A Property-Based Hybrid Genetic Algorithm and Tabu Search for Solving Order Acceptance and Scheduling Problem with Trapezoidal Penalty Membership Function

作     者：An, Youjun Zhao, Ziye Chen, Xiaohui Li, Yinghe Gao, Kaizhou 

作者机构：College of Mechanical and Vehicle Engineering Chongqing University Chongqing400030 China Macau Institute of Systems Engineering Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology 999078 China School of Computer Science Liaocheng University Liaocheng252000 China 

出 版 物：《SSRN》 

年 卷 期：2022年

核心收录：

主　　题：Membership functions 

摘      要：Under a highly competitive make-to-order environment, the limited production capacity and strict delivery requirement make the manufacturer needs to simultaneously consider which orders should be accepted and how to arrange these accepted orders for production so as to ensure the profitability and improving the customer satisfaction. In this context, this paper studies an order acceptance and scheduling (OAS) problem in a single machine environment with the primary purpose of partitioning orders into two subsets (i.e., the subsets of accepted and rejected orders) and scheduling the accepted orders to maximize the total net profit (TNP), and the earliness / tardiness penalty under the common due window is considered. Specifically, (1) a trapezoidal earliness/tardiness penalty membership function under the common due window is designed, and a mathematical model is established to characterize the concerned problem;(2) six important problem properties are derived for determining which orders to be accepted or rejected, arranging the processing sequence of accepted orders and deciding the start processing time for each accepted orders;(3) an effective property-based hybrid algorithm (GATS-SSRIR) is designed to deal with the concerned problem, which hybridizes the genetic algorithm, tabu search and six important properties;and (4) eleven initialization rules are proposed to generate the high-quality initial population and a greedy selection method based on the similarity of individuals is applied to enhance the diversity of solutions. In the numerical experiments, the Taguchi method is first employed to optimize the parameter combination for the proposed algorithm under different initialization rules. Second, the effectiveness and superiority of proposed properties are verified by comparing with other strategies. Next, the performance and competitiveness of the proposed GATS-SSRIR algorithm are demonstrated by comparing with the variants of three intelligent algorithms. F