A Multi-Objective Evolutionary Algorithm for Scheduling Flexible Manufacturing Systems

다국어 초록 (Multilingual Abstract)

In recent decades, flexible manufacturing systems have emerged as a response to market demands of high product diversity. Scheduling is one important phase in production planning in all manufacturing systems. Although scheduling in classical manufacturing systems, such as flow and job shops, are well studied. Rarely, any paper studies scheduling of the more recent flexible manufacturing system. Since the problem class is NP-hard, different scheduling algorithms such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) can be designed to solve this problem. This paper investigates a multi-objective evolutionary algorithm for scheduling flexible manufacturing systems to minimizing makespan, earliness and tardiness and startup costs. The distinctive feature of the proposed multi-objective evolutionary algorithm is its ability to search the solution space by an intelligent method, which is unlike other meta-heuristic algorithms avoid the coincidental method. Also, answer with the best quality and highest dispersion to obtain the dominant answer is used. Finally, we carry out computational experiments to demonstrate the effectiveness of our algorithm. The results show that the proposed algorithm has the ability to achieve the good solutions in reasonable computational time.

목차 (Table of Contents)

• ABSTRACT
• 1. INTRODUCTION AND LITERATURE REVIEW
• 2. PROBLEM FORMULATION
• 3. SCATTER SEARCH METHOD
• 4. THE GENERAL STRUCTURE OF SCATTER SEARCH METHOD
• 5. THE STRUCTURE OF PROPOSED SCATTER SEARCH METHOD
• 6. NSGA-II ALGORITHM
• 7. DETERMINING THE PARAMETERS OF ALGORITHM
• 8. CONCLUSION
• REFERENCES

참고문헌 (Reference)

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