Scheduling In Group Technology And Online Supply Chain Problem

As an important class of combinatorial optimization problem, scheduling problem has a profound practical background and broad application prospects. It is widely used in management science, computer systems, industrial and agri-cultural production, transportation and so on. For the present, it has become a very active branch of operational research, which has attracted many domestic and international academic scholars'attention. Because scheduling in group tech-nology and online supply chain problems have obvious practical applications, they have high value for research. In this paper, our study is based on the above two models. The paper is divided into three chapters, and the structure is arranged as follows:In chapter 1, the application background of scheduling are introduced, then an overview of the problem as well as its current research situation and prospect are involved. At last, a brief introduction of the main results and innovative point of the article are summarized.In chapter 2, we mainly discuss both the online version and the offline ver-sion of scheduling problems in group technology. For the online version, the objective function is the sum of the total completion times. In this chapter, the corresponding online algorithm is given. As for the offline version, the objective is to minimize the total cost of the tardiness penalty (or to maximize the total earliness award). At present, the general version of the problem has been proved to be NP-hard, so we discusse several special cases, give corresponding optimal algorithms, and analyse the computational complexities of these algorithms.In chapter 3, we consider the online integrated production-distribution schedul-ing problem, and the objective is to minimize the sum of the maximum lateness and the total delivery cost. It is a new class of scheduling problem that combines the traditonal scheduling problem and contemporary logistics. In this chapter, we take advantage of the previous work, and add some new conditions that are more suitable to actual circumstances. Finally, reasonable online algorithms are given.