Economic and Food Safety: Optimized Inspection Routes Generation

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.