Yailen Martinez*, Tony Wauters, Elsy Kaddoum
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1 DICOMAS Meeting 05/07/2010 Yailen Martinez*, Tony Wauters, Elsy Kaddoum * Computational Modeling Lab, Vrije Universiteit Brussel Brussel - Belgium ymartine@vub.ac.be Vakgroep Informatietechnologie, Departement Industrieel Ingenieur, KaHo Sint-Lieven Gent - Belgium tony.wauters@kahosl.be Systèmes Multi-Agents Coopératifs (SMAC) IRIT ( Université Paul Sabatier Toulouse France elsy.kaddoum@irit.fr
2 Outline Context & Challenge Dynamic Flexible Job Shop (FJS) Scheduling Definition & Entities Entities Characteristics Offline Approach Rolling Time Approach Self-Adaptive Flexible Scheduling (SAFlex) Conclusion & Perspectives 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting
3 Context & Challenge Growing complexity Dependencies between parameters Dynamic environment Constraints modification New entities entering the system Etc. Multi-disciplinary, multi-objective and multi-scale dimensions Increasing size of information Requirements Satisfaction of constraints and delays Minimizing the costs Minimizing the execution time Run-time adaptation, Robustness, Reactivity 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 3/16
4 Flexible Job Shop Scheduling Definition Principles and techniques used to plan in the short term, control and evaluate the production activities of the manufacturing organization [Duggan, 91] Entities Machines / Stations Jobs / Containers Operations /Qualifications 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 4/16
5 Flexible Job Shop Scheduling Entities Characteristics Machines/Stations Set of Operations/Qualifications Specific Processing Time per Operation/Qualification Perturbations Preemption Constraints Jobs/Containers Goal Release & Due Dates Constraints Ordered set of Operations/Qualifications Precedence Constraints Schedule the operations on the machines Verify constraints Minimize Different Objectives 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 5/16
6 Offline Approach Two learning phases 1. Operations learn which is the most suitable machine Agent per operation Actions: which machine to choose from the set 2. Solve the resulting classical JSSP Agent per resource Actions: which job to process next out of a set of currently waiting jobs at the corresponding resource. Mode Optimization Procedure (Forward-Backward Alg.) 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 6/16
7 Offline Approach M J 1 2 operations O 1, 10 1 M 2, 15 O 2 M J 2 3 operations O 1, 20 1 O 2 M 3, 25 M 2, 12 M 3, 18 M 1, 25 M 2, 18 O 3 M 2, 15 M 3, 25 M 1 M 2 O 11, 10 O 21, 20 O 12, 12 O 22, 18 O 23, 15 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 7/16
8 Rolling Time Approach Works with partial schedules Reschedules on every new event New job arrival Perturbation Uses Serial Schedule Generation method for rescheduling 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 8/16
9 SAFlex (Self-Adaptive Flexible Job Shop) Distributed & Decentralized Multi-Agent System AMAS Theory (Adaptive Multi-Agent System) Cooperation = engine for self-adaptation Two Cooperative Agents Containers Stations 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 9/16
10 SAFlexS: Scenario P=10 Ask for PT Ask Ask for for delay S1 S1 (P=8) Accept Reject Reject (cause: breakdown) Send relevant neighbourhood (@ P=6 Ask for S1 delay S1 (P=6) Ask for PT Ask for S2 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 10/16
11 Experiments 2 Categories No Perturbation Perturbations using Poisson distributions Mean Inter-arrival Time Mean Perturbation Duration 10 Classical Flexible Job Shop Problems [Brandimarte, Annals of Operations Research 1993 ] 5 dynamic instances per classical problem Different due dates & release dates 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 11/16
12 Results : No Perturbation Mk07: 20 Jobs 5 Machines Mk10: 20 Jobs 15 Machines Tmean: mean Tardiness Tn: number of tardy jobs Tmax: maximum Tardiness Cmax: makespan 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 12/16
13 Results : Poisson Mk01: 10 Jobs 6 Machines Mk10: 20 Jobs 15 Machines Tmean: mean Tardiness Tn: number of tardy jobs Tmax: maximum Tardiness Cmax: makespan 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 13/16
14 Conclusions Comparison of three approaches Objective functions Global Measure for Offline & Rolling Time approaches Better in some dimension (Makespan) Local Measure for SAFlex Less Tardiness Solving process Offline mode : Learning/Optimizing the complete schedule Online mode Rolling Time: complete future re-scheduling SAFlex: reactive adaptation (SAFlex) 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 14/16
15 Perspectives Analyze the results differences Test the scalability of each approach Study the behavior transition Vary problem characteristics (Perturbations) 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 15/16
16 Thank you (André Machonin) & Schedule Interface (Tony Wauters) 22/04/2011 Flexible Job Shop Scheduling DICOMAS Meeting 16/16
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