Study On Soft Computing Of Production Scheduling Problem

With increasingly keen market competition, each enterprise is searching for a good management system for production and operation to improve efficiency in production, operation and management as well, thereby enhance its own core competitive advantage. While the key of production and operation management is whether the production and scheduling process could achieve the optimal solution, the research on the production planning and scheduling is of great value both in theory and in practice.The paper details the objective, types and present research of production scheduling, discusses the current research state of the flow shop scheduling and the relative algorithm of this type of problem, pinpoints existing disadvantages, and then provides the generalization of ordinal coding of genetic algorithm and genetic computing element based on the order. Finally, genetic optimization research is summarized on several typical production scheduling problems.After expounding the general idea of genetic algorithm, the comparative advantages in contrast to the traditional algorithm, the basic characteristics of genetic algorithm and its theoretical base, the paper puts emphasis on the efficiency of genetic algorithm in the scheduling of Flow Shop, and puts forward an improving genetic algorithm: the ordinal genetic algorithm based on the heuristic rules. The new algorithm introduces into the initial group the solution of heuristic algorithm, and in the group structure adopts a strategy of first ordering according to the priority of the adaptive solution, and then defining a new way of choosing probability by segments, which provides more hybridizing opportunity for optimized individuals, and designs variation-control rule to prevent single population and partial optimal solution. By the computer simulation of different scales of real examples, the results show that the new algorithm embraces all-round searching advantages toward flow shop scheduling problem, and achieves overall optimal solution.In addition, on the base of the analysis of the influences of different movements on the production practice and input order, the paper constructs a mathematical model of the flow shop scheduling in the situation of its parallel movement, illustrates the structure of hybrid algorithm and both advantages and disadvantages of three algorithms, and exhibits hybrid optimal strategy derived from genetic algorithm and relative structure procedure. With the consideration of genetic algorithm's innate drawbacks of "premature restraint" and poor partial searching capability, the paper puts forth auto-adaptive hybrid genetic optimization algorithm,combining the simulated annealing process and group evolution process. The algorithm is further used to simulate the flow shop scheduling in the parallel movement. The results display that the "premature restraint" is restrained, and the solution quality is improved.What is more, based on the computing model of the finishing time per piece of flow shop scheduling in the parallel movement, the paper analyzes the subordinate function of its finishing time per piece in respective conditions of definite due date and fuzzy due date, and mutual relationship of two objective functions between minimization of delayed term and maximization of general satisfaction, pointing that the former is the subset of the latter. And representing the satisfaction level of the manager toward finishing time of the piece with the subordinate function of fuzzy due date, making general satisfaction level as objective function, the paper accordingly sets up a mathematical model in the condition of fuzzy due date, and designs a computer simulating system in light of genetic algorithm to carry on an emulation experiment. The results are satisfactory.Furthermore, the paper discusses the optimization of production and process of binary group production system, builds an assembled model of production planning and scheduling of multi-type and multi-process by expanding the existent model, taking into consideration...