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Economic and Food Safety: Optimized Inspection Routes Generation

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Agents and Artificial Intelligence (ICAART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12613))

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Abstract

Data-driven decision support systems rely on increasing amounts of information that needs to be converted into actionable knowledge in business intelligence processes. The latter have been applied to diverse business areas, including governmental organizations, where they can be used effectively. The Portuguese Food and Economic Safety Authority (ASAE) is one example of such organizations. Over its years of operation, a rich dataset has been collected which can be used to improve their activity regarding prevention in the areas of food safety and economic enforcement. ASAE needs to inspect Economic Operators all over the country, and the efficient and effective generation of optimized and flexible inspection routes is a major concern. The focus of this paper is, thus, the generation of optimized inspection routes, which can then be flexibly adapted towards their operational accomplishment. Each Economic Operator is assigned an inspection utility – an indication of the risk it poses to public health and food safety, to business practices and intellectual property as well as to security and environment. Optimal inspection routes are then generated typically by seeking to maximize the utility gained from inspecting the chosen Economic Operators. The need of incorporating constraints such as Economic Operators’ opening hours and multiple departure/arrival spots has led to model the problem as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows. Exact and meta-heuristic methods were implemented to solve the problem and the Genetic Algorithm showed a high performance with realistic solutions to be used by ASAE inspectors. The hybrid approach that combined the Genetic Algorithm with the Hill Climbing also showed to be a good manner of enhancing the solution quality.

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Acknowledgements

This work is supported by project IA.SAE, funded by Fundação para a Ciência e a Tecnologia (FCT) through program INCoDe.2030. This research was partially supported by LIACC (FCT/UID/CEC/0027/2020).

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Authors and Affiliations

  1. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    Telmo Barros, Alexandra Oliveira, Henrique Lopes Cardoso & Luís Paulo Reis

  2. Escola Superior de Saúde do Instituto Politécnico do Porto (ESS-IPP), Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal

    Alexandra Oliveira

  3. Autoridade de Segurança Alimentar e Económica (ASAE), Rua Rodrigo da Fonseca, 73, 1269-274, Lisbon, Portugal

    Cristina Caldeira & João Pedro Machado

Authors
  1. Telmo Barros
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  2. Alexandra Oliveira
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  3. Henrique Lopes Cardoso
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  4. Luís Paulo Reis
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  5. Cristina Caldeira
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  6. João Pedro Machado
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Corresponding author

Correspondence to Telmo Barros .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Barros, T., Oliveira, A., Cardoso, H.L., Reis, L.P., Caldeira, C., Machado, J.P. (2021). Economic and Food Safety: Optimized Inspection Routes Generation. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-71158-0_23

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  • Online ISBN: 978-3-030-71158-0

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