Research Of Flow Shop Scheduling Problems Under Uncertainty

Production scheduling problems is about study of how to allocate resource to meet one or more production targets and allows manufacturers to obtain the maximum economic benefit and social benefit with the conditions of finite resources. Production scheduling occupy a strategic position of importance in production management in the manufacturers. It is the most important thing in management of the manufacturers that how to allocate the available resources to meet the specific production targets in order to keep in lead in the increasingly fierce market competition. Therefore production scheduling problem has been the research focus for many enterprises such as manufacturers all along. The study of production scheduling problem has important theoretical significance and practical significance.The thesis carries out systematically expounded of the production scheduling problems, and it gives most importance to the research of the Flow Shop scheduling problem. There is so much uncertainty in real production which make the optimal scheduling performance no longer feasible with certainty model and certainty method, so this paper takes the uncertainty in real production as an indispensable condition when resolving production problem. Next is the meaning and classification of the uncertainty in production scheduling problem and the description of Flow Shop scheduling problem under fuzzy due date.The research methods of production scheduling problems experienced a process from simple to complex. Those methods explains the abstraction and simplification of complex, multi-objective, dynamic scheduling problems in specific production environment from different level. And the main criteria to evaluate a scheduling algorithm is the satisfaction of effect of optimization. Traditionally scheduling optimization method mainly concentrate on mathematical programming, simple rule and so on. In the relate decades, the studies of genetic algorithm, simulated annealing algorithm, neural network algorithm and fuzzy logic has been very active, and these studies has become one area of the scheduling research hotspot. As an important research method, the genetic algorithm realizes random optimization and effective researching by simulating Darwinian principle of the survival of the fittest and the function in the iteration of Mender's genetic variation, and it has been a most important method in production research field. The main idea and procedure and the design of the parameters about the algorithm is given in the paper, and it is used to research the Flow Shop scheduling problem.The paper aims to research the production scheduling algorithm with uncertain product condition and focuses on the Flow Shop scheduling problem, and it gives most importance to the research of earlobes and tardiness flow shop scheduling problems with finite intermediate storage. Along with the development of Just-in-time, the enterprises aim to achieve the maximization customer's satisfaction level which means the punishment value of earlobes and tardiness flow shop scheduling problems should be as small as it can be. The thesis proposes a method of satisfaction to resolve flow shop scheduling problems. What's more, because the instability of product in production procedure, it can be stored in the intermediate storage between two production units within a finite period of time. The paper studies flow shop scheduling problems with finite intermediate storage with different delivery windows under uncertainty. It describes the uncertainty of processing time using triangular fuzzy members, and using the natural coding method generates initial population randomly. It takes the reciprocal of expectation value of the punishment as the fitness objective function value, which follows the using of selection, crossing and mutation of the genetic algorithm. According to the experimental result the research method of this paper is effective.