Wen-Hung Kuo

Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect

In this study, we introduce a time-dependent learning effect into a single-machine scheduling problem. The time-dependent learning effect of a job is assumed to be a function of total normal processing time of jobs scheduled in front of it. We introduce it into a single-machine scheduling problem and we show that it remains polynomially solvable for the objective, i.e., minimizing the total completion time on a single machine. Moreover, we show that the SPT-sequence is the optimal sequence in this problem.

Single-machine group scheduling with a time-dependent learning effect

In many realistic situations, the more time you practice, the better learning effect you obtain. Thus, we propose a time-dependent learning effect and introduce it into the single-machine group scheduling problems. The two objectives of scheduling problems are to minimize the makespan and the total completion time, respectively. We also provide two polynomial time algorithms to solve these problems.

Minimizing the makespan in a single-machine scheduling problem with the cyclic process of an aging effect

In this paper, we study a single-machine scheduling problem with the cyclic process of an aging effect. This phenomenon appears in many realistic production processes. Thus, it is important to consider the phenomenon in scheduling problems. We analyse the single-machine makespan scheduling problem with two different aging effect models and provide a polynomial time algorithm to solve the problem.

Single machine scheduling with past-sequence-dependent setup times and learning effects

This paper studies a single machine scheduling problem with setup times and learning considerations. The setup times are proportional to the length of the already scheduled jobs. That is, the setup times are past-sequence-dependent. It is assumed that the learning process reflects a decrease in the process time as a function of the number of repetitions, i.e., as a function of the job position in the sequence. The following objectives are considered: the makespan, the total completion time, the total absolute differences in completion times and the sum of earliness, tardiness and common due-date penalty. Polynomial time algorithms are proposed to optimally solve the above objective functions.

Some scheduling problems with deteriorating jobs and learning effects

This paper considers some scheduling problems with deteriorating jobs and learning effects. The following objective functions are considered: the makespan, the total completion times, and the total absolute differences in completion times. Several polynomial time algorithms are proposed to optimally solve the single-machine scheduling problems. Finally, we show that several special cases of the flowshop scheduling problems remain polynomially solvable under the proposed model.

Single-machine scheduling with both deterioration and learning effects

This paper considers a single-machine scheduling problem with both deterioration and learning effects. The objectives are to respectively minimize the makespan, the total completion times, the sum of weighted completion times, the sum of the kth power of the job completion times, the maximum lateness, the total absolute differences in completion times and the sum of earliness, tardiness and common due-date penalties. Several polynomial time algorithms are proposed to optimally solve the problem with the above objectives.

Multi-family scheduling in a two-machine reentrant flow shop with setups

Abstract This paper considers a two-machine multi-family scheduling problem with reentrant production flows. The problem consists of two machines, M1 and M2, and each job has the processing route (M1, M2, M1, M2). There are identical jobs in the same family and the jobs in the same family are processed in succession. Each machine needs a setup time before the first job in a family is processed. The objective is to minimize the maximum completion time. Examples of such a problem occur in the bridge construction, semiconductor industry and job processing on numerical controlled machines, where they usually require that the jobs are reprocessed once and there are identical jobs in the same family. This problem is shown to be NP-hard. A branch-and-bound algorithm is proposed, and computational experiments are provided.

Scheduling with a position-weighted learning effect based on sum-of-logarithm-processing-times and job position

Scheduling research has increasingly taken the concept of learning into consideration. In general, a workers learning effect on a job depends not only on the total processing time of the jobs that he has processed but also on the jobs position. Besides, in the early stage of processing a given set of jobs, the worker is not familiar with the operations, so the learning effect on the jobs scheduled early is not apparent. Based on the above observations, we introduce in this paper a position-weighted learning effect model based on sum-of-logarithm-processing-times and job position for scheduling problems. We provide optimal solutions for the single-machine problems to minimize the makespan and the total completion time, and for the single-machine problem to minimize the sum of weighted completion times, the maximum lateness, and the total tardiness under an agreeable situation. We also solve two special cases of the flowshop problem under the learning model.

A two-machine flowshop scheduling problem with a separated maintenance constraint

This paper considers a two-machine flowshop scheduling problem with a separated maintenance constraint. This means that the machine may not always be available during the scheduling period. It needs a constant time to maintain the machine after completing a fixed number of jobs at most. The objective is to find the optimal job combinations and the optimal job schedule such that the makespan is minimized. The proposed problem has some practical applications, for example, in electroplating process, the electrolytic cell needs to be cleaned and made up a deficiency of medicine. In this paper, we propose a heuristic algorithm to solve this problem. Some polynomially solvable cases and computational experiments are also provided.

Worst-case and numerical analysis of heuristic algorithms for flowshop scheduling problems with a time-dependent learning effect

In this paper, we investigate a time-dependent learning effect in a flowshop scheduling problem. We assume that the time-dependent learning effect of a job was a function of the total normal processing time of jobs scheduled before the job. The following objective functions are explored: the makespan, the total flowtime, the sum of weighted completion times, the sum of the kth power of completion times, and the maximum lateness. Some heuristic algorithms with worst-case analysis for the objective functions are given. Moreover, a polynomial algorithm is proposed for the special case with identical processing time on each machine and that with an increasing series of dominating machines, respectively. Finally, the computational results to evaluate the performance of the heuristics are provided.