Production Scheduling Of Discrete Manufacturing Under Complete Kit Concept

The major intention of this dissertation is to research production scheduling in the discrete manufacturing underlying complete kit(CK).In high-variety low-volume(HVLV) or in one-of-a kind production(OKP) environment,shortage of components is the common things the machining-and-assembly type enterprises confronted with.It has strong impact on the synchronization of the manufacturing process.Moreover,the management information system(MIS) applied in enterprise centered by material requirements planning(MRP) did not provide the precedence relationship among jobs and the degree of product complete kit clearly, and this have already wakened the performance of the manufacturing system.Still further,the traditional production scheduling researches ignored the job availability and the importance of customer order.This dissertation researches two basic kinds of production scheduling problem toward complete kit:the complete job shop scheduling problem(CJSSP) and the customer order scheduling problem in job shop(COSPJS),with the motive to improve the performance of manufacturing system and enhance the satisfaction level of customers.The main research work lies in six aspects as follows.(1) The state-of-the-art of the literature concerning CK concept are surveyed.On the base of CK connotation analyses,CK terms are defined and CK application problem are traversed. Moreover,CK-oriented production scheduling paradigm is proposed.On the foundation of production scheduling overview involved assembly constraint and production theory analysis, this thesis put forward CJSSP concept.CJSSP are classified according three kinds,and its meaning and purpose are gathered up.Based on the systematic problem description,the single-product and multi-product model of CJSSP are established.(2) The infeasible search space based genetic algorithm(GA) for CJSSP are coined. The CJSSP features are explored.The extended operation-based representation is adopted in GA and the selective decoding string(SDS) is configured to deal with the infeasible chromosomes caused by the assembly constraint,therefore constraint-handling technique comes into being.The computation results indicate the feasibility and validity of the proposed approach using single- and multi-CJSSP adapted from the famous FT10 benchmark.The findings are summed up so that the feasible search space based GA are proposed.The infeasible chromosomes conversion technique is the key of it.(3) Four chromosome conversion approaches are suggested.The constraint-handling techniques are reviewed.Four conversion approaches are designed and realized based on the four requirements proposed for the conversion.Computational results indicate that all methods are viable in application,though different in speed and quality,and consistent with observation and GA application test results.Among them SLRRLS(subtree locus reversion based on root left shift) and CIP(circulatory interchanging based on pathway) are the best in quality,but the former executes rapider.(4) Four metrics for conversion quality are defined.Based on the semantic analysis of chromosome,four metrics:the gene position-preserving degree of chromosome,the gene locus right shift of parent constituents,the gene locus displacement of parent constituents and entropy loss of conversion population are designed.Both the performance tests and GA application results are conducted,the results differentiate the conversion approaches remarkably well so the metrics are confirmed convincingly.(5) The feasible search space based genetic algorithm(GA) for CJSSP are advanced. Based on the above-mentioned outcome,chromosome conversion based initial population construction is proposed.A divide-and-conquer strategy approach is adopted to maintain chromosome's feasibility:operation-based representation overcomes the precedence constraints while genetic operators tackle the assembly constraints.Two core concepts, constituent type and operable gene string,are defined to build crossover and mutation.The genetic algorithm is tested on both practical instances and problems adapted from JSSP benchmarks.Comparison between the GA results and that of some sophisticated heuristics validated the GA to be the better.(6) COSPJS is studied.The literature of the customer order related scheduling problems are summarized.COSPJS is modeled mathematically.The self-adaptation crossover and mutation and the similarity-based crossover technique are adopted in the GA.They assist customer order-based crossover and customer order end shift that are created to overcome fitness value conglomeration,to solve COSPJS successfully.Large constructed benchmarks are used to test the performance of GA.The research results in this dissertation demonstrate that CK concept is very useful to deal with shortage of components widely existed in production management of discrete manufacturing.Both CJSSP and COSPJS are the two basic production scheduling problems towards CK.They are of importance theoretically and valuable in practice.Genetic algorithms are powerful to solve these two nearly intractable problems,so as to yield favorable schedules in job shop.