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Using evolutionary computation and local search to solve multi-objective flexible job shop problems

Published: 07 July 2007 Publication History

Abstract

Finding realistic schedules for Flexible Job Shop Problems has attracted many researchers recently due to its NP-hardness. In this paper, we present an efficient approach for solving the multi-objective flexible job shop by combining Evolutionary Algorithm and Guided Local Search. Instead of applying random local search to find neighborhood solutions, we introduce a guided local search procedure to accelerate the process of convergence to Pareto-optimal solutions. The main improvement of this combination is to help diversify the population towards the Pareto-front. Empirical studies show that 1) the gaps between the obtained results and known lower bounds are small, and 2) the multi-objective solutions of our algorithms dominate previous designs for solving the same benchmarks while incurring less computational time.

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  • (2024)Multi-objective flexible job-shop scheduling problem with improved NSGA2 algorithm2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662041(2154-2159)Online publication date: 28-Jul-2024
  • (2024)D-MEANDS-MD: an improved evolutionary algorithm with memory and diversity strategies applied to a discrete, dynamic, and many-objective optimization problemThe Knowledge Engineering Review10.1017/S026988892400007939Online publication date: 2-Dec-2024
  • (2018)Convergence analysis of evolutionary algorithms solving the Flexible Job Shop Problem2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477685(1-7)Online publication date: Jul-2018
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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2007

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    Author Tags

    1. flexible job shop problems
    2. guided local search
    3. multi-objective evolutionary algorithm

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2024)Multi-objective flexible job-shop scheduling problem with improved NSGA2 algorithm2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662041(2154-2159)Online publication date: 28-Jul-2024
    • (2024)D-MEANDS-MD: an improved evolutionary algorithm with memory and diversity strategies applied to a discrete, dynamic, and many-objective optimization problemThe Knowledge Engineering Review10.1017/S026988892400007939Online publication date: 2-Dec-2024
    • (2018)Convergence analysis of evolutionary algorithms solving the Flexible Job Shop Problem2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477685(1-7)Online publication date: Jul-2018
    • (2017)A Simple Estimation of Distribution Algorithm for the Flexible Job-Shop Problem2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969575(2233-2239)Online publication date: Jun-2017
    • (2012)A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problemInternational Journal of Computer Applications in Technology10.1504/IJCAT.2012.05070045:2/3(115-125)Online publication date: 1-Dec-2012
    • (2010)Solving multiobjective flexible job-shop scheduling using an adaptive representationProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830615(737-742)Online publication date: 7-Jul-2010
    • (2010)A robust multi‐objective resource allocation scheme incorporating uncertainty and service differentiationConcurrency and Computation: Practice and Experience10.1002/cpe.148122:3(314-328)Online publication date: 27-Jan-2010
    • (2009)Adaptive representation for flexible job-shop scheduling and reschedulingProceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation10.1145/1543834.1543903(511-516)Online publication date: 12-Jun-2009
    • (2009)An Improved PSO Algorithm for Flexible Job Shop Scheduling with Lot-Splitting2009 International Workshop on Intelligent Systems and Applications10.1109/IWISA.2009.5072720(1-5)Online publication date: May-2009
    • (2008)A novel multi-objective optimization scheme for grid resource allocationProceedings of the 6th international workshop on Middleware for grid computing10.1145/1462704.1462711(1-6)Online publication date: 1-Dec-2008

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