The Research Of The Multi Objective And Flow Shop Scheduling Based On The Whole Set Orders

The problem of customers'orders is the center of the operation of enterprises. With the due time limits of orders or jobs, how to schedule the production of jobs to maximize the demand of customer is always being the hot problem in the research field of production operation. This paper proposes a multi objective model to the coordinated planning of production and jobs delivery based on the whole set orders.First, a definition of the whole set orders problem and its basic model—maximizing the Weighted Number of whole set orders in Single Machine Scheduling are introduced as the base of the whole research .On the basis of the basic research of whole set orders problem in Single Machine Scheduling, Considered the process of flow shop and its complexity, a 0-1mix integer model is established to maximize the weighted whole-set orders. The algorithm of combined composite dispatching rule with local searching is proposed to solve the model .An example is given to prove the efficiency of the combined algorithm, but this algorithm has some limitations on the global optimization. And then, we propose a multi objective model to the coordinated planning of production and jobs delivery based on the whole set orders. In this problem, we define the due time to jobs are transported to customer instead of the jobs'finished time on the production lines. We both consider the production of jobs and the delivery of finished jobs to the objective customers, with the capacity limitation of the conveyance, so that we setup two functions about maximizing the number of weighted whole set orders and minimizing the total transportation time. In addition, an algorithm of limited solution of multi objective scheduling, based on the Genetic Algorithm and a heuristic algorithm is designed to solve the above model. Consequently, a numeric example is given to prove the efficiency of the algorithm. Operations of the above example indicate that the option of the limitation for the first objective function greatly influences the effect of this algorithm.