Volume 6 Number 4 (Apr. 2011)
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JSW 2011 Vol.6(4): 584-594 ISSN: 1796-217X
doi: 10.4304/jsw.6.4.584-594

Adaptive Hybrid Ant Colony Optimization for Solving Dual Resource Constrained Job Shop Scheduling Problem

Jingyao Li, Shudong Sun, Yuan Huang
School of Mechatronics, Northwestern Polytechnical University, Xi’an, China; Key Lab of Contemporary Design and Integrated Manufacturing Technology, MOE, Northwestern Polytechnical University, Xi’an, China

Abstract—This paper presents a scheduling approach, based on Ant Colony Optimization (ACO), developed to address the scheduling problem in manufacturing systems constrained by both machines and heterogeneous workers called as Dual Resource Constrained Job Shop Scheduling Problem with Heterogeneous Workers. This hybrid algorithm utilizes the combination of ACO and Simulated Annealing (SA) algorithm and proposes an adaptive control mechanism based on ant flow of route choice to improve the global search ability. Two adaptive adjusting schemes of parameters based on iteration times and quality of solutions respectively are imposed to actualize the performance optimization by stages. Then the performances of different optimization methods with different resource allocation strategies are compared according to simulation experiments on both concrete instance and random benchmarks while related discussion are represented at last.

Index Terms—Dual Resource Constrained; Ant Colony Optimization; Adaptive Adjusting Parameters; Ant flow


Cite: Jingyao Li, Shudong Sun, Yuan Huang, "Adaptive Hybrid Ant Colony Optimization for Solving Dual Resource Constrained Job Shop Scheduling Problem," Journal of Software vol. 6, no. 4, pp. 584-594, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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