Study On Job-Shop Scheduling Algorithms Under Uncertain Environment

With the development of industrial technology and globalized market economy,the demandsfor production management of manufacturing enterprises become higher,especially the coretechnology in production management—Job Shop Scheduling.Previous studies on Job ShopScheduling problem are mostly concentrated in the deterministic Job Shop Schedulingproblem,which cannot accurately describe the random factors in the actual production.Accordingto the uncertain factors in production,the paper studies the algorithm to solve the Job-ShopScheduling problems under uncertain environment.This paper studies mathematical modeling of the Job Shop Scheduling problem,analyses themodeling method of the problem,the input and output data,the optimization objective and thecommonly used optimization algorithms method of the model.This article also studies the basic Genetic Algorithm,and designs a improved GeneticAlgorithm which can converge to the global optimal solution quickly,while considering theadvantages and disadvantages of the basic Genetic Algorithm.In the improved algorithm,take theelite individuals to retain and cross-group fitness value distribution adjustment of crossover andmutation probability.A job shop schedule problem model based on fuzzy mathematics theory is presented inorder to deal with the job-shop scheduling under uncertain environment.The uncertainprocessing time is represented by triangle fuzzy numerals.The uncertain due date is representedby trapezoidal fuzzy numerals..The optimization target is to maximum the average customersatisfaction of the products.A improved genetic algorithm based on fuzzy mathematics isproposed to solve the uncertain job-shop scheduling problems.Simulation experiments which useMATLAB are carried on to prove the effectiveness and stability of the algorithm and achieve thepurpose of solving the Job-Shop Scheduling problem under uncertain environment.