Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities

Document Type

Article

Publication Date

2-1-2019

Abstract

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.

Identifier

85057599830 (Scopus)

Publication Title

Applied Soft Computing Journal

External Full Text Location

https://doi.org/10.1016/j.asoc.2018.11.027

ISSN

15684946

First Page

548

Last Page

561

Volume

75

Grant

71601001

Fund Ref

National Natural Science Foundation of China

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