Research On Integrated Scheduling Problem Of Production And Distribution With Uncertain Factors

With the increasing globalization of trade and the improvement of technology,market competition has also intensified.This means that manufacturing companies must provide better services to win more customers and achieve higher profits.Production,inventory and distribution are the three basic modules of manufacturing.Most scholars study these three issues separately.However,integrating production,inventory,and distribution issues can effectively improve overall benefits and achieve a highly efficient,low-cost supply chain.At present,due to the increase in costs and the prolonged circulation of products caused by high inventory,more and more companies are adopting the business model of Make-to-order(MTO),with little or no finished products inventory in actual operation,making production and distribution plans very closely linked,and must be coordinated to achieve the desired on-time delivery performance with minimal total cost.In the actual distribution process,due to weather,traffic congestion and other factors,the driving time of the vehicle is uncertain.Therefore,this paper studies integrated production and multi trip vehicle routing problem under the uncertain travel times.This paper first analyzes the research significance of this problem,and combs the domestic and foreign research status of integration of production and distribution under uncertain factors and the heuristic algorithm to solve the problem,which lays the foundation for the subsequent research.The first phase considers scheduling a set of jobs on parallel machines with machine-dependent ready times,and the second phase focuses on the delivery of completed jobs by a fleet of identical vehicles,which may differ in their ready times.In addition,each job has a specific delivery time windows and the travel times in distribution are uncertain.The objective is to minimize the total cost comprising the travel cost and penalty cost caused by tardiness.In order to solve the uncertainty of the problem,we present a robustness approach,known as "Elastic p-Robustness",to deal with travel time variations when historical risk data are limited or non-existent,and develop a memetic algorithm with an effective search strategy to solve the problem.We randomly generate data based on real experience and conduct numerical studies on it to assess the effectiveness and efficiency of the proposed method.The computational results show that the proposed solution approach yields relatively good solutions in comparison with current mainstream heuristic algorithms.