Research On Intelligent Optimization Of Job Shop Scheduling For Batch Production Of Discrete Enterprises

Job-shop scheduling is the very important, but weak part in the integrated manufacturing system. In view of the Job-shop scheduling characteristics for batch production of discrete enterprises, intelligent optimization algorithms taken as the main technical measure, the following four issues about Job-shop scheduling for batch production are deeply studied and its optimization solutions are proposed: equal lot splitting problem, multi-objective optimization problem for equal batch splitting Job-shop scheduling under parallel and ordinal shift mode, multi-objective optimization problem for equal batch splitting Job-shop scheduling under multiple process flows, multi-objective optimization problem for batch production FJSP based on JIT delivery. The main contributions of this thesis are shown as follows:①The classification, characteristic, development and method of Job-shop scheduling is systematically induced and summarized. The research status for Job-shop scheduling of batch production is analyzed. The existing problems and the research object are presented.②Description is given to Job-shop scheduling problem for batch production of discrete enterprises. Considering the characteristics of Job-shop scheduling for batch production, a six-hierarchy technical framework is proposed, including object hierarchy, rule hierarchy, influence factor hierarchy, variable hierarchy, scheme hierarchy and technique hierarchy. The basic theories and critical techniques in the framework are thoroughly studied.③For the equal lot splitting problem of batch production, two optimization techniques are proposed: one is the equal lot splitting technique based on Witness combinational simulation optimization technique (WSC-ELS), the other is that based on NSGA II(NSGAII-ELS). The former is based on decomposition optimization strategy, belonging to a local optimization method, calculating quickly, fit for large-scale equal lot splitting Job-shop scheduling problem. The latter is based on integrated optimization strategy, belonging to a global optimization method, calculating relatively slowly, fit for modest-scale equal lot splitting Job-shop scheduling problem.④For the multi-objective optimization problem of the equal batch splitting Job-shop scheduling under parallel and ordinal shift mode, two intelligent optimization techniques are put forward: one is the multi-objective optimization technique of equal lot JSP under parallel and ordinal shift mode (PO-MJSP) , the other is that of equal lot FJSP parallel and ordinal shift mode (PO-MFJSP). The basic solution thought is demonstrated as follows: Firstly, a multi-objective optimization model is established; Secondly, an improved NSGA II is presented and designed to solve the model, in which four delicacy scheduling techniques are used to reduce the makespan, including parallel and ordinal shift, time separation, similar operation and interval squeezing; Thirdly, conclusion is drawn by case study.⑤For the multi-objective optimization problem of the equal batch splitting Job-shop scheduling under multiple process flows, two intelligent optimization techniques are presented: one is the multi-objective optimization technique of equal lot JSP under multiple process flows (MPF-MJSP), the other is that of equal lot FJSP under multiple process flows (MPF-MFJSP). The basic solution thought is shown as follows: Firstly, a multi-objective optimization model is established with the objective to minimize the makespan and the manufacturing cost; Secondly, an improved NSGA II is proposed and designed to solve the model, in which process flow code is introduced to implement the optimal selection of process flow for every process batch; Thirdly, conclusion is drawn by case study.⑥For the multi-objective optimization problem of batch production Flexible Job-shop scheduling based on JIT delivery, two intelligent optimization techniques are proposed: one is the single-objective optimization technique of batch production FJSP under JIT delivery (JIT-SFJSP), the other is multi-objective optimization technique of batch production FJSP under JIT delivery (JIT-MFJSP) . For the former issue, an optimization model is established with the objective to maximize the weighted average membership degree, and an improved multi-stage hybrid mutation taboo search algorithm is put forward and designed to solve the model. For the latter issue, a multi-objective optimization model is constructed with the objective to maximize the weighted average membership degree and minimize the total flow time value , and an improved NSGA II is presented and designed to solve the model, in which the earliest allowable begin time of every process batch is introduced to eliminate the contradiction between the JIT delivery demand and quick production.⑦Finally, the whole research work of the dissertation is summarized, and the future work of batch production scheduling is given.