Flexible Disassembly Job Scheduling For Remanufacturing And Its Application

With the development of world economic and quickly changing in science and technology, new products that are more powerful and better quality constantly improve the people’s material and spiritual culture level, but leading to a rise in the number of discarded products excessive exploitation of natural resources and the deterioration of the environment. Remanufacturing en-gineering can reuse the waste materials, not only saving resources and protecting environment, but also enhancing the attachment value of the deep processing. Disassembly technology is one of the key problems to recycle and remanufacture products effiently. With the expanding of disassembly industrialization and the development of flexible disassembly equipment, flexible disassembly jobs are increasingly widely used. In order to improve the utilization of flexible disassembly equipments and the efficiency of disassemly and the management level, shorten the disassembly time and reduce the cost, research on multi-objective optimization for Flexible Disassembly Job Scheduling(FD JS) has important significance in theory. In this paper, proposed FDJS model for remanufacturing, designed Dynamic Pixel Size Particle Swarm Optimization(DPSPSO) solving FDJS, evaluted schemes of FDJS based on Entropy Method-AHP, applied the sample to validate the correctness and feasibility of these theory and method.The main contents are as follows:In the first chapter, introduced the background and significance of this paper, reviewed the current development of remanufacturing disassembly and flexible job scheduling, discussed the problems about FDJS for remanufacturing, gave the contents and framework of this paper.In the second chapter, presented FDJS model for remanufacturing. Summarized the charac-teristics of FDJS, gave multi-objective functions including the maximum completion time, the disassembly cost and green, listed the constraint conditions of the model.In the third chapter, designed Dynamic Pixel Size Particle Swarm Optimization(DPSPSO) to solve FDJS. DPSPSO presented dynamical update of the pixel size and the pixel distance, calculated probability of crossover and mutation operation adaptively, realized the optimization of Particle Swarm Optimization. Aiming at the characteristics of FDJS, designed multi-level coding method and discreted crossover and mutation operation adaptively to solve multi-objective FDJS problem and obtained several solutions.In the fourth chapter, this paper researched the multi-criteria evaluation method for FDJS based on Entropy Method-AHP. According to the decision maker’s preference, applied AHP to distribute weights for each evaluation index; Based on the data of each evaluation index, used the Entropy Method to gain the entropy weights, then obtained the comprehensive weights of each evaluation index, gave play to their respective advantages. And the instance of FJDS’solutions was introduced to validate the method.In the fifth chapter, applied the theory and methods in the dissertation to a complex model, and proved them feasibility and practicability.In the sixth chapter, summarized the main research contents of this paper, and prospected the further research work.