Planning Of Workload Allocations In Production Scheduling Base On GSA

With the development of the technology and economic,the markets give much pressure and competition to the corporations,and it requires the corporations more quickly to respond the markets ever-changing and finishes their work planning to meet the requirement of the markets.Therefore the enterprises have to produce suitable products in correct time due to the demands. Researchers all over the world have done a lot of work in such fields, one of the typical work is the GRAI model and according methods which was first presented by Lab at University of Bourdeuax Ⅰ,France. According as the turn-on intensity running rules of the place ,this paper improves the liner model of workload allocations as to set up a corresponding nonlinear model which base on the minimizing costs of manufacturing and storge.The Genetic Simulated Annealing Algorithm (GSA),applied to the complicated problem,enhances the flexibility and the efficiency of the workload allocations.The researches involve the following aspect : 1. Base on the turn-on intensity running rules of the place, this paper improves the liner model of workload allocations which use the matrix analysis theory of Petri nets as to set up a corresponding nonlinear model. 2. In this paper, a genetic simulated annealing algorithm (GSA), which combine the advantages of the genetic algorithms and the simulated annealing algorithm, is employed to workload allocations.The genetic operations,penalty function and the control parameters of the simulated annealing are analyzed in detail and designed carefully. In order to select the appropriate parameters, an example of workload allocations is tested to demonstrate the different effects of different parameters in vertical direction; On the other hand , In order to compare the optimization performance of Genetic Algorithms,Simulated Annealing Algorithm,Genetic Simulated Annealing Algorithm, the example is also tested to demonstrate the different effects of different Algorithms in horizontal direction,the results prove the feasibility and validity of the Genetic Simulated Annealing Algorithm.