• Korean Management Science Review
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• v.24 no.2
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• pp.33-42
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• 2007

In this paper we consider an alternate m machine scheduling problem in which each job having at most two eligible machines should be assigned with the objective of makespan minimization. For this problem. we propose a $O(m2^m)$ time rounding algorithm with performance ratio at most 1.5. For a little general problem where each job can be processed in at most three machines, we prove that a polynomial time algorithm does not exist with performance ratio less than 1.5.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.25-32
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• 1998

A modified simulated annealing algorithm is proposed and applied to the permutation flowshop scheduling with the makespan objective. Based on the job deletion and insertion method, a newly defined Max-min perturbation scheme is proposed to obtain a better candidate solution in the simulated annealing process. The simulation experiments are conducted to evaluate the effectiveness of the proposed algorithm against the existing heuristics and results are reported.

• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.165-177
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• 2003

크로스도킹이란 창고나 물류센터에 하역된 물품이 저장됨이 없이 도착지별로 재분류되어서 직출하되는 물류 시스템이다. 크로스도킹은 물류비용의 큰 비중을 차지하는 보관비용을 감소시킬 수 있을 뿐만 아니라 고객의 요구에 빠른 대응을 할 수 있다는 장점을 지니고 있다. 크로스도킹이 성공적으로 수행되기 위해서는 창고나 물류센터의 입고에서부터 출고까지의 모든 작업들이 계획적이고 원활하게 수행되어져야만 한다. 본 연구에서는 임시보관 장소를 보유하지 않은 크로스도킹 시스템의 총 운영시간을 최소화하기 위한 입고 트럭과 출고 트럭의 일정계획 수립을 위한 수학적 모델을 개발하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.190-196
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• 2003

We consider the problem of scheduling n jobs on m machines with the objective of minimizing makespan. Each job cannot be eligible to all the machines but to its consecutively eligible set of machines. For this problem, a 2-approximation algorithm, MFFH, is developed. In addition, an example is presented to show the tightness of the algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.607-617
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• 1996

This paper shows that fuzzy set theory can be useful in modeling and solving job scheduling problems with uncertain processing times. The processing times are considered as fuzzy numbers(fuzzy intervals or time intervals) and the fuzzy Delphi method is used to estimate a reliable time interval of each processing time. Based on these time estimates, we then propose an efficient methodology for calculating the optimal sequence and the fuzzy makespan.

• Journal of Industrial Convergence
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• v.5 no.2
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• pp.21-32
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• 2007

This paper deals with the sequencing problem in the operation of the manufacturing systems with the constraint of buffer capacity. Some of studies for this theme have been progressed for several years. And then most of them considered only one objective, such as maximum lateness, machine utilization, makespan, mean flowtime and so on. This study deal with two objectives of the delivery for customers and the idle time of machines for producers. For the decision of sequence, the utility function is used. The developed heuristic algorithm presents a good solution. Through a numerical example, the procedures of the job sequencing is explained.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.77-88
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• 2015

In this paper, we consider a two-agent single-machine scheduling problem with exponential job-dependent position-based learning effects. The objective is to minimize the total weighted completion time of one agent with the restriction that the makespan of the other agent cannot exceed an upper bound. First, we propose a branch-and-bound algorithm by developing some dominance /feasibility properties and a lower bound to find an optimal solution. Second, we design an efficient simulated annealing (SA) algorithm to search a near optimal solution by considering six different SAs to generate initial solutions. We show the performance superiority of the suggested SA using a numerical experiment. Specifically, we verify that there is no significant difference in the performance of %errors between different considered SAs using the paired t-test. Furthermore, we testify that random generation method is better than the others for agent A, whereas the initial solution method for agent B did not affect the performance of %errors.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.270-273
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• 2017

Productivity through reduced defects in plastic injection molding and reduced cycle times is a long-standing need in the injection industry. In particular, productivity is very urgent for the domestic injection industry, which is caught between the pursuit of latecomers such as China and technological gap with Germany and Japan which will not be narrowed down. Through 30 years of research and experience in the domestic injection industry, we have found that controlling the surface temperature of injection molds is the key of quality control. There have been various attempts to utilize advanced control techniques such as PID control, but the productivity against leading companies in Germany and Japan is still insufficient. Using Approximation Algorithm - "Knapsack" and "Minimum Makespan Scheduling", We want to show how to efficiently control objects with periodic repetitive data patterns that are difficult to solve with PID control. In addition, We want to propose that the control by Approximation Algorithm is effective enough to improve the productivity of the product by analyzing the data extracted from actual injection site.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.132-141
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• 2015

We focus on the fire scheduling problem (FSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing makespan to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We develop several dominance properties and a lower bound for the problem, and suggest a branch and bound algorithm implementing them. Also, In addition, heuristic algorithms that can be used for obtaining an initial upper bound in the B&B algorithm and for obtaining good solutions in a short time were developed. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithm solves problems of a medium size in a reasonable amount of computation time. The proposed lower bound, the dominance properties, and the heuristics for upper bound are tested in B&B respectively, and the result showed that lower bound is effective to fathoming nodes and the dominance properties and heuristics also worked well. Also, it is showed that the CPU time required by this algorithm increases rapidly as the problem size increases. Therefore, the suggested B&B algorithm would be limited to solve large size problems. However, the employed heuristic algorithms can be effectively used in the B&B algorithm and can give good solutions for large problems within a few seconds.