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A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems

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Operations Research and Enterprise Systems (ICORES 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1162))

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Abstract

Multi-Agent Systems (MASs) have received great attention from scholars and engineers in different domains, including computer science and robotics. MASs try to solve complex and challenging problems (e.g., a mission) by dividing them into smaller problem instances (e.g., tasks) that are allocated to the individual autonomous entities (e.g., agents). By fulfilling their individual goals, they lead to the solution to the overall mission. A mission typically involves a large number of agents and tasks, as well as additional constraints, e.g., coming from the required equipment for completing a given task. Addressing such problem can be extremely complicated for the human operator, and several automated approaches fall short of scalability. This paper proposes a genetic algorithm for the automation of multi-agent mission planning. In particular, the contributions of this paper are threefold. First, the mission planning problem is cast into an Extended Colored Traveling Salesperson Problem (ECTSP), formulated as a mixed integer linear programming problem. Second, a precedence constraint reparation algorithm to allow the usage of common variation operators for ECTSP is developed. Finally, a new objective function minimizing the mission makespan for multi-agent mission planning problems is proposed.

This work was supported by the project Aggregate Farming in the Cloud (AFarCloud) European project, by the Swedish Foundation for Strategic Research under the project “Future factories in the cloud (FiC)” with grant number GMT14-0032, with project number 783221 (Call: H2020-ECSEL-2017-2), and by the Knowledge Foundation with the FIESTA project.

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Notes

  1. 1.

    CTSP is an abbreviation used in the literature for a Clustered TSP as well.

  2. 2.

    The link to benchmark scenarios: https://github.com/mdh-planner/ECTSP.

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Acknowledgements

Special thanks to Afshin E. Ameri for developing GUI for the MMT.

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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, 721 23, Västerås, Sweden

    Branko Miloradović, Baran Çürüklü, Mikael Ekström & Alessandro V. Papadopoulos

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  1. Branko Miloradović
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  2. Baran Çürüklü
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  3. Mikael Ekström
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  4. Alessandro V. Papadopoulos
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Correspondence to Branko Miloradović .

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Editors and Affiliations

  1. INFORMS, Catonsville, MD, USA

    Greg H. Parlier

  2. Universidad Carlos III de Madrid, Madrid, Spain

    Federico Liberatore

  3. RMIT University, Melbourne, VIC, Australia

    Marc Demange

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Miloradović, B., Çürüklü, B., Ekström, M., Papadopoulos, A.V. (2020). A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems. In: Parlier, G., Liberatore, F., Demange, M. (eds) Operations Research and Enterprise Systems. ICORES 2019. Communications in Computer and Information Science, vol 1162. Springer, Cham. https://doi.org/10.1007/978-3-030-37584-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-37584-3_6

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