文献类型：期刊文献

英文题名：Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment

作者：Zhang, Hankun[1];Buchmeister, Borut[2];Li, Xueyan[3];Ojstersek, Robert[2]

第一作者：Zhang, Hankun

通讯作者：Ojstersek, R[1]

机构：[1]Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China;[2]Univ Maribor, Fac Mech Engn, Maribor 2000, Slovenia;[3]Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China

第一机构：Beijing Technol & Business Univ, Sch E Business & Logist, Beijing 100048, Peoples R China

通讯机构：[1]corresponding author), Univ Maribor, Fac Mech Engn, Maribor 2000, Slovenia.

年份：2021

卷号：9

期号：8

外文期刊名：MATHEMATICS

收录：;Scopus(收录号：2-s2.0-85105215583);WOS:【SCI-EXPANDED(收录号:WOS:000644519700001)】；

基金：This research was funded by "The Research Foundation for Youth Scholars of Beijing Technology and Business University, Grant number QNJJ2020-41", "The Capacity Building for Scientific and Technological Innovation Services-Basic Scientific Research Business Expenses, Grant number PXM2020_014213_000017", "The Youth Project of Humanities and Social Sciences Financed by the Ministry of Education, Grant number 20YJC630069" and "The Slovenian Research Agency (ARRS), Grant number P2-0190".

语种：英文

外文关键词：metaheuristic algorithm; Improved Heuristic Kalman Algorithm; cellular neighbor network; simulation modeling; decision-making; dynamic job shop scheduling

摘要：As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.