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Cheol Min Joo,Byung Soo Kim 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.1
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.
주철민,김병수 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.1
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.
Joo, Cheol-Min,Kim, Byung-Soo Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.1
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.
이종 병렬설비 공정의 납기지연시간 최소화를 위한 유전 알고리즘
최유준(Yu Jun Choi) 한국산업경영시스템학회 2015 한국산업경영시스템학회지 Vol.38 No.1
This paper considers a parallel-machine scheduling problem with dedicated and common processing machines using GA (Genetic Algorithm). Non-identical setup times, processing times and order lot size are assumed for each machine. The GA is proposed to minimize the total-tardiness objective measure. In this paper, heuristic algorithms including EDD (Earliest Due-Date), SPT (Shortest Processing Time) and LPT (Longest Processing Time) are compared with GA. The effectiveness and suitability of the GA are derived and tested through computational experiments.
작업자 1명이 셋업을 수행하는 이종병렬기계에서 조기 및 지연 완료를 최소화하는 생산일정계획
김정배,정진아,고시근 한국경영공학회 2022 한국경영공학회지 Vol.27 No.4
Purpose This research deals with a scheduling problem that minimizes weighted sum of earliness and tardiness penalties in a single setup-operator and non-identical parallel machine system. Methods We first present a mixed integer linear programming formulation for the problem, and then, show the optimal solutions for relatively small problems can be easily found with the model and a commercial optimization software. However, since the problem is NP-hard and the size of a real problem can be large, we propose three heuristic algorithms including genetic algorithm to solve the practical big-size problems in a reasonable computational time. Results Through the computational experiments we found the heuristic algorithms show very good performances for the practical big-size problems. Conclusion The heuristic procedures proposed in this study can easily be used in the real situations.
작업자 1명이 준비작업을 담당하는 이종병렬기계의 휴리스틱 일정계획
고시근 ( Koh Shiegheun ),김상운 ( Kim Sangwoun ),손준석 ( Sohn Junseok ) 한국경영공학회 2017 한국경영공학회지 Vol.22 No.3
This research deals with a scheduling problem that minimizes makespan in a single setup-operator and non-identical parallel machine system with machine dependent setup and processing times. We first present a mixed integer programming formulation for the problem, and using this formulation, the optimal solutions for small problems can be easily found. However, since the problem is NP-hard and the size of a real problem is large, we propose three genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found some heuristic algorithms show very good performances.
작업순서 및 기계 의존적인 준비시간을 고려한 제약이 주어진 이종병렬기계의 일정계획에 관한 연구
주철민 ( Cheol Min Joo ) 한국경영공학회 2012 한국경영공학회지 Vol.17 No.3
This paper considers a restricted 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 machines to minimize the 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-heuristic algorithms based on ant colony system and genetic algorithm are proposed. The performance of the proposed algorithms are evaluated using randomly generated several examples.
작업자 1명이 내부 및 외부 셋업을 수행하는 이종병렬기계의 일정계획
고시근,손준석 한국경영공학회 2019 한국경영공학회지 Vol.24 No.1
This research deals with a scheduling problem that minimizes makespan in a single setup-operator and non-identical parallel machine system, in which the machine dependent setup time of each job is divided into internal and external setup times. The internal setup refers to those setup actions that inevitably require that the machine be stopped, and the external setup refers to actions that can be taken while the machine is operating. We first present a mixed integer linear programming formulation for the problem, and using this formulation, the optimal solutions for relatively small problems can be easily found. However, since the problem is NP-hard and the size of a real problem can be large, we propose a genetic algorithm based heuristic to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithm, we conduct a computational experiment, from which we found the heuristic algorithm shows very good performances.