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A Hybrid Genetic Algorithm For The Early/Tardy Scheduling Problem

Author

Listed:
  • JORGE M. S. VALENTE

    (Faculdade de Economia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

  • JOSÉ FERNANDO GONÇALVES

    (Faculdade de Economia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

  • RUI A. F. S. ALVES

    (Faculdade de Economia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

Abstract

In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

Suggested Citation

  • Jorge M. S. Valente & José Fernando Gonçalves & Rui A. F. S. Alves, 2006. "A Hybrid Genetic Algorithm For The Early/Tardy Scheduling Problem," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 23(03), pages 393-405.
    • Handle: RePEc:wsi:apjorx:v:23:y:2006:i:03:n:s0217595906000978
    DOI: 10.1142/S0217595906000978
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    Cited by:

    1. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.

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