time thus enhance profit.
Keywords: Optimization, Scheduling, Heuristic, Inventory, Layouts
1. Introduction
Constraints in industry lead to reduction of profit to industry especially some constraints like budgetary
and space when imposed on system. These constraints has impact on cost incurred in that product basically
considering the scheduling as constraints specially in case when we have to adopt in change in product mix,
demand and design.
2. Traditional approach
There are many relations for EOQ
Q = √ 2 Ci .Yi i = 1,2,3 _ _ _n ----------------- (i)
Bi
For ith product Ci = order cost per unit
Yi = demand per unit
11
2. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011
Bi = the stock holding cost per unit per unit time
There are chances when all n products have to be replenished at same time. If restriction of maximum
capital investment (W) at any time in inventory is active, then (A) is valid if Q (i= 1.2. - - - n) satisfy the
constraints
n
∑ Vi. Qi ≤ W ------------------------------ (ii)
i =1
Where V is the value of unit of product i otherwise Q ( i = 1,2, ---- n)
To satisfy (ii) Lagrange multiplier technique is used to obtain modified Q
Q = √ 2 Ci .Yi i = 1,2,3 _ _ _n ----------------- (iii)
Bi + 2µVi
µ - Lagrange multiplier associated with capital investment constraints
But by these also we may not necessarily get optimality.
The proposed simple heuristic rule for staggering of the replenishments of product under equal order
interval method and provide a simple formula for obtaining the upper limit of maximum investment in
inventory we assume that each product is replenished at equal time interval during common order interval T,
i.e. for n product we order at a time interval of T/n and upper limit of maximum investment in inventory
can be obtained by making following arrangement.
Arranging the item in descending order of demand of each item and its unit value (i.e. max [Vi,Yi] is
for i=I) If INV denote the capital investment required at time (j - 1) T/n j = 1,2, --- n then INVj can be
expressed as
n
INVj = T/n ∑ rjk. V k . Y k j = 1,2----n
k=1
rjk = n+k-j if k ≤ j
= k-j if k ≥ j
MLUL = max (INVj) gives upper limit of maximum investment in cycle duration j.
To have exact utilization of invested budget the estimated value of T should be obtained from W = MLUL
Comparison of optimal result by both methods. It is clear that heuristic rule provide better cost performance
12
3. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011
than Lagrange multiplier method.(Table1)
3. Numerical example
Considered the company nearby Indore (India) (Table2) for the Demand rate, order cost, stock holding
cost, Value and EOQ.
Maximum allowable investment W = Rs 15000/- If EOQ used ignoring budgetary constraint, the maximum
investment in inventory would be 17,120/- which is greater than W. To find optimal solution by Lagrange
multiplier technique which when applied gives(Table 3)
Q L1 = 88, Q L2 = 139, Q L3 = 98 Total cost = Rs 3541 when µ = 0.03
From the result it ensures that heuristic rule performs well. Also the proposed rule in easy to implement in
practice.
3.1 In case of Scheduling as constraints we can classify the Layout as
• Liner (single and double row machine layout) (Figure 1)
• Loop Layout (Figure 2)
• Ladder layout (reduces travel distance)(Figure 3)
• The Carousel Layout (Part flow in one direction around loop The Load and unload stations are
placed at one end of loop)(Figure 4)
• The Open Field Layout (Consist of Ladder and Loop)(Figure 5)
Scheduling is considered as Static Scheduling where a fixed set of orders are to be scheduled either using
optimization or priority heuristics also as dynamic scheduling problem where orders arrives periodically.
Process of Solution for problem
Pi be a partial schedule containing i schedule operations.
Si The set of Schedulable operation at stage i corresponding to given Pi
Pj The earliest time at which operation j and s could be started.
Q Earliest time at which operation J, sj could be completed.
4. Priority Rule Used
• SPT (Select operation with minimum processing time)
• MWKR (Most work remaining)
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4. Industrial Engineering Letters www.iiste.org
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Vol 1, No.4, 2011
• Random (Select operation at random)
4.1 Algorithm
Step 1 : Let t=0 assume Pt = {Ǿ}
Step 2 : Determine q* = min {q}and corresponding machine m* on which q* could be realized.
Step 3 : For operation which belongs to S that requires machine m* and satisfy the condition p < q*
identify an operation according to specified priority add this operation to pi for next stage.
Step 4 : For each new partial schedule p t+1 created in step 3 update the data set as follows
i) Remove operation j from Si.
ii) From St+1 by adding the direct successor of operation j from si.
iii) Increment by 1
Step 5 : Repeat step 2 to step 4 for each p t+1 created in steps and continue in this manner until all
active schedules are generated.
Step 6 : From To chart is developed from routing data the chart indicated number of parts moves between
the machines.
Step 7 : Adjust flow matrix is calculated from frequency matrix, distance matrix and cost matrix.
Step 8 : From to Sums are determined from the adjusted flow matrix.
Step 9 : Assign the a machine to it on minimum from sums. The machine having the smallest sum is
selected. If the minimum value is to sum, then the machine placed at the beginning of sequence.
If the minimum value is from sum, then machine placed at end of sequence.
5. Example
Arrangements of machine are considered for Liner layout, loop layout and ladder layout. The input
data required in inter slot distance load unload distances and unit transportation cost, processing times for
different jobs, processing sequence of jobs on different machines.(Table 4)(Table 5)
• Inters lot distance and load, unload matrices for Liner layout (Table 6,7)
• Inter slot distance and load, unload matrix for Loop Layout(Table 8,9)
• Inter slot Distance and Load, Unload Matrices for Ladder Layout (Table 10,11)
6. Results
• Waiting time of machine (Table 12)
• Waiting time for Job (Table 13) Production time for a batch of component = 2600 hrs
• Sequence of job manufactured on different machine for minimum production time.(table 14)
• Machine order and total cost for different types of layouts (Table 15)
7. Conclusion
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5. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011
The traditional method for determining optimal order quantities and the optimal reorder points in
multi-product inventory system with constraints are not easy to implement in reality but heuristic rule is not
only easy to implement but also give better result than traditional method. By using heuristic procedure
with scheduling as constraint layout is optimized. The other parameters such as flow time, job sequence to
manufactured, Machine sequence, total transportation cost, Machine and job waiting time are determined.
References
Brown G.G, Dell R.F, Davis R.L and Dutt R.H (2002) “Optimizing plant line schedules and an
application at Hidden valley Manufacturing Company”, INTERFACES INFORMS Volume 32
No.-3, pp 1-14.
Oke S.A.,Charles E. and Owaba O.E.(2007) “A fuzzy linguistic approach of preventive
maintenance scheduling cost optimization”, Kathmandu University Journal of science and
Engineering and Technology Volume 1 No.3 January pp 1-13
Osman K, Nagi R. Christopher R.M. (2001 “New Lot sizing formulation for less nervous
production schedules”, Computers and Operation Research 27, pp 1325-1345.
Pegels C.C and Watrous C. (2005) “Application of theory of constraints to bottleneck operation in
manufacturing plant”, Journal of Manufacturing Technology Management. Vol 16 No-03, pp
302-311.
Solimanpar.M,Vrat .P and Shankar R,(2004) “ A heuristic to minimize make span of cell
scheduling problem”, International journal of Production Economics 88,pp 231-241.
First Author. Bharat Chede has received Bachelor’s degree from Amravati University, India and Masters
Degree in Mechanical Engineering (Production Engineering) from Shivaji University, India, He is currently
pursuing PhD from Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India. He is working as Head of
Department (Mechanical Engineering) at Mahakal Institute of Technology and Management Ujjain India.
His Current area of research is Optimization in manufacturing techniques using fuzzy logics.
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6. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011
Second Author. Dr C.K.Jain Phd in Production Engineering from IIT Rourkee. A renowned
academician, was responsible for making trendsetting transformations during his last stint as Principal,
UEC. Having received a Gold Medal in M.Tech and an award winning research scholar during his PhD. His
Current area of research is Casting methods optimization.
Third Author. Dr S.K.Jain. Phd from APS university Rewa India. He is principal Ujjain Engineering
College Ujjain, India . His current areas of research are Fuzzy logic Technique in Engineering
Fourth Author. Aparna Chede has received Bachelors and Masters Degree in Mechanical Engineering
from Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India. She is currently working as Lecturer in
Mechanical Engineering at Mahakal Institute of Technology and Science Ujjain India. Her current areas
of research are Industrial Engineering techniques.
Method of T Q1 Q2 Q3 Total Cost
solution
Langrange --- 145 44 114 Rs 4063
Multiplier
Heuristic 0.108 216 54 108 Rs 3919
Table No 1: (Comparison of optimal result by both methods)
Product(i) 1 2 3
Demand rate (units per year) Y 2000 1000 1000
Order cost C (Rs per unit per year) 50 50 50
Stock holding cost B (Rs Per unit per 16 10 4
year)
Value V (Rs per unit) 80 150 20
EOQ 112 100 158
Table 2: (Demand rate, order cost, stock holding cost, value and EOQ.)
Method of solution T Q1 Q2 Q3 Total Cost
Langrange Multiplier --- 98 88 139 Rs 3541
Heuristic 0.079 158 79 79 Rs 3716
16
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