Research On Rush Order Inserting For Flexible Job Shop Scheduling Problem

With the flourishing development of manufacturing in our country,more and more enterprisers throw themselves into the manufacturing industry,that gives rise to the increasingly fierce competition and the diversity of customers' demands.So,the events happened all the time that suppliers often order change.These orders are named rush order,they always will be accepted because the process of the enterprise in order to improve their service quality and higher production profit.Meanwhile,these rush orders should be completed in a short time and do not be allowed to delay.For a production schedule,inserting an urgent order into the original is equivalent to adjusting all subsequent production schedules,which often results in delivery being delayed.Consequently,the reasonable arrangement of these urgent orders can not only bring more benefits to enterprise but also construct the corporate reputation.?In order to insert these urgent orders reasonably and shorten the delay time of the existing orders to the greatest extent,the mathematical model of the interpolation problem a Genetic algorithm and Simulated annealing is proposed to solve the problem.Genetic algorithm is good at global search,but easy to fall into local optimum,and its local search ability is poor.The advantage of simulated annealing algorithm is that it has the ability to jump out of local optimal solution,thus,the simulated annealing algorithm and genetic algorithm,can gather both the advantages and avoid their shortcomings,and makes the hybrid algorithm do better in searching optimization.We choose the process-machine coding method because the flexible workshop not only to determine the processing sequence but also the appropriate processing machine.In order to improve the quality of the initial solution,the order priority is determined by the grey correlation analysis method,and it is used as the guide to generate the initial solution.The block model can effectively reduce the search dimension and complexity of the solution.In order to improve the convergence speed of the algorithm,the block model is introduced in this paper.Through the three steps such as block mining,block competition and artificial chromosome combination,it can be come true that search the optimal solution further which is on the basic of the excellent population,and accelerate the convergence speed.In this paper,5 regular orders and 1 rush order are taken as cases for simulation tests,and these tests results are analyzed and will be compared with those obtained by the return and delay order placement algorithm and the continuous delay order placement algorithm.It can be seen clearly that GASA has better performance in terms of the quality and speed with solving problems,thus verifying the effectiveness of GASA.