Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times

The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper p...

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Bibliographic Details
Published in:IEEE transactions on engineering management 2017-05, Vol.64 (2), p.149-165
Main Authors: Li, Xiangtao, Ma, Shijing
Format: Article
Language:English
Subjects:
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Algorithms
Decomposition
Evolutionary computation
flow shop
Job shop scheduling
Job shops
local search
multiobjective optimization
Multiple objective analysis
Optimization
Optimization algorithms
Permutations
Processor scheduling
Production scheduling
Scheduling
Search algorithms
Sequential scheduling
Setup times
Sociology
Statistics
Swarm intelligence
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Summary:The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D, to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2016.2645790