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Lecture 6: Job Shop Scheduling Introduction© J. Christopher Beck 2005
Outline Job Shop Scheduling Introduction to Solution Techniques LEKINBasic Definition Introduction to Solution Techniques © J. Christopher Beck 2005
Job Shop Scheduling Operation/Task/ Activity Job Precedence Constraint© J. Christopher Beck 2005
Job Shop Scheduling JSP is Hard RCPSP is a generalization of JSPmakespan © J. Christopher Beck 2005
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
Solving the JSP Many, many approaches20,300 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
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
Shifting Bottleneck Pick most loaded resourceFind 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
Tabu Search Start with a random scheduleMake 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
Integer Programming and Branch-&-BoundRepresent 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
Constraint ProgrammingB&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
Summary Dispatch Rules Shifting Bottleneck Tabu SearchHeuristic & 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
LEKIN Demo © J. Christopher Beck 2005
Lecture 8: Dispatch Rules
© J. Christopher Beck Lecture 17: Tabu Search.
© J. Christopher Beck Lecture 7: Shifting Bottleneck.
ISE480 Sequencing and Scheduling Izmir University of Economics ISE Fall Semestre.
1 Short Term Scheduling. 2 Planning horizon is short Multiple unique jobs (tasks) with varying processing times and due dates Multiple unique jobs.
© J. Christopher Beck Lecture 10: (Full) Shifting Bottleneck.
Lecture 9: Simplified Shifting Bottleneck
Introduction to LEKIN Gareth Beddoe
Lecture 10: Integer Programming & Branch-and-Bound
© J. Christopher Beck Lecture 16: Local Search.
Job-shop Scheduling n jobs m machines No recirculation – Jobs do not revisit the same machine (i, j) is referred to as an operation in which job j is processed.
Algorithm Design Methods (I) Fall 2003 CSE, POSTECH.
© J. Christopher Beck Lecture 11: Constraint Programming 1.
Operational Research & ManagementOperations Scheduling Introduction Operations Scheduling 1.Setting up the Scheduling Problem 2.Single Machine Problems.
IE 3265 – POM R. Lindeke Spring 2005
© J. Christopher Beck Lecture 5: Project Planning 2.
© J. Christopher Beck Lecture 14: Assembly Line Scheduling 2.
Introduction to Job Shop Scheduling Problem Qianjun Xu Oct. 30, 2001.
1 IOE/MFG 543 Chapter 7: Job shops Sections 7.1 and 7.2 (skip section 7.3)
Algorithm Design Methods Spring 2007 CSE, POSTECH.
© J. Christopher Beck Lecture 13: Modeling in Constraint Programming.
Outline Introduction Minimizing the makespan Minimizing total flowtime
© J. Christopher Beck Lecture 15: CP Search.
© J. Christopher Beck Lecture 25: Workforce Scheduling 3.
© J. Christopher Beck Lecture 19: Timetabling with Operator Constraints.
21st European Conference on Operational Research Algorithms for flexible flow shop problems with unrelated parallel machines, setup times and dual criteria.
Metaheuristics The idea: search the solution space directly. No math models, only a set of algorithmic steps, iterative method. Find a feasible solution.
Scheduling. Characteristics of a “job” Constituent operations Constituent operations Due date Due date Time of arrival in shop Time of arrival in shop.
© J. Christopher Beck Lecture 24: Workforce Scheduling.
1 Single Machine Deterministic Models Jobs: J 1, J 2,..., J n Assumptions: The machine is always available throughout the scheduling period. The machine.
Job Shop Reformulation of Vehicle Routing Evgeny Selensky University of Glasgow
Vehicle Routing & Job Shop Scheduling: Whats the Difference? ICAPS03, June 13, 2003 J. Christopher Beck, Patrick Prosser, & Evgeny Selensky Dept. of Computing.
Scheduling – Day 2. Production Planning Process Process Planning Strategic Capacity Planning Aggregate Planning Master Production Scheduling Material.
Solving IPs – Implicit Enumeration Similar to Binary IP Branch and Bound General Idea: Fixed variables – those for which a value has been fixed. Free Variable.
SOFTWARE / HARDWARE PARTITIONING TECHNIQUES SHaPES: A New Approach.
© J. Christopher Beck Lecture 12: Constraint Programming 2.
FLOW SHOPS: F2||Cmax. FLOW SHOPS: JOHNSON'S RULE2 FLOW SHOP SCHEDULING (n JOBS, m MACHINES) n JOBS BANK OF m MACHINES (SERIES) n M1 M2Mm.
Spring, Scheduling Operations. Spring, Scheduling Problems in Operations Job Shop Scheduling. Personnel Scheduling Facilities Scheduling.
5. Operations Scheduling
Solutions for Scheduling Assays. Why do we use laboratory automation? Improve quality control (QC) Free resources Reduce sa fety risks Automatic data.
Lateness Models Contents
Solving a job-shop scheduling problem by an adaptive algorithm based on learning Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute.
1 IOE/MFG 543 Chapter 5: Parallel machine models (Sections )
Scheduling. Production Planning Process Process Planning Strategic Capacity Planning Aggregate Planning Master Production Scheduling Material Requirements.
© J. Christopher Beck Lecture 24: Workforce Scheduling 2.
1 Contents college 3 en 4 Book: Appendix A.1, A.3, A.4, §3.4, §3.5, §4.1, §4.2, §4.4, §4.6 (not: §3.6 - §3.8, §4.2 - §4.3) Extra literature on resource.
Parallel Machine Scheduling
Algorithm Design Methods 황승원 Fall 2011 CSE, POSTECH.
Iterative Flattening in Cumulative Scheduling. Cumulative Scheduling Problem Set of Jobs Each job consists of a sequence of activities Each activity has.
Heuristic Methods for the Single- Machine Problem Chapter 4 Elements of Sequencing and Scheduling by Kenneth R. Baker Byung-Hyun Ha R2.
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