TY - JOUR
T1 - A meta-heuristic for minimizing total weighted flow time on parallel batch machines
AU - Jia, Zhao hong
AU - Zhang, Han
AU - Long, Wen tao
AU - Leung, Joseph Y.T.
AU - Li, Kai
AU - Li, Wei
N1 - Funding Information:
The work is supported by the National Natural Science Foundation under grants 71601001 and 71671168 , the Humanity and Social Science Youth Foundation of Ministry of Education of China under grant 15YJC630041 , the Science Foundation of Anhui Province under grant 1608085MG154 , the Natural Science Foundation of Anhui Provincial Education Department under grant KJ2015A062 .
Publisher Copyright:
© 2018
PY - 2018/11
Y1 - 2018/11
N2 - To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.
AB - To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.
KW - Ant colony optimization algorithm
KW - Non-identical job sizes
KW - Parallel scheduling
KW - Patch processing machines
KW - Total weighted completion time
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U2 - 10.1016/j.cie.2018.08.009
DO - 10.1016/j.cie.2018.08.009
M3 - Article
AN - SCOPUS:85052949429
SN - 0360-8352
VL - 125
SP - 298
EP - 308
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
ER -