Ant Colony Optimization for Single Machine On-line Scheduling Problem

• Main Authors: Yu-Sheng Chen, 陳煜昇
• Other Authors: 梁韵嘉
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/59889568601256464998

碩士 === 元智大學 === 工業工程與管理學系 === 95 === In reality, delay of the orders may cause the loss of business credibility. On the contrary, earliness of the orders may lead to enormous inventory cost. Hence, it is important to assign an appropriate due date to the orders and determine an efficient production scheduling. Thus, the purpose of this research is to develop an ant colony optimization (ACO) algorithm hybridized with different local search mechanisms to solve the single machine on-line scheduling problem. The objective of the problem is to minimize the total weighted due date and total weighted quoted lead time. When a job arrives, the production sequence will be determined and a due date will be assigned to the job accordingly by using a two-phase approach. The first phase applies the ACO algorithm to insert the arriving job into the waiting list, while the second phase assigns a slack time, which is calculated according to limited information about the future, to determine the due date of the job. Three large size instances consisting of 100, 500, and 2500 jobs and associated with three distributions for the processing times and inter-arrival times of jobs respectively are tested. Two ACO algorithms are proposed and compared with a heuristic algorithm Hi from the literature and two offline dispatching rules WSPTA and FCFS. The computational results show that ACO-II+EDD is able to solve the on-line scheduling problem effectively.