Research On Integrated Scheduling Of Production And Distribution In Distributed Flow Shops

With the development of economic globalization and information technology,supply chain management problems have become a hot topic in the fields of industrial and operations research.To quickly respond to the dynamic changes in the market environment,enterprises continue to improve their core competitiveness and pursue higher overall supply chain benefits,thus supply chain management is facing great challenges.Production and distribution are two essential components of the supply chain,and efficient production scheduling and reasonable distribution decisions can directly influence the overall efficiency and competitiveness of supply chains.Since most of activities in the supply chain are interdependent,we need to view the whole supply chain with systematic and overall thinking,so it is necessary to manage and organize the production and distribution activities by using an integrated approach.In addition,influenced by the economic globalization,manufacturing enterprises employ distributed production systems for production and manufacturing,and gradually extend the production structure with single factory to multi-factory distributed manufacturing structures.Distributed manufacturing can take full advantage of the equipment and technology from multiple factories to quickly reach the production of products at an acceptable cost through reasonable allocation,combination and sharing of limited resources.In recent years,distributed flow shop scheduling in the distributed manufacturing context has been extensively researched.It aims at optimizing the given objectives by considering job allocation among factories and job processing sequence on machines.Through effectively scheduling distributed flow shops,and employing suitable work divisions as well as cooperation,it is beneficial for enterprises to produce high-quality products at low cost and low risk.Hence,this work studies an integrated production and distribution scheduling problem in distributed flow shops,and its contents mainly include the following three aspects:(1)This work presents an integrated production and distribution scheduling problem in distributed flow shops considering the time criterion,and builds a mixed integer mathematical programming model to minimize the maximum delivery time.Considering the NP-hard nature of the presented problem,an enhanced black widow optimization algorithm is designed to address it.To test the validity of the proposed method,three well-known swarm intelligence methods are chosen to carry out numerical experiments on a set of test instances with different scales.The experimental results demonstrate that the proposed algorithm can get superior performance than its peers in solving the problem under consideration.(2)This work considers an integrated production and distribution scheduling problem in distributed flow shops considering the customer satisfaction.In order to clearly describe the considered problem,this work establishes a mixed integer mathematical programming model with the objective of minimizing the total weighted earliness and tardiness penalty.In view of the characteristics of the problem studied,an enhanced brain storm optimization is presented as an optimizer.To verify the validity of the proposed method,this work selects five famous swarm intelligence methods as a comparison pool to perform numerical experiments on different scale test instances.The experimental results indicate that the proposed method has outstanding performance in handling the considered problem.(3)This work studies a multi-objective integrated production and distribution scheduling problem in distributed flow shops considering both time and customer satisfaction criteria.The production stage aims at minimizing the makespan,and the goal of the distribution stage is to minimize the total weighted earliness and tardiness penalty.In order to describe the considered problem,this work formulates a mixed integer mathematical programming model.Considering that the proposed problem is multi-objective and NP-hard,this work designs a multi-objective brain storm optimization algorithm to cope with it.In this research,three classical multi-objective evolutionary algorithms are employed to conduct numerical experiments on several test instances with different scales.The experimental results verify that the presented algorithm has excellent performance in handling the studied problem.