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Title:	A two-phase heuristic initialisation approach for fuzzy jobshop scheduling problems
Authors:	Iman Mousa Shaheed (P75865)
Supervisor:	Syaimak Abdul Shukor, Dr.
Keywords:	Fuzzy jobshop Scheduling problems Meta-heuristic algorithms Dissertations, Academic -- Malaysia
Issue Date:	8-Jun-2016
Description:	In manufacturing systems, scheduling is an important planning activity to help optimize the usage of scarce resources and improve customer satisfaction. In this environment, scheduling is a challenging problem due to the complexity of production flows and the uncertainty of the practical requirements. Therefore, Fuzzy Job Shop Scheduling Problems (FJSSPs) considered as one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput. Since FJSSP is an NP-hard problem, there has been a growth of interest in the development of meta-heuristic algorithms to solve it. These algorithms commonly derive near-optimum solutions within reasonable computational times, almost by two main steps. These are initialization step and then improvement step. Notably, metaheuristics performance is mainly affected by the performance of its initialization method, by means the quality and the diversity of the initial solutions generated in this step. However, previous studies ascertain several performance weaknesses of the existing FJSSPs initialization methods which are the random and priority rules-based methods. On the other hand, although heuristic initialization has been proven its effectiveness in the production of a high quality initial population that consists of solutions close to the optimum, in which accelerates the convergence of the improvement algorithm to the optimal solution, a heuristic initialization method remains lacking in the FJSSPs domain. Therefore, this study aimed to develop a heuristic approach that provides high quality and diversity initial population. To this end, a two-phase heuristic initialization approach has been developed. In the construction phase, a proposed priority rule-reverse technique integrated to the G&Tbased algorithm to create high-quality original population. While in the enhancement phase, a proposed total sub-mutation operation applied to generate FJSSP opposite solutions. This is to enhance the population diversity. To evaluate the effectiveness of the developed approach, two experiments have been conducted. In the first experiment, the developed approach applied to FJSSP benchmarks and compared with the existing initialization methods in terms of population quality and diversity. While, in the second experiment, Memetic Algorithm (MA), which initialized by the developed approach tested on FJSSP benchmarks and compared with MA that initiated by the existing initialization methods in terms of computational time. The results show that the developed approach has outperformed the existing initialization methods over a wide range of FJSSP benchmarks. Furthermore, MA initiated by the proposed approach has quickly found solutions to FJSSPs.,Certification of Master's/Doctoral Thesis" is not available
Pages:	149
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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