Research On Flow Shop Scheduling With Order Selection And Its Intelligent Optimization Algorithms

In order to cope with the increasingly fierce market competition better, avoid market risks and meet market demand, More and more manufacturing enterprises have transferred their former stocking-type production model to make-to-order(MTO) production model. Customers usually demand manufacturing enterprises to complete the order production within the stipulated delivery time. Otherwise manufacturing enterprises would bear the loss of economic, market reputation, and other aspects. Under this manufacturing environment, MTO manufacturing enterprises must implement order selection and order scheduling at the same time to maximize total revenue of enterprise. Therefore, the research of decision problem of order selection and order scheduling would not only enhance the market competitiveness of MTO manufacturing enterprises, but also enrich and improve the theoretical results of order selection and order scheduling in the MTO production system.This thesis studies the flow shop scheduling problem of order selection: order acceptance and flow shop scheduling problem with the flow shop scheduling problem of order outsourcing, building corresponding mathematical model respectively, designing intelligent optimization algorithms and testing the effectiveness of the algorithm through simulation. The main research work of this thesis is summarized as follows:(1)Introducing the flow shop scheduling problem, order acceptance and scheduling problem with the flow shop scheduling problem of orders outsourcing respectively. The main principles include these three issues and research status.(2)A brief introduction of intelligent optimization algorithms and a detailed introduction of three algorithms related to this thesis: genetic algorithm, variable neighborhood search algorithm and artificial bee colony algorithm.(3)In connection with the order acceptance and flow shop scheduling problem, providing a parallel variable neighborhood search algorithm. In the algorithm, two string representation, new types of neighborhood structures and special parallel searching mechanism are used. We compared the parallel variable neighborhood search algorithm with the genetic algorithm and the artificial bee colony algorithm. The experimental results show that the parallel variable neighborhood search algorithm has good optimization ability and results.(4)In connection with the flow shop scheduling problem of order outsourcing, providing an improved artificial bee colony algorithm. This algorithm improves the initialization mechanism of artificial bee colony algorithm and neighborhood search mechanism of employed bees and onlooker bees. Simulation results show the effectiveness of the algorithm.