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**Lecture 6: Job Shop Scheduling Introduction**

© J. Christopher Beck 2005

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**Outline Job Shop Scheduling Introduction to Solution Techniques LEKIN**

Basic Definition Introduction to Solution Techniques © J. Christopher Beck 2005

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**Job Shop Scheduling Operation/Task/ Activity Job Precedence Constraint**

© J. Christopher Beck 2005

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**Job Shop Scheduling JSP is Hard RCPSP is a generalization of JSP**

makespan © J. Christopher Beck 2005

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**Your Very Own JSP Can you find a schedule with a makespan of 31?**

Don’t forget about the precedence constraints on the activities in each job Activities Jobs 1 2 3 4 M1, 9 M2, 8 M3, 4 M4, 4 M1, 5 M2, 6 M4, 3 M3, 6 M3, 10 M1, 8 M2, 9 M4, 2 © J. Christopher Beck 2005

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**Solving the JSP Many, many approaches**

20,300 hits on Google Scholar Also used to solve other scheduling problems and other optimization problems We are going to spend the next 6 lectures talking about them © J. Christopher Beck 2005

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Dispatch Rules Whenever a machine is free, look at all operations that can be scheduled and pick on with a simple rule: SPT: shortest processing time LPT: longest processing time EDD: earliest due date Try out SPT Activities Jobs 1 2 3 4 M1, 9 M2, 8 M3, 4 M4, 4 M1, 5 M2, 6 M4, 3 M3, 6 M3, 10 M1, 8 M2, 9 M4, 2 © J. Christopher Beck 2005

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**Shifting Bottleneck Pick most loaded resource**

Find optimal one-machine schedule Pick next most loaded resource Find optimal one-machine schedule consistent with previous one-machine schedules (This is a bit simplified) © J. Christopher Beck 2005

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**Tabu Search Start with a random schedule**

Make a “move” (e.g., swap two operations) Remember you last few moves and don’t undo them Keep going until you get bored © J. Christopher Beck 2005

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**Integer Programming and Branch-&-Bound**

Represent problem as an IP Sequence of every pair of operations is a 0-1 variable Use Branch-&-Bound (B&B) to find solution Will find optimal solution (if given enough time) © J. Christopher Beck 2005

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**Constraint Programming**

B&B (but not IP) plus inference Every time you branch, use specialized algorithms to find other decisions that must be true May also use sophisticated branching heuristics Also will find optimal given time © J. Christopher Beck 2005

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**Summary Dispatch Rules Shifting Bottleneck Tabu Search**

Heuristic & incomplete: No guarantees Dispatch Rules Shifting Bottleneck Tabu Search Integer Programming Constraint Programming But: work well for large problems Will find optimal (if given enough time) © J. Christopher Beck 2005

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LEKIN Demo © J. Christopher Beck 2005

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© J. Christopher Beck 20081 Lecture 15: CP Search.

© J. Christopher Beck 20081 Lecture 15: CP Search.

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