informs 2016 solving planning and scheduling problems with cplex

Solving Planning and Scheduling Problems with CPLEX

ffocacci@decisionbrain.comFilippo Focacci

AGENDA

1. Introduction2. Case Studies3. Best Practices

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 optimization solutions to help companies improve their business operations

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CUSTOMER EXAMPLES

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Workforce /  Maintenance

Manufacting /  Supply Chain

Support  to  R&D

AGENDA

1. Introduction2. Case Studies3. Best Practices

PRODUCTION PLANNING AND SCHEDULING IN ELECTRONIC MANUFACTURING

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DECISION PROBLEMS

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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

OPTIMIZED PLANNING AND SCHEDULING

CONTAINER TERMINAL: HONG KONG (HIT) AND SHENZHEN (YICT)

Multi-Vessel Optimization: • Improve the coordination between the Quay side and the Yard side by holistically optimizing the load / discharge operations of all vessels. •Minimize Yard Clash and Traffic Jam while respecting ETD constraints and limiting Reshuffling.

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

YARD VIEW

Yard  Block

Container  Moves

Cumulative  Moves

INTEGRAL’S FIELD SERVICE SCHEDULING

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• 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 scheduling and dynamic rescheduling of jobs that

arrives during the day

• Objective– Improve SLAs – Improve technician productivity (min travel time and idle time)– Minimize overtime – Maximize skill adequacy

DOC WEB INTERFACE

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AGENDA

1. Introduction2. Case Studies3. Best Practices

IBM DECISION OPTIMIZATION AND CPLEX ARE THE RIGHT TOOLS

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• 30% custom developments specific to 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

WAS

WAS

DOC Clients  Or  

Web  Clients

DOCEnterprise

Optimization  Server

Production   Environment

DOC  Enterprise

Data  Server

Database

Execution  Systems

Excel  Spreadsheet/  csv Files

Other  Database

Legacy  System

IBM DECISION OPTIMIZATION CENTER

• 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

• Effective UI and Application Logic is as important as Optimization– Users do not understand optimization– Good visualization and automation can also provide value to the planners – Good visualization and automation increase solution acceptance

• Data Validation and Solution Validation Components– Identify issues and provide clear explanation to the planners

• Solution Analysis 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 through these tasks

DECISION SUPPORT ≠ OPTIMIZATION MODEL

UNDERSTAND THE BUSINESS GOALS IS CRITICAL

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• 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

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-drivenQuick Wins

GUI & Limited Scope

OptimizationFull System Deployment

DataInfrastructure &

KPIs

Go-Live Support and Benefits analysis

Change Management

Processimprovements

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TYPICAL PROJECT RISKS AND MITIGATION

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Risk MitigationThe decision support system does not generate the expected business benefits

Process Improvements and Decision Supportare 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

Discussion

ffocacci@decisionbrain.comFilippo Focacci