Abstract

This paper concerns with the total weighted tardiness on a single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, four heuristic methods are developed to solve real size applications including the size of 1000 jobs. Proposed heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Results denote that developed heuristics are efficient for the considered problem. Research on this topic shows that no study exists on the total weighted tardiness problem with learning effect and unequal release dates simultaneously tackled in this paper.

摘要

本文关注具有学习效果和不等发布日期概念的单机调度问题的总加权延迟。提出了一个具有二元变量的数学模型，由于其 NP 硬度，只能有效解决小规模问题。因此，开发了四种启发式方法来解决实际规模的应用程序，包括 1000 个作业的规模。建议的启发式算法是：遗传、遗传与解决方案组合、袋鼠和遗传-袋鼠混合算法。结果表明，开发的启发式算法对于所考虑的问题是有效的。对该课题的研究表明，本文没有同时解决具有学习效果和不等发布日期的总加权迟到问题的研究。