• Korean Management Science Review
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• v.31 no.2
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• pp.65-70
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• 2014

This paper considers a single machine scheduling problem to determine release and processing times where both the release times and processing times are linearly decreasing functions of resources. The objective is to minimize the sum of the associated resource consumption cost and scheduling cost including makespan, sum of completion times, maximum lateness, or sum of lateness. This paper proves that the scheduling problem is NP-hard in the strong sense even if the release times are constant.

• Journal of the Korean Operations Research and Management Science Society
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• v.4 no.1
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• pp.69-77
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• 1979

A single machine stochastic scheduling problem is considered. Associated with each job is its random processing time and random deferral cost. The criterion is to order the jobs so as to minimize the sum of the deferral costs. The expected sum of the deferral costs is theroretically derived under the stochastic situation for each of several scheduling decision rules which are well known for the deterministic environment. It is also shown that certain stochastic problems can be reduced to equivalent deterministic problems. Two examples are illustrated to show the expected total deferral costs.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.158-168
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• 2001

We present a tabu search (TS) algorithm to minimize maximum lateness on a single machine in the presence of sequence dependent setup times and dynamic job arrivals. The TS algorithm starts with a feasible schedule generated by a modified ATCS (Apparent Tardiness Cost with Setups) rule, then through a series of search steps it improves the initial schedule. Results of extensive computational experiments show that the TS algorithm significantly outperforms a well-known RHP heuristic by Ovacik and Uzsoy, both on the solutions quality and the computation time. The performance advantage is particularly pronounced when there is high competition among jobs for machine capacity.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.101-108
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• 2014

The parallel machine scheduling is to schedule each job to exactly one parallel machine so that the total completion time is minimized. It is used in various manufacturing system areas such as steel industries, semiconductor manufacturing and plastic industries. Each job has a setup phase and a processing phase. A removal phase is needed in some application areas. A processing phase is performed by a parallel machine alone while a setup phase and a removal phase are performed by both a server and a parallel machine simultaneously. Most of previous researches used a single server and considered only a setup phase and a processing phase. If a single server is used for scheduling, the bottleneck in the server increases the total completion time. Even though the number of parallel machines is increased, the total completion time is not reduced significantly. In this paper, we have proposed an efficient algorithm for the parallel machine scheduling using multiple servers and considering setup, processing and removal phases. We also have investigated experimentally how the number of servers and the number of parallel machines affect the total completion time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

• Korean Management Science Review
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• v.30 no.1
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• pp.15-24
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• 2013

We consider the single machine scheduling problem with a rate-modifying activity and time-dependent deterioration after the activity. The class of scheduling problems with rate-modifying activities and the class of scheduling problems with time-dependent processing times have been studied independently. However, the integration of these classes is motivated by human operators of tasks who has fatigue while carrying out the operation of a series of tasks. This situation is also applicable to machines that experience performance degradation over time due to mal-position or mal-alignment of jobs, abrasion of tools, and scraps of operations, etc. In this study, the integration of the two classes of scheduling problems is considered. We present a mathematical model to determine job-sequence and a position of a rate-modifying activity for the integration problem. Since the model is difficult to solve as the size of real problem being very large, we propose genetic algorithms. The performance of the algorithms are compared with optimal solutions with various problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.306-309
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• 1996

The job shop scheduling problem has been a major target for many researchers. And, most of the past studies did not consider setup time. In many cases of real manufacturing environment, however, there exists a setup time for each operations. The setup can be divide into two parts, one can be done after job arrival. The setup time based on the latter can be summed together with processing time, but that based on the former can not be. We propose an approximation method based on shifting bottleneck procedure for solving the job shop scheduling problem with sequence independent setup time. It schedules the machines one by one, taking a bottleneck machine among the machines not yet scheduled. Every time after a new machine is scheduled, all schedules previously established are updated. Both the bottlenck search and the schedule updating procedure are based on solving a single machine scheduling problem with ready time, setup time and delivery time iteratively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.153-158
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• 2010

This study presents a single machine scheduling algorithm to minimize total cost(lateness cost, earliness cost and failure cost) by controlling machining speed. Generally, production scheduling uses the information of process planning and machining speed is not changed at production scheduling. And failure cost is not consider for scheduling algorithm. Therefore, the purpose of this study is to consider the change of machining speed for efficient production scheduling. And performance criteria for algorithm considers total cost. Especially, failure cost of product by increasing machining speed is considered.

• Korean Management Science Review
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• v.30 no.2
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• pp.133-141
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• 2013

In this paper, we considers the outsourcing decision problem in a single machine scheduling problem. The decision problem is to determine for each job whether to be processed on an in-house manufacturing or external facilities(outsourcing). Moreover, this paper considers a situation where each job has a due date. The objective of the problem is to minimize the outsourcing cost, subject to the due date constraints. The considered problem is proved to be NP-hard. Some solution properties and valid inequalities are derived, and an effective lower bound is derived based on the LP-relaxation. The results of experimental tests are presented to evaluate the performance of the suggested lower bound.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.197-205
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• 1997

Manufacturers can meet the due dates of orders by using overtime, in which case, additional cost incurs for the amount of overtime. Although many studies have been reported on scheduling, only a few papers are founds on production scheduling using overtime. We consider the problem of production scheduling using overtime on a single machine, in which each job has a given due-date, a constant processing time. We assume that the daily overtime does not exceed the daily regular operation time. The objectives is to minimize the total overtimes, while meeting all due dates. We first present a mathematical model, and then suggest a heuristic algorithm for the problem.