Research On Multi - Customer Supply Chain Scheduling With Installation Time

Supply chain scheduling is the scheduling theory of application in logistics and supply chain management, and it mainly studies the production scheduling, batch, shipment. Supply chain scheduling with setup times, learning effects and rejection are relatively new supply chain scheduling models, which have attracted great attention of scholars in the field. In this thesis,many new results of the above models on complexities and algorithms are presented respectively,The main works are as follows.In chapter I, we mainly describe the fundamental concepts and preliminaries of scheduling problem, and briefly summarize the research results.In chapter II, we study comprehensively the supply chain scheduling of multiple customers with setup times and learning effects. There is only manufacturer that has single machine, which can process at most one work at the same time. A setup time is required for a job if it is the first job to be processed on the single machine or its processing follows a job that belongs to another customer. There are position-based learning effects when the jobs are processed by the manufacturer for the same customer, which means the actual processing time of the job is decreased when there are some jobs of the same customer processed before the job. The completed jobs of the same customer need to be delivered in batches to their respective customers, and each shipment incurs a corresponding delivery time and transportation cost. The objective is to minimize the sum of customer service level and total transportation cost of the jobs, where customer service level is measured by the maximum delivery time, the total weighted delivery time, the maximum lateness, respectively. We propose polynomial time algorithms for solving these problems, and analyze the computational complexities of these algorithms, respectively.In chapter III, we extend the three models in the second chapter, and consider an integrated production and delivery single-machine scheduling with setup times, learning effects and rejection. Every job is either rejected, in which causes a rejection penalty has to be paid, or accepted and processed on the single machine. The objective is to minimize the sum of the scheduling cost of the accepted jobs and total transportation cost of the accepted jobs, where the scheduling cost is the maximum completion time, the total weighted delivery time, the maximum lateness, respectively. However, the total rejection penalty of the rejected jobs cannot exceed a given upper bound. First, we show that the problems for distinct objective functions are all NP-hard in the ordinary sense. Then, we design pseudo-polynomial time algorithms throughdynamic programming and analyze the computational complexities of these algorithms.In chapter IV, we address the supply chain scheduling model of more manufacturers and multiple customers. The objective is to minimize the total weighted delivery time and total transportation cost of jobs. We design dynamic programming algorithms.In chapter V, the conclusion of this paper is summarized, and the future research is forecasted.