Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem

The multi-objective flexible job shop scheduling problem is solved using a novel path-relinking algorithm based on the state-of-the-art Tabu search algorithm with back-jump tracking. A routing solution is identified by problem-specific neighborhood search, and is then further refined by the Tabu sea...

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Vydáno v:Computers & operations research Ročník 47; s. 11 - 26
Hlavní autoři: Jia, Shuai, Hu, Zhi-Hua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.07.2014
Elsevier
Pergamon Press Inc
Témata:
Algorithms
Applied sciences
Benchmarking
Computer memory
Decision theory. Utility theory
Exact sciences and technology
Flexible job shop scheduling
Heuristic
Job shop scheduling
Job shops
Mathematical models
Multi-objective optimization
Operational research and scientific management
Operational research. Management science
Operations research
Path-relinking
Production scheduling
Scheduling algorithms
Scheduling, sequencing
Searching
Studies
Tabu search
Tracking
Multi-objective optimization
Tabu search
Path-relinking
Flexible job shop scheduling
Search problem
Tracking
Job shop
Multiobjective programming
Routing
Scheduling
Competitive algorithms
Optimization
Search algorithm
Neighborhood search
Flexible manufacturing system
Heuristic method
Tracking(movable target)
Medium term
Sequencing
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:The multi-objective flexible job shop scheduling problem is solved using a novel path-relinking algorithm based on the state-of-the-art Tabu search algorithm with back-jump tracking. A routing solution is identified by problem-specific neighborhood search, and is then further refined by the Tabu search algorithm with back-jump tracking for a sequencing decision. The resultant solution is used to maintain the medium-term memory where the best solutions are stored. A path-relinking heuristics is designed to generate diverse solutions in the most promising areas. An improved version of the algorithm is then developed by incorporating an effective dimension-oriented intensification search to find solutions that are located near extreme solutions. The proposed algorithms are tested on benchmark instances and its experimental performance is compared with that of algorithms in the literature. Comparison results show that the proposed algorithms are competitive in terms of its computation performance and solution quality.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2014.01.010