The Rapid development and the emergence of technologies capable of producing real-time data
opened new horizons to both planning and optimization of vehicle routes [4]. In this dissertation, the Autoridade de Segurança Alimentar e Económica (ASAE) operation's scenario will be
explored and analyzed as a case study to the problem. ASAE is a Portuguese administrative authority specialized in food security and economic auditing and is responsible to regulate thousands of
economic entities in the Portuguese territory. ASAE inspections are usually done by brigades using vehicles to inspect economic operators, taking into account their timetables. Previous work on
this topic led to the implementation of an inspection route optimization module capable of defining and assigning routes to inspect economic operators, seeking to maximize a utility function.
Using optimization algorithms, inspection routes are calculated for each brigade, with information regarding specific map paths and inspection schedules. The approach used does not take into
consideration the dynamic properties of real-life scenarios, as the precalculated operation plan is
not reviewed in real-time. This work aims to study the dynamic properties of ASAE's operational
environment and proposes a solution to efficiently review the precalculated inspection routes and
apply the required changes in an appropriate time frame.
Vehicle routing problems (VRP) are optimization problems where the aim is to calculate the
set of optimized routes for a vehicle fleet, from a starting point to several interesting locations.
Dynamic vehicle routing problem (DVRP) is a variant of VRP that makes use of real-time information to calculate the most optimized set of routes at a certain moment [39]. DVRP is a
challenging problem because its scope is real-time, meaning that decisions sometimes must be
made in short time windows, preventing the use of complex algorithms that require long computational times [10]. The typical approach to this problem is to initially calculate the routes for
the whole fleet and dynamically revise the defined operations plan in real-time, once a disruption
occurs. This work will model the problem as a DVRP and will compare the performance of heuristics and other modern optimization techniques, proposing a solution that will reduce the impact of
disruptions on inspection routes.
An optimized operations plan will reduce the time required for inspections, allowing massive
economic savings, while reducing a company's ecological footstep. The work can eventually be
scaled and used in other institutions, such as GNR or PSP in Portugal, that operate similarly
