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      KCI등재 SCOPUS

      An Insertion Procedure to Solve Hybrid Multiobjective Permutation Flowshop Scheduling Problems

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      https://www.riss.kr/link?id=A107199679

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      다국어 초록 (Multilingual Abstract)

      This paper present an insertion procedure (IP) which can be used to improve the performance of the multiobjective scheduling problems (MOSPs) algorithms. The proposed procedure use the variable neighborhood search (VNS) combined with insertion method ...

      This paper present an insertion procedure (IP) which can be used to improve the performance of the multiobjective scheduling problems (MOSPs) algorithms. The proposed procedure use the variable neighborhood search (VNS) combined with insertion method which can be adapted with any MOSP whether it is heuristic or metaheuristic. The aim is to solve 2- machine permutation flowshop scheduling problem (PFSP) and minimize the two objective functions simultaneously: Maximum completion time (makespan) and total completion times (ΣjCj ) (TCT) in order to find the efficient (non dominated) solutions. The proposed IP combined with two algorithms in the literature, the non dominated sorting genetic algorithm (NSGA-2) and the multi-objective partial enumeration algorithm (MOPE), the objective is to explore more non dominating solution by the use of insertion concept of jobs through all possible positions of considered sequence. To evaluate the algorithms, large set of test instances involving up to 80 jobs were used for our investigation. The results show the efficiency of the proposed IP to improve the performance of considered MOSPs algorithms.

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      목차 (Table of Contents)

      • ABSTRACT
      • 1. INTRODUCTION
      • 2. LIERATURE REVIEW
      • 3. PROBLEM CONSTRAINS AND FORMULATION
      • 4. PROPOSED ALGORITHMS
      • ABSTRACT
      • 1. INTRODUCTION
      • 2. LIERATURE REVIEW
      • 3. PROBLEM CONSTRAINS AND FORMULATION
      • 4. PROPOSED ALGORITHMS
      • 5. PROPOSED IP
      • 6. PROPOSED ALGORITHMS
      • 7. PERFORMANCE OF THE PROPOSED ALGORITHMS
      • 8. CONCOLUSIONS AND FUTURE WORK
      • REFERENCES
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      참고문헌 (Reference)

      1 Ghasemi, P., "Stochastic optimization model for distribution and evacuation planning(A case study of Tehran earthquake)" 71 : 100745-, 2020

      2 Xu, L., "Solving hybrid flow-shop scheduling based on improved multiobjective artificial bee colony algorithm" 43-47, 2016

      3 Geiger, M. J., "Randomised variable neighbourhood search for multi objective optimisation"

      4 Wang, L., "Process Planning and Scheduling for Distributed Manufacturing" Springer Science & Business Media 2007

      5 Pinedo, M., "Operations Research Proceedings 1991" Springer-Verlag 35-42, 1992

      6 Aldowaisan, T., "New heuristics for m-machine no-wait flowshop to minimize total completion time" 32 (32): 345-352, 2004

      7 Murata, T., "Multiobjective genetic algorithm and its applications to flowshop scheduling" 30 (30): 957-968, 1996

      8 Arroyo, J. E. C., "Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows" 281 : 5-19, 2011

      9 Kan, A. H. G. R., "Machine scheduling problems:classification, complexity and computations" Springer Science & Business Media 2012

      10 El-Mihoub, T. A., "Hybrid genetic algorithms : A review" 13 (13): 124-137, 2006

      1 Ghasemi, P., "Stochastic optimization model for distribution and evacuation planning(A case study of Tehran earthquake)" 71 : 100745-, 2020

      2 Xu, L., "Solving hybrid flow-shop scheduling based on improved multiobjective artificial bee colony algorithm" 43-47, 2016

      3 Geiger, M. J., "Randomised variable neighbourhood search for multi objective optimisation"

      4 Wang, L., "Process Planning and Scheduling for Distributed Manufacturing" Springer Science & Business Media 2007

      5 Pinedo, M., "Operations Research Proceedings 1991" Springer-Verlag 35-42, 1992

      6 Aldowaisan, T., "New heuristics for m-machine no-wait flowshop to minimize total completion time" 32 (32): 345-352, 2004

      7 Murata, T., "Multiobjective genetic algorithm and its applications to flowshop scheduling" 30 (30): 957-968, 1996

      8 Arroyo, J. E. C., "Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows" 281 : 5-19, 2011

      9 Kan, A. H. G. R., "Machine scheduling problems:classification, complexity and computations" Springer Science & Business Media 2012

      10 El-Mihoub, T. A., "Hybrid genetic algorithms : A review" 13 (13): 124-137, 2006

      11 Jaszkiewicz, A., "Genetic local search for multiobjective combinatorial optimization" 137 (137): 50-71, 2002

      12 Li, J., "Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions" 181 : 584-598, 2018

      13 Li, J., "Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots" 52 : 100600-, 2020

      14 Framinan, J. M., "Efficient heuristics for flowshop sequencing with the objectives of makespan and flowtime minimisation" 141 (141): 559-569, 2002

      15 Ghasemi, P., "An integrated FAHP-PROMETHEE approach for selecting the best flexible manufacturing system" 3 (3): 1137-1150, 2014

      16 Liu, X., "An improved SPEA2 algorithm with local search for multi-objective investment decision-making" 9 (9): 1675-, 2019

      17 Deb, K., "An evolutionary manyobjective optimization algorithm using referencepoint-based nondominated sorting approach, part I : solving problems with box constraints" 18 (18): 577-601, 2013

      18 Öztop, H., "An energy-efficient permutation flowshop scheduling problem" 150 : 113279-, 2020

      19 Zheng, D. Z., "An effective hybrid heuristic for flow shop scheduling" 21 (21): 38-44, 2003

      20 Li, B. B., "An effective PSO-based hybrid algorithm for multiobjective permutation flow shop scheduling" 38 (38): 818-831, 2008

      21 de Siqueira, E. C., "An MO-GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem" 27 (27): 614-650, 2020

      22 Arroyo, J. E. C., "A partial enumeration heuristic for multi-objective flowshop scheduling problems" 55 (55): 1000-1007, 2004

      23 Sadeghi, J., "A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management : An NSGA-II with tuned parameters" 41 : 53-64, 2014

      24 Reddy, B. S. P., "A hybrid multiobjective GA for simultaneous scheduling of machines and AGVs in FMS" 31 (31): 602-613, 2006

      25 Tavakkoli-Moghaddam, R., "A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives : Weighted mean completion time and weighted mean tardiness" 177 (177): 5072-5090, 2007

      26 Sindhya, K., "A hybrid framework for evolutionary multi-objective optimization" 17 (17): 495-511, 2012

      27 Li, J., "A hybrid artificial bee colony algorithm to solve multi-objective hybrid flowshop in cloud computing systems" Springer International 201-213, 2017

      28 Nawaz, M., "A heuristic algorithm for the m-machine, n-job flowshop sequencing problem" 11 (11): 91-95, 1983

      29 Deb, K., "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II" Springer 849-858, 2000

      30 Nagar, A., "A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem" 63 (63): 397-414, 1996

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2015-08-03 학술지명변경 한글명 : Industrial Engineeering & Management Systems -> Industrial Engineering & Management Systems
      외국어명 : Industrial Engineeering & Management Systems An International Journal -> Industrial Engineering & Management Systems An International Journal
      KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2009-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2007-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.13 0.13 0.1
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.1 0.09 0.316 0.05
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