A memetic algorithm based on Immune multi-objective optimization for flexible job-shop scheduling problems

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job scheduling which is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying parallel goals. This paper addresses the FJSP with two objectives: Minimiz...

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Veröffentlicht in:2014 IEEE Congress on Evolutionary Computation (CEC) S. 58 - 65
Hauptverfasser: Jingjing Ma, Yu Lei, Zhao Wang, Licheng Jiao, Ruochen Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2014
Schlagworte:
Algorithm design and analysis
Flexible job-shop scheduling
immune algorithm
memetic algorithm
Memetics
multi-objective optimization
Scheduling
Simulated annealing
Sociology
Statistics
Vectors
ISSN:1089-778X
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Zusammenfassung:The flexible job-shop scheduling problem (FJSP) is an extension of the classical job scheduling which is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying parallel goals. This paper addresses the FJSP with two objectives: Minimize makespan, Minimize total operation cost. We introduce a memetic algorithm based on the Nondominated Neighbor Immune Algorithm (NNIA), to tackle this problem. The proposed algorithm adds, to NNIA, local search procedures including a rational combination of undirected simulated annealing (UDSA) operator, directed cost simulated annealing (DCSA) operator and directed makespan simulated annealing (DMSA) operator. We have validated its efficiency by evaluating the algorithm on multiple instances of the FJSPs. Experimental results show that the proposed algorithm is an efficient and effective algorithm for the FJSPs, and the combination of UDSA operator, DCSA operator and DMSA operator with NNIA is rational.
ISSN:1089-778X
DOI:10.1109/CEC.2014.6900331