Single-machine Scheduling Problems With Learning Effects In Intermittent Batch Production

Recently scheduling with learning effect has received considerable attention in the literature since Biskup introduced learning effects into scheduling field in 1999. So it is an active branch of scheduling with the extensive application prospect. In this dissertation, we consider three kinds of learning effect models in intermittent batch production of single-machine scheduling problems. The structure of this paper is organized as follows.In the first chapter, some notations and definitions of scheduling problems are introduced. Then, we summarize the main results and innovation of this paper.In the second chapter, we consider the single-machine scheduling problems with Dejong's learning and forgetting effects in intermittent batch production. The objectives are to minimize the makespan and the total completion time, respectively. We discuss three models of no transmission, partial transmission and total transmission of the learning effect from batch to batch according to how long the time has elapsed between production runs. We indicate that the problems for the models of no transmission and partial transmission of learning effect from batch to batch are polynomially solvable. Further, we provide some polynomial time algorithms for some special cases of the problem with the total transmission models.'In the third and fourth chapter, we introduce the time-dependent learning effect and General learning effect into single machine scheduling problems in in-termittent batch production. The objectives are to minimize the makespan and the total completion time, respectively. We consider two models of no trans-mission and partial transmission of learning effects from batch to batch. We also provide some polynomial time algorithms to solve these problems, and prove these algorithms are optimally.