Research Of Double Target Production Scheduling Problems In Multiple Processors Flowshop

The virtuality of production scheduling is a class of optimal scheduling problem,and is a research direction of operation research. This problem can generally bedescribed as: given the premise of production tasks according to the order, the limitedhuman and material resources allocated to different tasks, so as to meet certainspecified targets. Typical scheduling problems include the collection of the finishedproduct; a process operation for each product; the equipment or other resources of thevarious processes, which must be processed according to certain routes to beprocessed. The goal is to arrange the processing order and the processing start time, toget the order to meet the constraints, while making some performance indicators arebeing optimized. Production scheduling problem with multiple constraints, multipleobjectives, uncertainties and other characteristics, is a typical NP-hard problem. Asthe key production management, it is important to study the modeling, optimizationand to improve the production efficiency.For multi-objective production scheduling problem, the context of this article toa production enterprise, based on the dual goals of the study the minimum completiontime with minimal human resources. Studying the related multi-objective optimizationtheory. Advance a fitness sharing strategy to avoid simply fitting a single objectiveproblem. Draw on the basic of genetic algorithm and hybrid algorithm, and combinedwith improved production scheduling problems, we applied them to flow shopscheduling problem.The main work of the paper is as follows:(l) Summarized the case of multiple processor flow shop target productionscheduling problem. Combined to the scheduling theory and the actual situation ofproduction, and create a realistic target production scheduling model.(2) In-depth analysis of multi-objective optimization methods, and then elicit togenetic algorithm for multi-objective optimization problem, which makes good resultsfor solving engineering optimization problems. In this paper, the basic principles ofgenetic algorithm and framework are used to solve the multi-objective optimizationproblems. The proposed genetic strategy of the algorithm has a good effect withnon-inferior solution for the Pareto Elitist.(3) Base on the character of genetic algorithm for the dual-optimization problem,and combined with the principle of the particle swarm algorithm, proposed a hybridalgorithm for dual-objective flow shop production scheduling method. The hybridalgorithm combines the advantages of each algorithm. by fitness sharing method, thesolution to be problem is assessed. So the information of non-inferior solutions arefound and used effectively. The simulation results of different problems demonstrate the superiority of the model and proposed algorithms.