Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 3, Pages 1430-1442Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2939219
Keywords
Optimal scheduling; Job shop scheduling; Heuristic algorithms; Genetic algorithms; Dynamic programming; Batch-processing machine (BPM); differential evolution (DE); operator adaptation; parameter adaptation; scheduling
Categories
Funding
- National Natural Science Foundation of China [61773120, 61873328, 61876163]
- National Natural Science Fund for Distinguished Young Scholars of China [61525304]
- Natural Science Foundation of Hunan Province [2018JJ3891]
- Dongguan Innovative Research Team Program [2018607202007]
- Foundation for the Author of National Excellent Doctoral Dissertation of China [2014-92]
Ask authors/readers for more resources
This article discusses a single BPM scheduling problem with unequal release times and job sizes, proposing a self-adaptive differential evolution algorithm to address the issue. Experimental results show that the proposed algorithm is more effective in solving the scheduling problem compared to other existing algorithms.
Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available