This document discusses optimization solutions for planning and scheduling problems using CPLEX. It begins with an introduction to DecisionBrain and examples of applications in manufacturing, supply chain, and maintenance scheduling. Case studies are presented on production planning in electronics manufacturing, container terminal optimization, and field service scheduling. Best practices are discussed around choosing the right optimization technology, emphasizing decision support over pure optimization, understanding business goals, and integrating process improvements with advanced decision support. Project risks around not achieving benefits, performance issues, and user acceptance are also addressed.
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a. Observe the critical path diagram. Why are there two arrows pointing to task F? b. Why is the critical path shown as A-B-E-G-I? How is the critical path defined? c. What would happen if activity F was revised to take 4 days instead of 2days?
Winter Simulation Conference 2021 - Process Wind Tunnel TalkSudhendu Rai
The talk associated with this presentation can be accessed at:
https://youtu.be/VXEVuXW9knU
Abstract
In this talk, we will introduce a simulation-based process improvement framework and methodology called the Process Wind Tunnel. We will describe this framework and introduce the underlying technologies namely process mapping and data collection, data wrangling, exploratory data analysis and visualization, process mining, discrete-event simulation optimization and solution implementation. We will discuss how Process Wind Tunnel framework was utilized to improve a critical business process namely, the post-execution trade settlement process. The work builds upon and generalizes the Lean Document Production solution (2008 Edelman finalist) for optimizing printshops to more general and complex business processes found within the insurance and financial services industry.
Today's fast paced product market has shorter lifecycles and tighter budgetary concerns. Tolerance analysis software provides an ideal solution to reduce the number of crucial steps needed to optimize a product at the design step itself. 3DCS Variation Analyst is the world's most used tolerance analysis software that is fully integrated into NX/ CATIA V5/ Creo and CAD Neutral Multi-CAD. 3DCS Variation Analyst is designed to use a consistent format and set of mathematical formulae that create reliable results, enabling engineers to gain a complete insight into their design. The software empowers design engineers to control variation and optimize their designs to account for inherent process and part variation, which in turn reduces non-conformance, scrap, rework and other associated costs.
3DCS Variation Analyst
Used by the world’s leading manufacturing OEM’s to reduce the cost of quality, 3DCS Variation Analyst comes in two flavours:
1) 3DCS Variation Analyst (NX / CAA V5 or Creo Based) is an integrated solution for NX / CATIA V5 or Creo. Since it is an integrated solution, users can not only activate 3DCS workbenches from within the modelling solution, they can use many of its inbuilt functionality to support their modelling.
3DCS Variation Analyst provides three analysis methods:
Monte Carlo Analysis
High-Low-Mean (Sensitivity Analysis) and
Geofactor Analysis (Relationship)
OPS 571 HELP Redefined Education--ops571help.comclaric212
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a. Observe the critical path diagram. Why are there two arrows pointing to task F? b. Why is the critical path shown as A-B-E-G-I? How is the critical path defined? c. What would happen if activity F was revised to take 4 days instead of 2days?
Winter Simulation Conference 2021 - Process Wind Tunnel TalkSudhendu Rai
The talk associated with this presentation can be accessed at:
https://youtu.be/VXEVuXW9knU
Abstract
In this talk, we will introduce a simulation-based process improvement framework and methodology called the Process Wind Tunnel. We will describe this framework and introduce the underlying technologies namely process mapping and data collection, data wrangling, exploratory data analysis and visualization, process mining, discrete-event simulation optimization and solution implementation. We will discuss how Process Wind Tunnel framework was utilized to improve a critical business process namely, the post-execution trade settlement process. The work builds upon and generalizes the Lean Document Production solution (2008 Edelman finalist) for optimizing printshops to more general and complex business processes found within the insurance and financial services industry.
Today's fast paced product market has shorter lifecycles and tighter budgetary concerns. Tolerance analysis software provides an ideal solution to reduce the number of crucial steps needed to optimize a product at the design step itself. 3DCS Variation Analyst is the world's most used tolerance analysis software that is fully integrated into NX/ CATIA V5/ Creo and CAD Neutral Multi-CAD. 3DCS Variation Analyst is designed to use a consistent format and set of mathematical formulae that create reliable results, enabling engineers to gain a complete insight into their design. The software empowers design engineers to control variation and optimize their designs to account for inherent process and part variation, which in turn reduces non-conformance, scrap, rework and other associated costs.
3DCS Variation Analyst
Used by the world’s leading manufacturing OEM’s to reduce the cost of quality, 3DCS Variation Analyst comes in two flavours:
1) 3DCS Variation Analyst (NX / CAA V5 or Creo Based) is an integrated solution for NX / CATIA V5 or Creo. Since it is an integrated solution, users can not only activate 3DCS workbenches from within the modelling solution, they can use many of its inbuilt functionality to support their modelling.
3DCS Variation Analyst provides three analysis methods:
Monte Carlo Analysis
High-Low-Mean (Sensitivity Analysis) and
Geofactor Analysis (Relationship)
Approach to improve effectiveness of Enterprise ITEvgeny Nedelko
(translated to English) Excerpts from the customer proposal, which describes the experience of implementing Lean IT principles in several projects in Russia
OPS 571 Effective Communication - snaptutorial.comdonaldzs45
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Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Current ratio
Receivable turnover
Conditional interval variables: A powerful concept for modeling and solving c...Philippe Laborie
Scheduling is not only about deciding when to schedule a predefined set of activities. Most of real-world scheduling problems also involve selecting a subset of activities (oversubscribed problems) and a particular way to execute them (resource or mode allocation, alternative recipes, preemptive activity splitting, etc.). We present the notion of conditional interval variable in the context of Constraint Programming and show how this concept can be leveraged to model and solve complex scheduling problems involving both temporal and non-temporal decisions.
This slide deck was presented at the 21st International Symposium on Mathematical Programming (ISMP 2012).
Philippe Laborie
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
Recent advancements in Linear and Mixed Programing give us the capability to solve larger Optimization Problems. In this talk using CPLEX Optimization Studio we will discuss modeling practices, case studies and demonstrate good practices for solving Hard Optimization Problems. We will also discuss recent CPLEX performance improvements and recently added features.
Approach to improve effectiveness of Enterprise ITEvgeny Nedelko
(translated to English) Excerpts from the customer proposal, which describes the experience of implementing Lean IT principles in several projects in Russia
OPS 571 Effective Communication - snaptutorial.comdonaldzs45
For more classes visit
www.snaptutorial.com
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Current ratio
Receivable turnover
Conditional interval variables: A powerful concept for modeling and solving c...Philippe Laborie
Scheduling is not only about deciding when to schedule a predefined set of activities. Most of real-world scheduling problems also involve selecting a subset of activities (oversubscribed problems) and a particular way to execute them (resource or mode allocation, alternative recipes, preemptive activity splitting, etc.). We present the notion of conditional interval variable in the context of Constraint Programming and show how this concept can be leveraged to model and solve complex scheduling problems involving both temporal and non-temporal decisions.
This slide deck was presented at the 21st International Symposium on Mathematical Programming (ISMP 2012).
Philippe Laborie
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
Recent advancements in Linear and Mixed Programing give us the capability to solve larger Optimization Problems. In this talk using CPLEX Optimization Studio we will discuss modeling practices, case studies and demonstrate good practices for solving Hard Optimization Problems. We will also discuss recent CPLEX performance improvements and recently added features.
All About Business Analyst Becoming a successful BAZaranTech LLC
We are the leaders in providing Role-Specific training and e-learning solutions for individuals and corporations. Our curriculums are based on real-time job functions as opposed to being product-based.specialized and innovative global expert IT Training and Consulting services
This presentation is intended to give the reader a brief of Lean Six Sigma. It is tried to impart the knowledge based on personal learnings and literature available over the internet related to Lean Six Sigma Yellow and Green Belt.
Business analysis activities for an
analyst. This PPT will help to understand how to analyze a business requirement preparation like business case, BRD and SRS.
Key Business Processes And Activities For Excellence PowerPoint Presentation ...SlideTeam
It covers all the important concepts and has relevant templates which cater to your business needs. This complete deck has PPT slides on Key Business Processes And Activities For Excellence PowerPoint Presentation Slides with well suited graphics and subject driven content. This deck consists of total of thirty six slides. All templates are completely editable for your convenience. You can change the colour, text and font size of these slides. You can add or delete the content as per your requirement. Get access to this professionally designed complete deck presentation by clicking the download button below. https://bit.ly/315nefa
Enterprise resource planning (ERP) is an enterprise-wide information system designed to coordinate all the resources, information, and activities needed to complete business processes such as order fulfillment or billing. ... Ideally, the data for the various business functions are integrated.
The Certified Business Analysis Professional (CBAP) designation is a professional certification and registered trademark from International Institute of Business Analysis (IIBA) granted to individuals with extensive business analysis experience.
How Digitising Vocational Training Allowed Audi Australia To Strengthen Its B...Acquia
Presented at Acquia Engage APAC by Graham O'Connell, Lead Developer, GMWEB & Justin Barrie, Principal, Design Managers Australia.
The vocational training market is increasingly offered COTS options for managing student progress. This case study shows how Audi Australia chose to go another way – using Drupal and Acquia to tailor and enhance their training outcomes and outputs.
Audi Australia now has a world-leading, customised student management system that goes above and beyond government requirements while supporting the entire network of dealers and service centres in Australia.
Graham and Justin will share the processes used and challenges overcome while moving to an in-house digital environment for apprentice management, including the effective use of service design to develop clear customer experience journeys that lead to the delivery of innovative technology solutions.
Identification of all areas contributing to problems and determining scope of projects are challenges for many organizations. A method to improve the outcomes can help reduce risk - find out how!
Is JDA a critical application for your business? Are you planning or completing a JDA upgrade? Have you experienced issues that are difficult to track, locate or root cause? If you answered YES to any of these questions, then this webinar is tailor-made for you.
Industry experts from Spinnaker and Germain Software discuss best practices in managing a JDA environment. They share war stories to highlight why you are having issues, how you can locate and root cause them, and proactively safe guard your environment from the issues in the future!
What Is Your PLM Challenge - Manage configurable products and maintain produc...Dawn Collins
DES CASE Corporation shows how to efficiently manage a growing number of configurable products and maintain product integrity with SIemens Teamcenter. Mercury PLM Services walks through the successful implementation. Geometric Solutions Is your one stop shop for PLM solutions, training and support. Rave Computer is the company for your customized hardware and server needs.
To achieve operational excellence, a business needs strong collaboration and streamline procedures to follow. It also needs proper management of finances and supply chain altogether. And, the key to achieve this is, BatchMaster manufacturing integrated with Microsoft Dynamics GP. Want to know how? Check out the presentation below
Sabrion has a highly qualified team of retail/manufacturing process experts and IT consultants, supporting both short and long-term needs. Our FastForward implementation methodology to support PLM and Merchandise planning.
Project Management
PMI – Project Management Institute
PMBOK – Project Management Body of Knowledge
Agile – We utilize Agile, Scrum, and Extreme methodologies when appropriate
We are flexible to embrace the methodologies used by our customers an business partners
Retail/Manufacturing Business Process Re – Engineering
As-Is and To-Be Modeling, SIPOC, RACI, Impact Analysis, Standard Operating Procedures
Application Design, Development and Integration
UML – Unified Modeling Language
Open Internet and Standards, HTML5, CSS3, JQuery, Javascript, Web Frameworks
Application Architecture
Application Infrastructure Design – Virtualization, Cloud, Application Servers, Storage, Web DMZ
Global Network Design – LAN, WAN, MPLS, Reverse Proxy, CDN
Deployment Architecture – Dev, QA, Staging, Production
Similar to Informs 2016 Solving Planning and Scheduling Problems with CPLEX (20)
We tested ODH|CPLEX 4.24 on Miplib Open-v7 Models, a public collection of 286 models to which and optimal solution has not been proven. 257 of these are known to have a feasible solution.
ODH|CPLEX proved optimality on 6 models and found better solutions in 2 hours, to 40% of the models with 12 threads and 35% with 8 threads. ODH|CPLEX matched on 21% of the models.
EX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment. CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to: Optimize business decisions with high-performance optimization engines. Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios. Create real-world applications that can significantly improve business outcomes. Optimization Direct has partnered with and entered into a technology licensing and distribution agreement with IBM. By combining the founders' industry and software experience and IBM’s CPLEX Optimization Studio product with the arsenal of Optimization modeling and solving tools from IBM provides customers the most powerful capabilities in the industry.
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate deterministic and stochastic non-parametric finite impulse response (FIR) models in IMPL applied to industrial gas furnace data identical to that found in TSE-GFD-IMF using parametric transfer-functions. The methodology of time-series analysis or system identification involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment/adjustment, auto-, cross- and partial-correlation, etc. to establish the parametric transfer function polynomial degrees especially when we are using non-parametric FIR estimation. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s Sequential Equality-Constrained QP Engine (SECQPE) and Supplemental Observability, Redundancy and Variability Estimator (SORVE). Other types of non-parametric identification known as Subspace Identification (Qin, 2006) and can used to estimate state-space models.
Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.
This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992).
The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.
Presented in this short document is a description of how to model and solve multi-utility scheduling optimization (MUSO) problems in IMPL. Multi-utility systems (co/tri-generation) are typically found in petroleum refineries and petrochemical plants (multi-commodity systems) especially when fuel-gas (i.e., off-gases of methane and ethane) is a co- or by-product of the production from which multi-pressure heating-, motive- and process-steam are generated on-site. Other utilities include hydrogen, electricity, water, cooling media, air, nitrogen, chemicals, etc. where a multi-utility system is shown in Figure 1 with an intermediate or integrated utility (both produced and consumed) such as fuel-gas, steam or electricity. Itemized benefit areas just for better management of an integrated steam network can be found in Pelham (2013) where his sample multi-pressure steam utility flowsheet is found in Figure 2.
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...Alkis Vazacopoulos
Presented in this short document is a description of how to model and solve advanced parameter estimation (APE) problems in IMPL. APE is the term given to the application of estimating, fitting or calibrating parameters in models involving a network, topology, superstructure or flowsheet. When estimating parameters with multiple linear regression (MLR), ordinary least squares (OLS), ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) there is no explicit model but simply an X-block and Y-block of data. Hence, these methods are referred to as “non-parametric” or “data-based” methods as opposed to the “parametric” or “model-based” method used here. To solve these types of problems we use what is commonly referred to as “error-in-variables” (EIV) regression which is conveniently implemented as nonlinear data reconciliation and regression (NDRR) using the technology found in Kelly (1998a; 1998b; 1999) and Kelly and Zyngier (2008a). The primary benefit of using EIV (NDRR) over the other regression methods is that we can easily handle the inclusion of conservation laws and constitutive relations, explicitly, a must for any industrial estimation problem (IEP).
Presented in this short document is a description of modeling and solving partial differential equations (PDE’s) in both the temporal and spatial dimensions using IMPL. The sample PDE problem is taken from Cutlip and Shacham (1999 and 2014) and models the process of unsteady-state heat transfer or conduction in a one dimensional (1D) slab with one face insulated and constant thermal conductivity as discussed by Geankoplis (1993).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
3. DECISIONBRAIN
• Global Presence
• France, Hong Kong, Singapore
• IBM Partnership
• Founded in 2013 by former ILOG and IBM employees
• IBM Business Partner
• Expertise & Thought Leadership:
• Planning and Scheduling in Manufacturing, Supply Chain, and Logistics
• Workforce Optimization, Price Optimization and Maintenance Optimization
• Development of Innovative Solutions and Advanced Analytics
• 40+ Scientific Publications in Optimization and Supply Chain, Patents, …
We implement optimizationsolutions to help companies
improve their business operations
3
7. DECISION PROBLEMS
7
Decisions Benefits
Cutting
• Combination of panels from different work orders • Minimize laminates waste
Press batching / 2D Packing
• Combination of panels from different work orders
• Tradeoff press throughput vs due dates
• Improve press throughput
• Minimize cupper waste
Production Planning
• Assignment of work orders to processes / machines
and daily buckets over planning horizon
• Provides a global view of the manufacturing
process
• Minimize setup times
• Minimize / Control WIP
• Maximize on-time delivery
• Tradeoff between due dates and outsourcing
3-day Scheduling
• Sequence work orders in machines for each process • Minimize setup times
• Minimize / Control WIP
• Maximize on-time delivery
• Reduced planning time
9. CONTAINER TERMINAL: HONG KONG (HIT) AND SHENZHEN (YICT)
Multi-Vessel Optimization:
• Improve the coordinationbetween the Quay side and the Yard side by
holisticallyoptimizingthe load / discharge operations of all vessels.
• Minimize Yard Clash and Traffic Jam while respectingETD constraints and
limitingReshuffling.
10. LOAD / DISCHARGE GANTT VIEW
Bridge
or
Engine
Room
One
color
per
quay
crane
ETB
marker
ETD
marker
Current
TIme
Frozen
Horizon
in
grey
Bay
of
the
Vessel
12. INTEGRAL’S FIELD SERVICE SCHEDULING
12
• A decision support system to build and maintain a daily
plan of the field engineers
– For Planned Preventive Maintenance (PPM) and Reactive
Maintenance (RM):
– Daily scheduling of jobs to engineers
– Manual schedulingand dynamic rescheduling of jobs that
arrives during the day
• Objective
– Improve SLAs
– Improve technicianproductivity(mintravel time and idle time)
– Minimize overtime
– Maximize skill adequacy
15. IBM DECISION OPTIMIZATION AND CPLEX ARE THE RIGHT TOOLS
15
• 30% custom developmentsspecificto
your business.
• 30% standard software components
that include
– Data validation
– Data cleaning
– Advanced visualization
– Industry specific mathematical models
• 40% a generic platform for Decision
Support
– IBM Decision Optimization center
– Technical capabilities needed in every
decision support system
Our solutions are composed of three layers
16. WAS
WAS
DOC Clients
Or
Web
Clients
DOC
Enterprise
Optimization
Server
Production
Environment
DOC
Enterprise
Data
Server
Database
Execution
Systems
Excel
Spreadsheet/
csv Files
Other
Database
Legacy
System
IBM DECISION OPTIMIZATION CENTER
17. • Mathematical Optimization
– Modeling all constraints lead to very high complexity
– A straightforward MIP model is not reasonable…
• Constraint Programming
– Constraints can be modeled (although some are quite complex)
– Objective functions are challenging (smooth resource utilization on the
Yard)
• Effective approach: MIP/CP-based Column Generation
• Key takeaway…
– Optimization Technology as a toolkit.
– Conceptually explore or prototype alternatives
– The most effective technique may require more than one technology
è Unique value of IBM CPLEX Optimization Studio
WHICH OPTIMIZATION TECHNOLOGY?
Example from Container Terminal Optimization
18. • Effective UI and ApplicationLogic is as important as Optimization
– Users do not understand optimization
– Good visualizationand automation can alsoprovidevalueto the planners
– Good visualizationand automation increase solution acceptance
• Data Validationand Solution ValidationComponents
– Identify issues and provide clear explanationto the planners
• SolutionAnalysis Components
– The quality of the solution is not judged by the value of the objective function
• Workflow Components
– The planner is not an analyst. If several tasks needs to be accomplished, you need to
guide him/her throughthesetasks
DECISION SUPPORT ≠ OPTIMIZATION MODEL
19. UNDERSTAND THE BUSINESS GOALS IS CRITICAL
19
• What is the right scope of the solution
• How does the solution fit within the customer’s business model
• Bottlenecks and how to achieve efficiency gains
• Understand where the complexity is and how to manage it
• Understand the KPIs
• Understand the success factors
• Define the planning process and process constraints
20. PROCESS IMPROVEMENTS AND ADVANCED DECISION SUPPORT MUST BE PART
OF THE SAME PROJECT
• Complexity reduction and Complexity modeling
• Alignment of the planning logic with the business strategy
• Alignment of incentives with planning KPIs
Analysis,
Requirements &
Solution Design
Data-driven
Quick Wins
GUI & Limited
Scope
Optimization
Full System Deployment
Data
Infrastructure &
KPIs
Go-Live Support and
Benefits analysis
Change
Management
Process
improvements
20
21. TYPICAL PROJECT RISKS AND MITIGATION
21
Risk Mitigation
The decision support system does not generate
the expected business benefits
Process Improvements and Decision Support
are analyzed holistically and maintained
aligned throughout the project
Low performance of the Optimization Engine
due to problem size and complexity
Datasets will be made available during the
Start Up phase to correctly design the
optimization engines.
Planners do not accept the solutions (e.g. do
not trust the results, find it difficult to use)
Iterative approach with high involvement of
the planners and continuous validation