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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Vydané v:2014 IEEE Congress on Evolutionary Computation (CEC) s. 58 - 65
Hlavní autori: Jingjing Ma, Yu Lei, Zhao Wang, Licheng Jiao, Ruochen Liu
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2014
Predmet:
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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Abstract 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.
AbstractList 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.
Author Licheng Jiao
Yu Lei
Ruochen Liu
Zhao Wang
Jingjing Ma
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  organization: Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ., Xidian Univ., Xi'an, China
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Snippet 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...
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StartPage 58
SubjectTerms Algorithm design and analysis
Flexible job-shop scheduling
immune algorithm
memetic algorithm
Memetics
multi-objective optimization
Scheduling
Simulated annealing
Sociology
Statistics
Vectors
Title A memetic algorithm based on Immune multi-objective optimization for flexible job-shop scheduling problems
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