Application And Research Of Improved Particle Swarm Optimization Algorithm In Vulcanization Workshop Scheduling

With the arriving of the computer era, traditional tire production faces huge challenge. How to product efficiently and satisfy clients’ demands becomes a problem to be solved presently. Since vulcanization process is an important process of tire production,optimizing workshop scheduling is vital to improving production level of tire companies. Therefore, this paper takes tire vulcanization workshop as research subject,studying the scheduling of vulcanization workshop.This paper expounds the current research situation of vulcanization workshop scheduling by consulting a large number of documents and analyzes the production characteristics of tire vulcanization process. According to practical production situation that vulcanization workshop has multiple targets and restrains,and considering all kinds of production factors which can influence vulcanization workshop,it brings up and establishes the mathematics model of vulcanization workshop.Aiming at the problem of vulcanization workshop scheduling, it brings up an improved PSO(Particle Swarm Optimization) which is combined with genetic operator.This algorithm firstly takes a search tactics which combines the parts with the whole,introducing the notion of local extreme value to revise the velocity formula of PSO,avoiding premature convergence of the algorithm. Then it mixes with genetic algorithm, and approaches the best value by selecting, crossing and mutating operators.According to the characteristics of vulcanization workshop, it adopts coding method which bases on tasks, making production tasks match vulcanization machines. In theend it applies improved PSO to work out the problem of vulcanization workshop scheduling, verifying the feasibility of the algorithm in practical scheduling.Then,it introduces relevant notions of Pareto solution in order to research vulcanization workshop of multiple targets. According to the characteristics of multi-objective vulcanization workshop, it brings up a multi-objective improved particle swarm optimization algorithm. This algorithm takes adaptive inertia weight to advance the particle velocity updating formula, keeping the population’s variety. Then it combines with heredity algorithm, and improves the search performance, making the improved algorithm satisfy the production demand of multiple targets. Compared with other improved algorithms, this algorithm verifies its superiority.Finally,it researches multi-objective vulcanization workshop under the dynamic uncertain status,introducing scrollable window re-dispatch tactics, disposing dynamic scheduling based on event driven re-dispatch tactics, using multi-objective improved particle swarm optimization algorithm to re-dispatch production tasks, and taking vulcanizing machine as an example to make simulation experiment. The simulation result verifies that this method is suitable for solving dynamic workshop scheduling.