The Model And Its Application For ERP Multi-objective Discrete Manufacturing Scheduling Which Based On Particle Swarm Optimization Algorithm

Job shop scheduling is one of the core ERP systems in the productionmanagement module. On the basis of the traditional ERP planning and schedulingsystem, this paper introduces a production scheduling system based on the capacitybalance plan, so as to control the balance between the production load and ability inthe production effectively. Aiming at the problem the multiple process routes and themulti-objective job shop scheduling problem of discretely manufacturing, the authorproposes an optimized particle swarm algorithm on the basis of the decision makers’preference to establish the mathematical model of the ERP-oriented productionscheduling system. Taking an engineering machinery manufacturing as an example,the author deduces the model’s implementation process in multi-objective schedulingof the discrete manufacturing shop which product structures are complex. Finally, anintegration framework of the workshop scheduling module based on the optimizedparticle swarm algorithm integrated with other modules of ERP system can be givenin this paper, which provides important and practical value for the process-complexdiscrete manufacturing ERP system to achieve scientific and effective production andoperations management with ERP system. The main achievements have been made asfollows:(1) The research on the production scheduling system which based on thecapacity balance plan: from order to product submitted, the paper puts forward threethoughts. Firstly, equilibrate the production tasks during the week according to theproduction planning management; and then obtain the specific arrangement process ofthe workshop working procedure on the basis of real-time capacity; finally, completeproduction-making through workshop process. Besides, the system is the complement and improvement one which has been improved on the basis of the traditional ERPplanning and production scheduling system.(2) The research on the goal of job shop scheduling: it is a supplement oftraditional scheduling goal research which involves only efficiency and cost factorswhen introduce the capacity factor index of average ultra-poor scheduling time andcapacity balance into scheduling goal.(3) The research on the particle swarm optimization algorithm based on thedecision makers’ preference: firstly, the particle swarm optimization algorithm isintroduced into the multiple process routes and the multi-objective job shopscheduling problem, and the mentioned swarm optimization algorithm based on thedecision makers’ preference is the improvement to the basic particle swarmoptimization algorithm among which the preference of decision-makers can guiderandom search direction and the inertial weight setting can improve convergenceperformance of the algorithm. At the same time, because of its particularity anduniversality, the algorithm suits for other researches on production schedulingproblems as well.(4) The research on job shop scheduling module integrated with other modules:the capacity balance production scheduling system based on the improved particleswarm optimization algorithm and ERP system integration framework achieves thedocking between job shops scheduling module and other modules of ERP systemwhich provides theoretical theory and reference value for the ERP system’s effectiveimplementation of the discrete manufacturing enterprise.