Reaearch On Optimization Problem Of Manufacturing Process In A Discrete Manufacturing Industry

Scheduling problems and cutting stock problems exist in the production process of discrete manufacturing industry widely. It is an effective measure of increasing the utilize rate of manufacture resource that the production is arranged by the optimization scheduling. The important approach of reducing the waste of raw material, enhancing the competition ability of the enterprise is that cutting stock by the optimization scheme. Now most scheduling problems and cutting stock problems are NP-hard problems because of the production fashion of multi-varietal and small batch in discrete manufacturing industry. So it is impossible to discover the exact optimal solution in a valid time, therefore people have to find a near-optimal solution to some extent instead in practice. This paper research the specific scheduilng problems and cutting stock problems existing in the production process of a discrete manufacturing industry. The improved adaptive hybrid genetic algorithms are proposed to solve the scheduling problems, and a two-phase heuristic algorithm is proposed to solve the cutting stock problem. The fruits of the research have been employed to design the production system. A optimization production and execution system is made. Concrete research the contents is as follows:1. A semi-flow shop scheduling problem is proposed after the research on manufacturing process of the production line of train wheel in Masteel Wheel Company. This scheduling problem is similar to flow shop scheduling problem, but it is different from the flow shop scheduling problem. The jobs are allowed to pass some working procedures in the production line. The math model of this scheduling problem is upbuilt according to the real production case. A novel hybrid algorithm that combines the improved adaptive genetic algorithm with FCFS scheduling rule is proposed for solving this scheduling problem.2. A single machine scheduling problem with batchs setup time depending on sequence is proposed. The math model to minimize setup times is upbuilt. An improved adaptive hybrid genetic algorithm is brought up for solving this scheduling problem. Several operators are redesigned according to the characteristic of this scheduling problem. Follow job optimization recombination crossover is advanced for transmitting the good character to the offspring. To improve the mountain climbing ability of the genetic algorithm, the mutation is replaced by a local optimization algorithm. 3. The shape of the material used for cutting is frustum of a cone in Masteel Wheel Company. And the material has various types. This cutting stock problem is defined as one dimensional, multi-type and variable-section material cutting stock problem. In this paper, the influence of cutting slot on calculation is considered. Optimization model will be discussed in real production constraints, and propose a two-phase heuristic algorithm to solve the problem.4. The concept of gene entropy is used for calculation of adaptive probabilities of crossover and mutation, making the measures of the diversity of the population more accurate, so as to improve the performance of the algorithm.5. In the niche genetic algorithm, the concept of entropy is employed to measure the extent of individuals sharing. The niche evolution entironment is set up to suppress the similar individuals to maintain large diversity of the population, improve the capability of the algorithm .6. In Masteel Wheel Company the production optimization and execution system is designed through combining optimization decision theory, optimization algorithm with information system. This system is integrated seamlessly with existent information systems (such as MES and ERP). In addition, a structure of optimization scheduling and cutting stock I3DSS is designed based on MAS.