Study On Job Scheduling In Multi-production Mode For Discrete Manufacturing Workshop

Job shop scheduling is the very important, but weak part in the production management of manufacturing enterprises, and it is the foundation and key to realize advanced manufacture and improve production efficiency. To address the difficult problem of job shop scheduling optimization in multi-production mode for discrete manufacturing workshop, Job scheduling problem in multi-production mode for discrete manufacturing workshop are deeply studied by means of intelligent optimization algorithms, the optimization solutions are proposed respectively. The main contents of this thesis are described as follows:①An overview of the shop scheduling problem is presented. The development process, classification and characteristics of job shop scheduling (JSS) are summarized. The research status of production shop scheduling and JSS is analyzed systematically. The insufficiency of JSS research is pointed out and the purpose of this research is presented.②The classification, related theories and techniques of job shop scheduling are studied in general. First, based on analysis, the JSS problems are divided into four types, namely single objective job shop scheduling problem (STJSSP) in classic production mode, multi-objective flexible job shop scheduling problem (MOFJSSP) in static non fuzzy production mode, MOFJSSP in static fuzzy production mode, MOFJSSP in dynamic production mode. Second, regarding those four sub-problems, related general mathematical models, typical process procedures and the overall technology frameworks are elaborated.③To solve the problem in the classic JSS, two optimization techniques are proposed considering the structure and combination of algorithm, namely the optimization technique based on Immune clonal algorithm and the optimization technique based on Elite strategy with niche genetic simulated annealing algorithm. For the former, the balance of depth search and breadth search is obtained by the application of immune memory mechanism, clonal proliferation, high frequency mutation and crossover operation based on the idea of population collaborative competition and parallel computing. For the latter, algorithm performance is improved by niche technology, adaptive double point crossover and interchange mutation strategy, elitism strategy, and with the two optimization techniques the CJSSP with minimization processing cycle is further optimized . ④For static multi-objective flexible job shop scheduling only consider the process and machinery parts and ignore the problem of man-machine cooperation,the scheduling optimization technique of man-machine cooperation configuration is proposed. The basic idea of solution is as follows: according to the characteristic that most scheduling theory only pays attention to the single equipment resource scheduling currently, double resource collaborative optimization configuration multi-objective model about man and machine is put forward; and the three layer encoding method is designed about the process route and man-machine cooperation two layer flexible constraints; the production path under multiple lines is optimized by non-dominated set genetic algorithms.⑤For the optimization problem of multiple resources and process routes in static fuzzy flexible job shop multi-objective scheduling, two optimization techniques are proposed. One optimization technique is based on Multi-group Genetic Algorithm; the other technique is Improved Non-dominated Sorting Genetic Algorithm. For the former, multiple objective simplification method is used to deal with the multiple objectives which need to be optimized, the multi-objective optimization model is established with the objective of minimizing the maximum completion time and maximizing the customer satisfaction, and a Genetic Algorithm of multi-group concerted evolution is presented. With the algorithm static fuzzy multi-objective FJSSP is studied. For the latter, the non-dominated solution set mind is used to deal with the multiple objectives which need to be optimized. The multi-objective optimization model is established with the objective of minimizing the total production cycle time, maximizing the customer satisfaction and minimizing the processing cost. An Improved Non-dominated Sorting Genetic Algorithm is presented. Static fuzzy multi-objective FJSSP is optimized with the algorithm.⑥For optimization problems with the cycle and event doubly perturbed in dynamic flexible job shop multi-objective scheduling, the multi-objective optimization technique based on adaptive Genetic Algorithm is presented. The basic idea of solution is shown as follows: Firstly, the strategy of dynamic scheduling, the research method of dynamic scheduling and the planning technique of dynamic window are studied. Secondly, the dynamic scheduling type based on the cycle driving, event driving and the hybrid driving of cycle and event are researched. Finally, the Adaptive Genetic Algorithm is used to optimize the multi-objective dynamic scheduling problem based on the hybrid driving of cycle and event, and the events and dispatching cycle impacting dynamic scheduling performance fluctuation are analyzed.⑦At last, the main contents and contributions of the research are summarized, and the suggestions for further research of this topic are presented.