Research On The Optimized Scheduling Algorithm For Manufacturing Workshop Under MTO Environment

Job shop scheduling problem could improve the machine utilization rate, reduce theproduction cost, and minimize the makespan through reasonable scheduling decision.Under the circumstance of MTO, because of the urgency, complexity, diversity of theorder, how to complete as soon as possible in order to reduce the delay loss has become aproblem for enterprises to solve immediately. Therefore, in order to meet the requirementsof MTO manufacturing, the routine production scheduling is required to determine theorder of priority accurately and to arrange the production more reasonably to improve thecompletion of time. Therefore, according to the characteristics and needs of the MTOscheduling problem, this paper takes the order manufacturing workshop as background,researches the issue from aspects of establishing the order based on the TOC theory andestablishing the improved ant colony algorithm based on job shop scheduling, whichmakes the enterprises more competitive.This paper takes the order manufacturing workshop as background. According to thelimited source of job shop and the diversity of the order, this paper divides the MTO jobshop into order sorting and job scheduling. The purpose of order sorting is to minimize theoverdue loss, while the purpose of the job scheduling is to schedule the order effectivelyand to reduce the affect of bottleneck.This paper starts the research from the identification method of bottleneck resourcesbased on analyzing the Theory of Constraints (TOC), uses the matrix method to find outthe bottleneck resources, and introduces the yang’s algorithm, through the combination ofthe two methods, application for determine the order’s order. The simulation results verifythe feasibility of method, to determine the orders to minimize the loss of overdue.In order to reduce the bottleneck restriction and accelerate the completion of taskorders, much attention is paid on job shop scheduling. In order to reduce the maximumcompletion time, an improved ant colony algorithm based on adaptive elitist strategy isproposed to get rid of faults of which the ant colony algorithm easy to be trapped in a localoptimum and slow convergence speed. The elitist strategy is adopted to find the elitist ants,updates the pheromone, and accelerates the convergence speed. When the algorithm istrapped into a local optimum, it adjusts the parameters of ant colony, expands the scope ofthe search, and makes the algorithm “out of the trap”, improves the algorithm’s globalsearch ability. In this paper, simulation research is carried out according to the basic problem of10×5. The simulation results verify the feasibility and effectiveness of thealgorithm.Since the parameters of ACO are very complex and the process of setting is verycumbersome. They definitely have a great impact on the performance of the algorithm. Theman-machine interface is designed by analyzing the parameters of the ACO in order tomeet the diversity of the orders, it also simplifies the process of selecting parameters. Thisinterface can be applied to other intelligent optimization algorithm simulation or run in theselection process with little change.