TY - JOUR
T1 - Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities
AU - Jia, Zhaohong
AU - Yan, Jianhai
AU - Leung, Joseph Y.T.
AU - Li, Kai
AU - Chen, Huaping
N1 - Funding Information:
The work of the first author is supported by the National Natural Science Foundation of China under grant 71601001 , the Humanity and Social Science Youth Foundation of Ministry of Education of China under grant 15YJC630041 , the Science Foundation of Anhui Province, China under grant 1608085MG154 , the Natural Science Foundation of Anhui Provincial, China Education Department under grant KJ2015A062 . The work of the last two authors are supported by the National Natural Science Foundation of China under grants 71871076 and 71671168 , respectively.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/2
Y1 - 2019/2
N2 - We study the problem of scheduling on parallel batch processing machines with different capacities under a fuzzy environment to minimize the makespan. The jobs have non-identical sizes and fuzzy processing times. After constructing a mathematical model of the problem, we propose a fuzzy ant colony optimization (FACO) algorithm. Based on the machine capacity constraint, two candidate job lists are adopted to select the jobs for building the batches. Moreover, based on the unoccupied space of the solution, heuristic information is designed for each candidate list to guide the ants. In addition, a fuzzy local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with several state-of-the-art algorithms through extensive simulated experiments and statistical tests. The comparative results indicate that the proposed algorithm can find better solutions within reasonable time than all the other compared algorithms.
AB - We study the problem of scheduling on parallel batch processing machines with different capacities under a fuzzy environment to minimize the makespan. The jobs have non-identical sizes and fuzzy processing times. After constructing a mathematical model of the problem, we propose a fuzzy ant colony optimization (FACO) algorithm. Based on the machine capacity constraint, two candidate job lists are adopted to select the jobs for building the batches. Moreover, based on the unoccupied space of the solution, heuristic information is designed for each candidate list to guide the ants. In addition, a fuzzy local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with several state-of-the-art algorithms through extensive simulated experiments and statistical tests. The comparative results indicate that the proposed algorithm can find better solutions within reasonable time than all the other compared algorithms.
KW - Fuzzy ant colony optimization algorithm
KW - Fuzzy job processing time
KW - Non-identical machine capacities
KW - Parallel batch processing machines
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U2 - 10.1016/j.asoc.2018.11.027
DO - 10.1016/j.asoc.2018.11.027
M3 - Article
AN - SCOPUS:85057599830
SN - 1568-4946
VL - 75
SP - 548
EP - 561
JO - Applied Soft Computing
JF - Applied Soft Computing
ER -