A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints

•A two-stage knowledge-based FOA for RCUPMSP.•Several properties for RCUPMSP.•Search manners with the guidance of knowledge.•Parameter setting and effectiveness demonstration. In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the rea...

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Bibliographic Details
Published in:Expert systems with applications Vol. 65; pp. 28 - 39
Main Authors: Zheng, Xiao-long, Wang, Ling
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.12.2016
Subjects:
Fruit fly optimization algorithm
Knowledge
Resource constraint
Unrelated parallel machine scheduling
Fruit fly optimization algorithm
Unrelated parallel machine scheduling
Knowledge
Resource constraint
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A two-stage knowledge-based FOA for RCUPMSP.•Several properties for RCUPMSP.•Search manners with the guidance of knowledge.•Parameter setting and effectiveness demonstration. In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the real world manufacturing systems is studied. With the objective of minimizing the makespan, a mixed integer linear programming model is presented and several properties are analyzed. Furthermore, a two-stage adaptive fruit fly optimization algorithm (TAFOA) is proposed to solve the UPMSP_RC. At the first stage, a heuristic is proposed to generate an initial solution with high quality. At the second stage, the initial solution is adopted as the initial swarm center for further evolution. During the evolution, the search manners are selected adaptively with the guidance of the problem-specific knowledge, which is a sufficient condition of the best schedule under a given job-to-machine assignment. Moreover, the effect of parameters on the performance of the TAFOA is investigated by using the two-factor analysis of variance (ANOVA). Finally, extensive numerical comparisons are carried out to show the effectiveness of the TAFOA in solving the UPMSP_RC.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2016.08.039