Dar-Li Yang

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.

Single-machine due-window assignment and scheduling with job-dependent aging effects and deteriorating maintenance

Due-window assignment and scheduling are important issues in modern manufacturing management. In this note we consider single-machine due-window assignment and scheduling with job-dependent aging effects and deteriorating maintenance. The objective is to find jointly the optimal time to perform maintenance, the optimal location and size of the due-window, and the optimal job sequence to minimize the total earliness, tardiness, and due-window related costs. We show that the problem can be optimally solved in O(n^4) time. We also show that a special case of the problem can be optimally solved by a lower order algorithm.

Minimizing the total completion time in single-machine scheduling with aging/deteriorating effects and deteriorating maintenance activities

In a real manufacturing system, a machine may need multiple maintenance activities to improve its production efficiency due to the effects of aging or deteriorating. This paper considers scheduling with aging or deteriorating effects and deteriorating maintenance activities simultaneously on a single machine. We assume that the machine may be subject to several maintenance activities during the planning horizon. However, due to the restriction of budget of maintenance, the upper bound of the maintenance frequency on the machine is assumed to be known in advance. Moreover, we assume that the duration of each maintenance activity depends on the running time of the machine. The objective is to find jointly the optimal maintenance frequencies, the optimal maintenance positions, and the optimal job sequence for minimizing the total completion time. We show that all the problems studied are polynomially solvable.

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.

A two-machine flowshop sequencing problem with limited waiting time constraints

Abstract We consider a two-machine flowshop sequencing problem with limited waiting time constraints. This means that for each job the waiting time between two machines cannot be greater than a given upper bound. The objective is to minimize the makespan. There are efficient algorithms for the special cases of infinite waiting time and zero waiting time. The two-machine flowshop sequencing problem with limited waiting time constraints is shown to be NP-hard. A branch-and-bound algorithm is proposed, and computational experiments are provided.

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.

Two-machine flowshop group scheduling problem

Abstract This paper considers a two-machine flowshop group scheduling problem. The jobs are classified into groups and the jobs in the same group must be processed in succession. Each group requires a set up time and removal time on both machines. A transportation time is required for moving the jobs from machine 1 to machine 2. The objective is to minimize the maximum completion time (makespan). A polynomial time algorithm is proposed to solve the problem. This generalizes the algorithms proposed by Baker and others. Scope and purpose Recently, an important class of scheduling problem is characterized by a group scheduling constraint where the jobs are classified into groups by the same operation requirements or characters. Each group requires a setup time and removal time on both machines. That is, each machine needs a time to set up or to remove the tools, jigs and fixtures when the group starts processing or completes processing. A transportation time is required to move the jobs between the machines. The objective is to find a sequence of groups and jobs in each group such that the maximum completion time (makespan) is minimized. Baker provided an optimal algorithm for this problem in the case of two-machine flowshop group scheduling without considering the removal and transportation times. But, in some manufacturing environments, it is required to consider the group removal time and job transportation time. The main contribution of this paper is to develop a polynomial time algorithm, which generalizes the algorithms proposed by Baker and others.

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 group scheduling problems under the effects of deterioration and learning

This paper investigates single-machine group scheduling problems with simultaneous considerations of deteriorating and learning effects to minimize the makespan and the total completion time of all jobs. The group setup time is assumed to follow a simple linear time-dependent deteriorating model. Two models of learning for the job processing time are examined in this study. We provided polynomial time solutions for the makespan minimization problems. We also showed that the total completion time minimization problems remain polynomially solvable under agreeable conditions.