Integrated Scheduling Of Production And Transportation With Multiple Customers Under MTO Mode

With the development of information technology and the specialization of socialdivision, the coordination keeps getting deeper between production and transportation insupply chain management. This trend plays well in the supply chain which applies amake-to-order mode, such as automobile and fashion. On one hand, it is pushed bycustomers’ individual demands; on the other hand, it is due to companies’ active explorationaiming to improve customers’ effective responses. Under this background, in-depth studiesare required to focus on the integrated scheduling of production and transportation withmulti-customer under MTO mode.This thesis began with a literature review concerning coordinated scheduling of supplychain, integrated scheduling of production and transportation and scheduling methods, andthen summarized the existing research results about integrated scheduling systematicallywhile proposing its breakthrough point. Subsequently, a qualitative analysis was adopted tothe integrated scheduling problem of production and transportation with multi-customerunder MTO mode. According to summing up the four characteristics of MTO mode, itanalyzed the constituent elements of production and transportation, and expounded thegeneral model of integrated scheduling and key points in executing that integration. On thisbasis, it probed one integrating scheduling problem regarding one-plant and multi-vehicleserving multi-customer. To minimize the sum of order completion times, this problem wasdecomposed and its main properties within its optimum solution were analyzed followingindicating a special case’s optimum solution. To solve it, a dynamic programming procedurewas developed whose time complexity was O (m2c v+2v c n2mv+m). Finally, instances were givenand compared considering multi-vehicle serving multi-customer, single-vehicle servingmulti-customer and multi-vehicle serving single-customer separately, which illustrated thevalidity of the algorithm above. Hope that the conclusions can provide some effectivetheoretical direction for companies to frame their supply chain coordination plan.