doi: 10.4304/jsw.6.4.584-594
Adaptive Hybrid Ant Colony Optimization for Solving Dual Resource Constrained Job Shop Scheduling Problem
- 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.
Keywords
- Dual
- Resource
- Constrained;
- Ant
- Colony
- Op
How to Cite
Jingyao Li, Shudong Sun, and 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. https://doi.org/ 10.4304/jsw.6.4.584-594
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).