Scheduling Method For Parallel Machines Under Constraints Of Completion Date And Number Of Tooling

The manufacturing industry is an ancient and vibrant industry, manufacturing industry directly reflects the level of a country’s productivity. With the continuous development of new technologies and applications, and the diversification of customer needs, the more varieties of small batch production orders has become an important part in the manufacturing sector, and this mode of production will become more common.Delivery is the baseline of order companies’production, in order to save costs and to ensure the credibility of the company, company achieve the maximum possible delivery within delivery. In some of the production process, it often requires matching tooling assistance, the number of tooling in the normal circumstances is limited. Tooling a limited number of circumstances, the maximum extent possible to meet the delivery needs, it becomes a major problem in the practice of production.A mold manufacturing enterprises as background, this paper research a class of parallel machine scheduling problem, the problem to minimize the tardiness cost objective, with the completion date of components and the number of tooling constraints. First the problem is described in mathematical language, and a single mathematical model is established for the problem; combination of a genetic and simulated annealing algorithm (GASA), the algorithm to the main line of genetic algorithms, simulated annealing algorithm as the variation of the genetic algorithm operators son, in each iteration, individuals which are selected to the mutation operation, are simulated annealing operation. On the generation of initial population, GASA use a combination of random generation and heuristic rules to generate. Then the number of actual production examples from a mold companies are simulated by Matlab, and simulation results show that the algorithm can resolve this issue fast convergence; those examples of five different sizes for the object are respectively simulated and solved by GASA algorithm and the traditional GA algorithm, the results show that the GASA algorithm for solving the optimal solution is obviously superior to the GA algorithm; five different sizes examples are selected and simulated by using the GASA algorithm and BBA algorithms, Comparative analysis of calculation results show that the optimal solution quality of two algorithms is quite, and solving speed of GASA is faster than BBA, but also over the scale of the task increases, the more obvious advantages of GASA algorithm, which shows the model and algorithm is more effective for this type of special problems. Then, taking into account the uncertainty of processing times in the actual production, based on the above research, this paper further research the parallel machine scheduling problem with fuzzy processing time, establishes a mathematical model, and uses a comprehensive evaluation of the Fuzzy Sets sorting method proposed by Lee-Li to make the objective function of this problem precise, and verify the feasibility of the GASA algorithm by simulating a example. Finally, based on the business processes of a mold enterprise’s actual production, combined with the research problem and its algorithm, a production planning and scheduling systems is designed and developed, which regulate the company’s production and operational process, and greatly increase the scheduling speed.