Research on shop scheduling and production scheduling problem based on genetic algorithm

This paper studies the problem of job shop scheduling, which is one of the important tasks of the research, many researchers and people who have worked for many years. A good timetable can improve the efficiency of the manufacturing system. However, given the inherent complexity of the problem and the constraints it governs, it is difficult and time-consuming to identify a good timetable. This usually requires a heavy computational effort. An increase in the size of the problem of exponential growth in computational volume. Pure mathematical optimization in the application of methods to determine the best solution may not be effective in practice, even in classic scheduling problems. A part of the assigned time resource's task is known to be defined as a Task scheduler. The problem involves setting a schedule that meets all the logical constraints of timing and scheduled tasks. This problem, in its general form, has been proved to be a completely NP problem. &http://www.aliyun.com/zixun/aggregation/37954.html ">nbsp; Heuristic method, it can get a close to the optimal solution, in a relatively short term, more appreciative and practical. Many different heuristic methods have been proposed, for example, scheduling rules, such as the first advanced first out, the shortest processing time, the key is that although there are many traditional sequential rules, such as the shortest processing time rules, each rule according to the state of a workshop, showing different performance. In recent years, some of the other heuristic methods have been widely adopted, such as branch offices, and bound into a book, mountain climbing algorithm, simulated annealing algorithm, Tabu search algorithm, genetic algorithm. In different heuristic methods, genetic algorithms are widely considered to be an appropriate scheduling and efficient method.   DS in multi-plant production machinery maintenance GA is a promising tool for solving realistic problems. Solve operational problems with GA, and provide a review file based on the nature of their problem classification. The paper gives a detailed tutorial survey, using GA to solve the classic job shop scheduling problem. In its first part of the investigation. In the second part, they commented on the thesis solving JSP using a hybrid genetic algorithm. A genetic algorithm was proposed to solve the problem of single process scheduling (SPPS). A hybrid genetic algorithm is adopted, which combines GA with scheduling rule (the earliest expiration date) to solve the multi-objective scheduling problem. Combining genetic algorithm and fuzzy logic model uncertainty, 2003.html "> Production date delivery time and sequential scheduling problem." This paper presents a technology based on genetic algorithm, which obtains quite good schedule, a dynamic reconfigurable production system, which includes several production lines that can be reconfigured to two or three separate lines, or regroup into single lines. The results show that the performance is better than the traditional standard scheduling rules, such as the shortest processing time and the earliest expiration date. Performance compared to their GA and stochastic construction Methods (stocom) for multimodal resource-constrained project scheduling. They put forwardA sequential crossover method applies mutations to change the business model. A hybrid method of GA and heuristic is proposed, which combines several heuristic algorithms designed to solve a specific cross operating system, which is irrelevant to parallel job shop scheduling problem.   Yongkaida APs to solve the scheduling problems that cannot be satisfied by ERP Http://www.yukontek.com,APS Production planning management expert 400-076-7600,021-68886010