Multi Rules Production Scheduling Simulation Optimization In Discrete Manufacturing System

Production scheduling is the core of modern production management, especially in discrete manufacturing system. Due to the randomness of the order, imbalance and dynamic production systems, it allows the scheduling of discrete manufacturing systems particularly difficult. Rules Scheduling, as a production scheduling, it's simple, which can guarantee to solve the scheduling problem, at the same time, can highlight visibility and readability of the schedule scheme. It has very important significance to enhance the core competitiveness for manufacturing companies, increase economy efficiency of enterprises, as well as develop the customer satisfaction.Literature studies have shown that multi-rule scheduling can better improve the performance of production systems than a single rule scheduling. The multi-rule scheduling approach for discrete manufacturing systems is to introduce the idea of combination multiple rules into the rules scheduling, considering the feeding, processing, handling, maintenance and other activities of a production system. This paper focuses on the application and solution seeking of multi-rule scheduling approach in discrete production system, and studies as the following.①The discrete manufacturing system was divided into a number of decision points, including processing activities and auxiliary processing activities. Introduce the scheduling rules and decision parameters for each decision point, selecting the average production cycle as performance indicators, the combination of rules and system parameters as decision parameters, Then, construct the production scheduling model for the discrete manufacturing system based on multiple rules, and propose the simulation optimization method for solving such issues.②The integration and customization capabilities based on arena software: adopting flexible simulation modeling method, dividing the discrete manufacturing system simulation model into functional module, control module and optimization module, building discrete manufacturing system simulation model and designing scheduling rule base for control module.③Select the tabu search as optimization algorithm, and design tabu search algorithm based on multi-rule scheduling. The events from the simulation model implemented the combination of the tabu search algorithm and simulation model.④As an example, the hot-rolled-cold-drawn steel tube production line, proved the feasibility of the study, through which we analyzed the effect that the simulation times had on the optimization results of the combination of rules.