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Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

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Abstract

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

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Authors and Affiliations

  1. Institut Supérieur de Gestion de Tunis, Université de Tunis, 41, Avenue de la Liberté, Cité Bouchoucha, Bardo, Tunis, Tunisie

    Houssem Eddine Nouri & Khaled Ghédira

  2. École Supérieure de Commerce de Tunis, Université de la Manouba, Tunis, Tunisie

    Olfa Belkahla Driss

Authors
  1. Houssem Eddine Nouri
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  2. Olfa Belkahla Driss
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  3. Khaled Ghédira
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Correspondence to Houssem Eddine Nouri.

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This paper does not contain any studies with human participants or animals performed by any of the authors.

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Nouri, H.E., Driss, O.B. & Ghédira, K. Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment. Appl Intell 45, 808–828 (2016). https://doi.org/10.1007/s10489-016-0786-y

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  • DOI: https://doi.org/10.1007/s10489-016-0786-y

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