1. Planning and Scheduling in Manufacturing and Services

2. What is Scheduling About?
Applied operations research
Models
Algorithms
Solution using computers
Implement algorithms
Draw on common databases
Integration with other systems
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3. Application Areas Procurement and production
Transportation and distribution
Information processing and communications
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4. Manufacturing Scheduling
Short product life-cycles
Quick-response manufacturing
Manufacture-to-order
More complex operations must be scheduled in shorter amount of time with less room for errors!
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5. Scope of Course Levels of planning and scheduling
Long-range planning (several years),
middle-range planning (1-2 years),
short-range planning (few months),
scheduling (few weeks), and
reactive scheduling (now)
These functions are now often integrated
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6. Scheduling Systems Enterprise Resource Planning (ERP)
Common for larger businesses
Materials Requirement Planning (MRP)
Very common for manufacturing companies
Advanced Planning and Scheduling (APS)
Most recent trend
Considered advanced feature of ERP
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7. Scheduling Problem Allocate scarce resources to tasks
Combinatorial optimization problem
Maximize profit
Subject to constraints
Mathematical techniques and heuristics
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8. Our Approach Scheduling Problem Model Conclusions Problem Formulation
Solve with Computer Algorithms
Conclusions
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9. Scheduling Models Project scheduling Job shop scheduling
Flexible assembly systems
Lot sizing and scheduling
Interval scheduling, reservation, timetabling
Workforce scheduling
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10. General Solution Techniques
Mathematical programming
Linear, non-linear, and integer programming
Enumerative methods
Branch-and-bound
Beam search
Local search
Simulated annealing/genetic algorithms/tabu search/neural networks.
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11. Scheduling System Design
Order
master file
Shop floor
data collection
Databases
Schedule generation
User interfaces
Database Management
Automatic Schedule Generator
Performance
Evaluation
Schedule Editor
Graphical Interface
User
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12. LEKIN Generic job shop scheduling system
User friendly windows environment
C++ object oriented design
Can add own routines
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13. Advanced Topics Uncertainty, robustness, and reactive scheduling
Multiple objectives
Internet scheduling
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14. Setting up the Scheduling Problem
Topic 1
Setting up the Scheduling Problem

15. Modeling Three components to any model: Decision variables
This is what we can change to affect the system, that is, the variables we can decide upon
Objective function
E.g, cost to be minimized, quality measure to be maximized
Constraints
Which values the decision variables can be set to
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16. Decision “Variables” Three basic types of solutions:
A sequence: a permutation of the jobs
A schedule: allocation of the jobs in a more complicated setting of the environment
A scheduling policy: determines the next job given the current state of the system
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17. Model Characteristics
Multiple factors:
Number of machine and resources,
configuration and layout,
level of automation, etc.
Our terminology:
Resource = machine (m)
Entity requiring the resource = job (n)
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18. Notation Static data: Dynamic data: Processing time (pij)
Release date (rj)
Due date (dj)
Weight (wj)
Dynamic data:
Completion time (Cij)
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19. Machine Configuration
Standard machine configurations:
Single machine models
Parallel machine models
Flow shop models
Job shop models
Real world always more complicated.
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20. Constraints Precedence constraints Routing constraints
Material-handling constraints
Storage/waiting constraints
Machine eligibility
Tooling/resource constraints
Personnel scheduling constraints
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21. Other Characteristics
Sequence dependent setup
Preemptions
preemptive resume
preemptive repeat
Make-to-stock versus make-to-order
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22. Objectives and Performance Measures
Throughput (TP) and makespan (Cmax)
Due date related objectives
Work-in-process (WIP), lead time (response time), finished inventory
Others
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23. Throughput and Makespan
Defined by bottleneck machines
Makespan
Minimizing makespan tends to maximize throughput and balance load
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24. Due Date Related Objectives
Lateness
Minimize maximum lateness (Lmax)
Tardiness
Minimize the weighted tardiness
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25. Due Date Penalties Lateness Tardiness Late or Not In practice
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26. WIP and Lead Time Work-in-Process (WIP) inventory cost
Minimizing WIP also minimizes average lead time (throughput time)
Minimizing lead time tends to minimize the average number of jobs in system
Equivalently, we can minimize sum of the completion times:
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27. Other Costs Setup cost Personnel cost Robustness
Finished goods inventory cost
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