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Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Author

Listed:
  • Tao Ren

    (College of Software, Northeastern University, Shenyang 110819, China)

  • Yan Zhang

    (College of Software, Northeastern University, Shenyang 110819, China)

  • Shuenn-Ren Cheng

    (Department of Business Administration, Cheng Shiu University, Kaohsiung 83347, Taiwan)

  • Chin-Chia Wu

    (Department of Statistics, Feng Chia University, Taichung 40724, Taiwan)

  • Meng Zhang

    (College of Software, Northeastern University, Shenyang 110819, China)

  • Bo-yu Chang

    (College of Software, Northeastern University, Shenyang 110819, China)

  • Xin-yue Wang

    (College of Software, Northeastern University, Shenyang 110819, China)

  • Peng Zhao

    (College of Software, Northeastern University, Shenyang 110819, China)

Abstract

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

Suggested Citation

  • Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1221-:d:389696
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    References listed on IDEAS

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    1. T. C. E. Cheng & Bo Peng & Zhipeng Lü, 2016. "A hybrid evolutionary algorithm to solve the job shop scheduling problem," Annals of Operations Research, Springer, vol. 242(2), pages 223-237, July.
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    Cited by:

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