• 한국컴퓨터정보학회논문지
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• 제22권10호
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• pp.101-107
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• 2017

In this paper, we study the problem of parallel machine scheduling considering the moving time of multiple servers. The parallel machine scheduling is to assign jobs to parallel machines so that the total completion time(makespan) is minimized. Each job has a setup phase, a processing phase and a removal phase. 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. A server is needed to move to a parallel machine for a setup phase and a removal phase. But previous researches have been done under the assumption that the server moving time is zero. In this study we have proposed an efficient algorithm for the problem of parallel machine scheduling considering multiple server moving time. We also have investigated experimentally how the number of servers and the server moving time affect the total completion time.

• 대한안전경영과학회지
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• 제25권2호
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.114-122
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• 2012

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

• 산업공학
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• 제25권3호
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• pp.357-364
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• 2012

This paper considers a unrelated parallel machine scheduling problem with ready times, due times and sequence and machine-dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of machines to minimize the total tardy time. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, a genetic algorithm using an effective dispatching method is proposed. The performance of the proposed genetic algorithm is evaluated using several randomly generated examples.

• 한국컴퓨터정보학회논문지
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• 제19권6호
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• pp.101-108
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• 2014

병렬 머신 스케줄링은 주어진 작업들의 총 완료 시간이 최소가 되도록 작업들을 병렬 머신들에 할당하는 문제로 강철 산업, 반도체 제조, 플라스틱 산업 등 다양한 제조 시스템 분야에서 활용되고 있다. 각 작업들은 준비 과정과 처리 과정을 거치게 되는데, 응용 분야에 따라 제거 과정이 필요한 경우도 있다. 이 중 처리 과정은 병렬 머신만 사용되는데 비해, 준비 과정이나 제거 과정은 서버와 병렬 머신이 동시에 사용된다. 기존의 연구들은 단일 서버를 사용하거나 준비 과정과 처리 과정만을 고려하는 연구가 대부분인데, 단일 서버를 사용하는 경우에는 서버에 병목 현상이 발생하게 되어 총 완료 시간이 늦어지게 되고, 병렬 머신의 수를 증가시키더라도 총 완료 시간은 별로 향상되지 않는 단점을 가지게 된다. 본 연구에서는 다중 서버를 사용하고 준비 과정, 처리 과정, 제거 과정을 모두 고려하는 병렬 머신 스케줄링 알고리즘을 제안하고, 서버의 수와 병렬 머신의 수가 총 완료 시간에 어떤 영향을 미치는지 실험을 통해 분석하였다.

• 산업경영시스템학회지
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• 제29권2호
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• pp.7-12
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• 2006

This study considers the problem of scheduling jobs on uniform parallel machines with a common due date. The objective is to minimize the total absolute deviation of job completion times about the common due date. This problem is motivated by the fact that a certain phase of printed circuit board manufacturing is bottleneck and the processing speeds of parallel machines in this phase are uniformly different for all jobs. Optimal properties are proved and a simple polynomial time optimal algorithm is developed.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.1-8
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• 2021

This study proposes a systems engineering approach for the development of an advanced planning & scheduling (APS) system for a cosmetic case manufacturing factory. The APS system makes production plans and schedules based on the injection process, which consists of 27 plastic injection machines in parallel to control recommended inventory of products. The system uses machine operation/failure information and defective product/work-in-process tracking information to support intelligent scheduling. Furthermore, a genetic algorithm model is applied to handle the complexity of heuristic rules and machine/quality constraints in this process. As a result of the development, the recommended inventory compliance rate is improved by scheduling the 30-day production plan for 15 main products.

• 산업공학
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• 제12권4호
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• pp.617-624
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• 1999

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider a scheduling problem for assembly machine in ship engine assembly shop. This paper considers the parallel machine scheduling problem in which n jobs having different release times, due dates and space limits are to be scheduled on m parallel machines. The objective function is to minimize the sum of earliness and tardiness. To solve this problem, a heuristic is developed. The proposed heuristic is divided into three modules hierarchically: job selection, machine selection and job sequencing, solution improvement. To illustrate its effectiveness, a proposed heuristic is evaluated with a large number of randomly generated test problems based on the field situation. Through the computational experiment, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic.

• Management Science and Financial Engineering
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• 제11권2호
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• pp.19-43
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• 2005

This paper considers a variation of the customer order scheduling problem, and the variation is the case where the machine-job assignment is fixed. We examine the parallel machine environment, and the objective is to minimize the sum of the completion times of the batches. While a machine can process only one job at a time, different machines can simultaneously process different jobs in a batch. The recognition version of this problem is known to be NP-complete in the strong sense even if there exist only two parallel machines. When there are an arbitrary number of parallel machines, we establish three lower bounds and develop a dynamic programming (DP) algorithm which runs in exponential time on the number of batches. We present two simple but intuitive heuristics, SB and GR, and find some special cases where SB and GR generate an optimal schedule. We also find worst case upper bounds on the relative error. For the case of the two parallel machines, we show that GR generates an optimal schedule when processing times of all batches are equal. Finally, the heuristics and the lower bounds are empirically evaluated.

• 경영과학
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• 제12권3호
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• pp.177-185
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• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.