A Genetic Algorithm Approach in Distributed Scheduling in Multi-Factory Production Networks

This dissertation, "A Genetic Algorithm Approach in Distributed Scheduling in Multi-factory Production Networks" by Sai-ho, Chung, 鍾世豪, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled A Genetic Algorithm Approach in Distributed Scheduling in Multi-Factory Production Networks Submitted by CHUNG Sai Ho For the Degree of Doctor of Philosophy at the University of Hong Kong in December 2006 In recent years, many companies have switched from traditional single-factory to multi-factory production in order to increase their international competitiveness because of globalization. These factories may be geographically distributed in different locations. This allows them to be closer to their customers, to comply with the local laws, to focus on a few product types, to produce and market their products more effectively, and to be more responsive to market changes. Production scheduling in multi-factory environments can be classified as a Distributed Scheduling (DS) problem. Indeed, production scheduling problems in single factories have been widely studied by many researchers for many years. However, little attention has so far been paid to DS. DS problems are much more complicated than classical scheduling problems because they involve not only the scheduling problems in each factory, but also the problems in the upper level of how to allocate the jobs to suitable factories. In general, DS problems focus on solving two issues simultaneously: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding i production schedules in each factory. The objective is to maximize system efficiency by finding an optimal plan for a better collaboration among various processes. In previous studies of DS problems, many researchers have assumed that each job has only one operation and can only be processed on a fixed machine in their models. In fact, each job generally consists of more than one operation. In addition, Flexible Manufacturing Systems (FMS) have recently been implemented in many factories, enabling each operation to be processed on more than one suitable machine. Furthermore, machine maintenance has usually been ignored during production scheduling, even though in reality, every machine requires maintenance and the maintenance policy applied will directly influence the machine's availability, and consequently the production scheduling. In the light of these problems, an innovative approach, named Genetic Algorithm with Dominant Genes (GADG) is proposed to deal with DS problems in multi-factory environments in which FMS production is implemented. This approach can simultaneously determine the scheduling of maintenance during DS. In addition, the proposed GADG reduces the difficulties of controlling the genetic parameters during the implementation of GA. Meanwhile, it improves the performance of genetic search and the quality of the solutions obtained. In this thesis, a number of problems are discussed and solved. The proposed GADG is compared with other proposed approaches and found to be more reliable and robust. Second, the makespan obtained from the simultaneous maintenance scheduling during DS is compared with that from the separate scheduling, and the simultaneous scheduling approach is shown to be better. Lastly, it is demonstrated that the shape of the maintenance curve will not influence the performance of the simultaneous scheduling approach. ii DOI: 10.5353/th_b3782677 Subjects: Genetic algorithms F