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An environment-driven, function-based approach to dynamic single-machine scheduling

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  • Arezoo Atighehchian
  • Mohammad Mehdi Sepehri

Abstract

In this paper, the dynamic single-machine scheduling problem with a sequence-dependent setup time and with minimising total weighted tardiness of jobs as the objective is investigated. Due to the dynamic nature of the problem, a function-based approach is developed that can capture dynamic characteristics associated with the environment. In order to find a function which maps the environment's states to an action at each decision point, a combination of simulated annealing and a multi-layer feed-forward neural network is employed in an algorithm named SANN. The efficiency of the proposed function-based approach is compared with the most commonly used dispatching rules and with an agent-based approach, which employs the Q-learning algorithm to develop a decision-making policy. Numerical results reveal that the proposed approach outperforms dispatching rules and the Q-learning algorithm. The mean value of the results is about 93% better than the mean of the best results obtained with dispatching rules. [Received 4 January 2010; Revised 28 September 2010, 22 February 2011, 6 June 2011, 28 June 2011; Accepted 3 July 2011].

Suggested Citation

  • Arezoo Atighehchian & Mohammad Mehdi Sepehri, 2013. "An environment-driven, function-based approach to dynamic single-machine scheduling," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 7(1), pages 100-118.
    • Handle: RePEc:ids:eujine:v:7:y:2013:i:1:p:100-118
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    Citations

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    Cited by:

    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
    3. Payman Eslami & Kihyo Jung & Daewon Lee & Amir Tjolleng, 2017. "Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 538-550, August.
    4. Allahverdi, Ali & Aydilek, Harun, 2014. "Total completion time with makespan constraint in no-wait flowshops with setup times," European Journal of Operational Research, Elsevier, vol. 238(3), pages 724-734.

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