Informs 2016 Solving Planning and Scheduling Problems with CPLEX

1. Solving Planning and Scheduling Problems with
CPLEX
ffocacci@decisionbrain.com
Filippo Focacci

2. AGENDA
1. Introduction
2. Case Studies
3. Best Practices

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
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4. CUSTOMER EXAMPLES
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Workforce /
Maintenance
Manufacting /
Supply Chain
Support
to
R&D

5. AGENDA
1. Introduction
2. Case Studies
3. Best Practices

6. PRODUCTION PLANNING AND SCHEDULING IN ELECTRONIC
MANUFACTURING
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7. 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

8. OPTIMIZED PLANNING AND SCHEDULING

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

11. YARD VIEW
Yard
Block
Container
Moves
Cumulative
Moves

12. 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 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

13. DOC WEB INTERFACE
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14. AGENDA
1. Introduction
2. Case Studies
3. Best Practices

15. IBM DECISION OPTIMIZATION AND CPLEX ARE THE RIGHT TOOLS
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• 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
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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

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
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21. TYPICAL PROJECT RISKS AND MITIGATION
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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

22. Discussion
ffocacci@decisionbrain.com
Filippo Focacci