Dynamic and Stochastic Job Shop Scheduling Problems Using Ant Colony Optimization Algorithm

摘要：

Reactive scheduling is often been criticized for its inability to provide timely optimized and stable schedules. So far, the extant literature has focused on generating schedules that optimize shop floor efficiency. Only a few have considered optimizing both shop floor efficiency and schedule stability. This paper applies a unique selfadaptation mechanism of the ant colony optimization (ACO) algorithm to enable the reactive scheduling approach to generate better and timely stable and quality schedules for dynamic and stochastic job shop scheduling problems.

关键词： Self-Adaptation Mechanism Ant Colony Optimization Schedule Stability Dynamic and Stochastic Job Shop Scheduling

作者: Rong Zhou Mark Goh Gang Chen Ming Luo Robert De Souza

作者单位: The Institute of Logistics – Asia Pacific, Singapore The Institute of Logistics – Asia Pacific, Singapore Business School, National University of Singapo School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore Singapore Institute of Manufacturing Technology (SIMTech), Singapore

会议类型: 国际会议

会议名称: The Fourth International Conference on Operations and Supply Chain Management(第四届运营与供应链管理国际会议 ICOSCM 2010)

会议地点: 香港·广州

会议语种:英文

页码: 310-315

在线出版日期: 2010-07-25（万方平台首次上网日期，不代表论文的发表时间）