• Title/Summary/Keyword: Parallel-Machine Scheduling

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Parallel Machine Scheduling Considering the Moving Time of Multiple Servers

  • Chong, Kyun-Rak
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.10
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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.

A Study on Memetic Algorithm-Based Scheduling for Minimizing Makespan in Unrelated Parallel Machines without Setup Time (작업준비시간이 없는 이종 병렬설비에서 총 소요 시간 최소화를 위한 미미틱 알고리즘 기반 일정계획에 관한 연구)

  • Tehie Lee;Woo-Sik Yoo
    • Journal of the Korea Safety Management & Science
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    • v.25 no.2
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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.

Non-Identical Parallel Machine Scheduling with Sequence and Machine Dependent Setup Times Using Meta-Heuristic Algorithms

  • Joo, Cheol-Min;Kim, Byung-Soo
    • Industrial Engineering and Management Systems
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    • v.11 no.1
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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.

Genetic Algorithm with an Effective Dispatching Method for Unrelated Parallel Machine Scheduling with Sequence Dependent and Machine Dependent Setup Times (작업순서와 기계 의존적인 작업준비시간을 고려한 이종병렬기계의 일정계획을 위한 효과적인 작업할당 방법을 이용한 유전알고리즘)

  • Joo, Cheol-Min;Kim, Byung-Soo
    • IE interfaces
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    • v.25 no.3
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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.

An efficient algorithm for scheduling parallel machines with multiple servers (다중 서버를 사용하는 병렬 머신 스케줄링을 위한 효율적인 알고리즘)

  • Chong, Kyun-Rak
    • 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.

Uniform Parallel Machine Scheduling (병렬기계에서의 스케쥴링에 관한 연구)

  • Kim, Dae-Cheol
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.29 no.2
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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.

Systems Engineering Approach to Develop Intelligent Production Planning Scheduling Model linked to Machine and Quality Data (설비 및 품질 데이터 연계 지능형 생산계획 스케줄링 모델 개발을 위한 시스템엔지니어링 접근 방법)

  • Park, Jong Hee;Kim, Jin Young;Hong, Dae Geun
    • Journal of the Korean Society of Systems Engineering
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    • v.17 no.2
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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.

A Heuristic for Efficient Scheduling of Ship Engine Assembly Shop with Space Limit (공간제약을 갖는 선박용 엔진 조립공장의 효율적인 일정계획을 위한 발견적 기법)

  • Lee, Dong-Hyun;Lee, Kyung-Keun;Kim, Jae-Gyun;Park, Chang-Kwon;Jang, Gil-Sang
    • IE interfaces
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    • v.12 no.4
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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.

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Customer Order Scheduling Problems with a Fixed Machine-Job Assignment

  • Yang, Jae-Hwan;Rho, Yoo-Mi
    • Management Science and Financial Engineering
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    • v.11 no.2
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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.

Tabu Search methods to minimize the number of tardy jobs in nonidentical parallel machine scheduling problem (동일하지 않는 병렬기계 시스템에서 지연작업수를 최소화하는 Tabu Search 방법)

  • 전태웅;강맹규
    • Korean Management Science Review
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    • v.12 no.3
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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.

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