Authors:
Telmo Barros
1
;
Alexandra Oliveira
2
;
1
;
Henrique Lopes Cardoso
1
;
Luís Paulo Reis
1
;
Cristina Caldeira
3
and
João Pedro Machado
3
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
;
2
Escola Superior de Saúde do Instituto Politécnico do Porto (ESS-IPP), Rua Dr. António Bernardino de Almeida, 400 4200 - 072, Porto
;
3
Autoridade de Segurança Alimentar e Económica (ASAE), Rua Rodrigo da Fonseca, 73, 1269-274 Lisboa, Portugal
Keyword(s):
Planning, Scheduling, Optimization, Decision Support, Vehicle Routing Problem.
Abstract:
Artificial intelligence techniques have been applied to diverse business and governmental areas, in order to take advantage of the huge amount of information that is generated within specific organizations or institutions. Business intelligence can be seen as the process of converting such information into actionable knowledge, which is the basis for data-driven decision making. With this in mind, this work is framed in a project that seeks to improve the activity of the Portuguese Food and Economic Safety Authority, regarding prevention in the areas of food safety and economic enforcement. More specifically, this paper focuses on the generation and optimization of flexible inspection routes. An optimal inspection route seeks to maximize the number of targeted Economic Operators, or the utility gained from the set of Economic Operators that are actually inspected. For that, each Economic Operator is assigned an inspection utility value. The problem was then modelled as a Multi-Depot
Periodic Vehicle Routing Problem with Time Windows, and solved using both exact and meta-heuristic methods. The comparison of the meta-heuristic algorithms showed a versatile Hill Climbing implementation in different test cases that explored the effect of the Economic Operators dispersion and density.
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