Artificial bee colony algorithm for solving a bi-objective job shop scheduling problem

被引：0
作者：
Zhang R. [1 ]
机构：
[1] School of Economics and Management, Nanchang University
来源：
Advances in Information Sciences and Service Sciences | 2011年 / 3卷 / 08期
关键词：
Artificial bee colony algorithm; Job shop scheduling; Multi-objective scheduling;
D O I：
10.4156/aiss.vol3.issue8.39
中图分类号：
学科分类号：
摘要：
Job shop scheduling is a significant decision problem in modern manufacturing systems. Most existing algorithms can only handle the minimization of makespan (a.k.a. the maximum completion time). However, due date related performances are also very important for the companies in a fiercely competitive market. In this paper, we propose an artificial bee colony (ABC) algorithm for solving the job shop scheduling problem with the objectives of minimizing the makespan as well as the total weighted tardiness. As a swarm-intelligence-based optimizer, ABC systematically incorporates the function of exploration and exploitation. The aggregating functions method has been adopted for adapting ABC to the multi-objective optimization problem. Finally, the computational experiments on a set of test instances show that the proposed ABC is effective and efficient for solving the scheduling problem.
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页码：319 / 326
页数：7
相关论文
共 19 条
  • [1] Lenstra J.K., Kan A.H.G.R., Brucker P., Complexity of machine scheduling problems, Annals of Discrete Mathematics, 1, pp. 343-362, (1977)
  • [2] Song C., Liu X., Wang W., Et al., Dynamic Integrated Algorithm for Production Scheduling Based on Iterative Search, Journal of Convergence Information Technology, 5, 10, pp. 159-166, (2010)
  • [3] Zhang R., Wu C., A hybrid immune simulated annealing algorithm for the job shop scheduling problem, Applied Soft Computing, 10, 1, pp. 79-89, (2010)
  • [4] de Giovanni L., Pezzella F., An improved genetic algorithm for the distributed and flexible job-shop scheduling problem, European Journal of Operational Research, 200, 2, pp. 395-408, (2010)
  • [5] Vela C.R., Varela R., Gonzalez M.A., Local search and genetic algorithm for the job shop scheduling problem with sequence dependent setup times, Journal of Heuristics, 16, 2, pp. 139-165, (2010)
  • [6] Sun L., Cheng X., Liang Y., Solving Job Shop Scheduling Problem Using Genetic Algorithm with Penalty Function, International Journal of Intelligent Information Processing, 1, 2, pp. 65-77, (2010)
  • [7] Zhang R., Wu C., A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling problem, Engineering Optimization, 42, 7, pp. 641-670, (2010)
  • [8] Sha D.Y., Hsu C.Y., A hybrid particle swarm optimization for job shop scheduling problem, Computers & Industrial Engineering, 51, 4, pp. 791-808, (2006)
  • [9] Fox B., Xiang W., Lee H.P., Industrial applications of the ant colony optimization algorithm, The International Journal of Advanced Manufacturing Technology, 31, 7-8, pp. 805-814, (2007)
  • [10] Pinedo M.L., Scheduling: Theory, Algorithms and Systems, (2008)
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