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Esearch on the Optimization Method for Flexible Resource Scheduling Based on Genetic Algorithm

Author: ZhaoShiKui
Tutor: GuXinJian; FangShuiLiang
School: Zhejiang University
Course: Industrial Engineering
Keywords: Flexible Resource Scheduling Flexible Job Shop Scheduling CloudManufacturing Resource Scheduling Genetic Algorithm Population Initialization MachineSelection Neighborhood Search
CLC: TP18
Type: PhD thesis
Year: 2013
Downloads: 85
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Abstract


With the development of processing technology and automation technology, production systems with a certain flexibilities including flexible manufacturing systems and CNC machining centers appeared. Flexible job shop with flexible resource selection gradually became a powerful tool to cope with dynamic market environment and sudden machine failures. Combining with manufacturing technologies, the improvements of new generation information technologies including internet, cloud computing, internet of things, and so on, gave birth to a new kind of advanced manufacturing modes:cloud manufacturing. The great social manufacturing resources were joined together to provide a variety of manufacturing services by cloud manufacturing. Flexible resource scheduling technology is a key technology in cloud manufacturing. In light of the related projects of our research group, it focused on the study of flexible resource scheduling problem in this dissertation. First, flexible job shop scheduling problem(FJSP) in the flexible workshop environment was studied in depth. Then, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing.In Chapter Ⅰ, it introduced the research background and significance, and gave an overview of the shop scheduling problem and cloud manufacturing. The research status of the domestic and foreign were summarized, and the limitations were analyzed. Finanlly, the main research contents and structure were presented for the dissertation.In Chapter Ⅱ, the overall technical framework for FJSP were studied. Firstly, the FJSP problem was described, and its mathematical model was given. Then, genetic algorithm was selected for solving FJSP, and the overall technical framework based on genetic algorithm was studied. The overall research ideas were determined to provide guidance for subsequent chapters on FJSP.In Chapter Ⅲ, optimization initialization method based on genetic algorithm of machine selection chains was studied for FJSP. A novel machine chains initialization method based on short time and workloads balancing strategies using genetic algorithm was proposed. To optimize the quality of machine chains quantitatively based on genetic algorithm. The optimized machine chains were selected and combined together as the initial machine selection population for the FJSP solving genetic algorithm. Finally, the feasibility and validity of the proposed method was demonstrated with some typical scheduling examples.In Chapter Ⅳ, an effective machine initialization method based on the limit scheduling completion time minimization was proposed for FJSP. While initializing machine selection chains, global selection and local selection were adopted macroscopically to optimize maximum machine load and maximum job processing time respectively. Microscopically, to select the operation in random sequence instead of the job processing order, and further to compare the processing time based on considering the optional machine load for selecting machine. It took into account both of the maximum machine load and maximum job processing time optimization. Machine selection results of benchmarks were analyzed, and the effectiveness of proposed method was verified.In Chapter V, it studied the initialization method of operations sequence population and operation-based encoding neighborhood search mechanism of genetic algorithm for FJSP. Initialization method of operations sequence was designed by combining active scheduling, non-delay scheduling with heuristic rules. Neighborhood search moving of key operations was carried out based on operation-based encoding to avoid infeasible solutions and chromosome testing-repair work. A method for chromosome standardization based on the active decoded gantt chart was proposed. Neighborhood search was implemented on the standardized chromosome individuals. Finally, benchmarks were applied to test and verify the effectiveness of the proposed algorithm.In Chapter VI, a kind of neighborhood search genetic algorithm for FJSP based on idle time was proposed. Idle-time-based neighborhood structure was designed. Shift positions of critical operations were identified by finding the machine idle time for critical operations. To ensure the feasible solution, relative position shift conditions of the operation were given and proved. The idle time between two adjacent operations on the same machine was analyzed, and the method of maximum finding critical operation’s machine idle time was provided. To find machine idle time both in front and behind of the critical operation. On condition that guaranteeing feasible solution, neighborhood search was achieved by shifting the critical operation to the idle time. The idle-time-based neighborhood search was implanted in genetic algorithm. Finally, the proposed algorithm was tested on benchmark examples and enterprise actual case, and its effectiveness was verified.In Chapter VII, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing. Flexible services scheduling optimization model for complex products with network structure in cloud manufacturing was built. Genetic algorithm was adopted to solve the problem. Task-rank-based zoning encoding method was designed. Several feasible crossover and mutation methods were provided for zoning coding. Decoding method with transportation time and task order constraints were studied. The feasibility and effectiveness of the algorithm were verified by simulation example. It summarized the dissertation and prospected the future research work in Chapter VIII.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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