An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L

The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

A multi-criteria decision support system for a routing problem in waste collection

Autor proofThis work presents a decision support system for route planning of vehicles performing waste collection for recycling. We propose a prototype system that includes three modules: route optimization, waste generation prediction, and multiple-criteria decision analysis (MCDA). In this work we focus on the application of MCDA in route optimization. The structure and functioning of the DSS is also presented. We modelled the waste collection procedure as a routing problem, more specifically as a team orienteering problem with capacity constraints and time windows. To solve the route optimization problem we developed a cellular genetic algorithm. For the MCDA module, we employed three methods: SMART, ValueFn and Analytic Hierarchy Process (AHP). The decision support system was tested with real-world data from a waste management company that collects recyclables, and the capabilities of the system are discussed.FCT Fundação para a Ciência e Tecnologia, Project Scope: PEst-OE/EEI/UI0319/2

Comparação de dois algoritmos genéticos aplicados ao TOP

A recolha seletiva de resíduos sólidos urbanos para reciclagem é um processo dispendioso, especialmente quando realizado em grande escala. Um problema importante neste processo reside na gestão de uma frota, uma vez que atualmente as estratégias utilizadas geralmente têm baixa eficiência. O processo de recolha seletiva de resíduos sólidos urbanos pode ser modelado como um problema de encaminhamento de veículos, em particular como um Problema de Orientação de Equipas (TOP - Team Orienteering Problem). No TOP uma frota de veículos é incumbida de visitar um conjunto selecionado de vértices, de modo a maximizar o luvro total. O objetivo deste trabalho é o de otimizar o processo de recolha selectiva de resíduos sólidos urbanos ao abordar as questões relacionadas com a gestão de uma frota. Isso deve ser alcançado através do desenvolvimento de uma ferramenta de software que implementa um algoritmo genético para resolver o modelo desenvolvido. Neste artigo apresentamos e comparamos dois algoritmos genéticos através de experiências computacionais realizadas com instâncias de teste conhecidas da literatura. O uso de algoritmos genéticos para resolver o TOP mostra ser uma escolha acertada, pois o método é eficiente produzindo bons resultados num tempo aceitável.Fundos FEDER através do Programa Operacional Fatores de Competitividade – COMPETE e por Fundos Nacionais através da FCT – Fundação para a Ciência e Tecnologia no âmbito do Projeto: FCOMP-01-0124-FEDER-022674GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME-GIN/ 120761/2010), financiado por fundos nacionais pela FCT / MCTES e co-financiado pelo by the European Social Development Fund (FEDER) through the COMPETE Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124-FEDER-020609

An efficient evolutionary algorithm for the orienteering problem

This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem, each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject to a limitation on the total route distance. To solve this problem, we propose an evolutionary algorithm, whose key characteristic is to maintain unfeasible solutions during the search. Furthermore, it includes a novel solution codification for the Orienteering Problem, a novel heuristic for node inclusion in the route, an adaptation of the Edge Recombination crossover developed for the Travelling Salesperson Problem, specific operators to recover the feasibility of solutions when required, and the use of the Lin-Kernighan heuristic to improve the route lengths. We compare our algorithm with three state-of-the-art algorithms for the problem on 344 benchmark instances, with up to 7397 nodes. The results show a competitive behavior of our approach in instances of low-medium dimensionality, and outstanding results in the large dimensionality instances reaching new best known solutions with lower computational time than the state-of-the-art algorithms.MTM2015-65317-P, TIN2016-78365-R, IT-609-13, IT-928-16, UFI BETS 201

AN EFFECTIVE METAHEURISTIC FOR TOURIST TRIP PLANNING IN PUBLIC TRANSPORT NETWORKS

The Time-Dependent Orienteering Problem with Time Windows (TDOPTW) is a combinatorial optimization problem defined on graphs. Its real life applications are particularly associated with tourist trip planning in trans-port networks, where travel time between two points depends on the moment of travel start. In the paper an effective TDOPTW solution (evolutionary algorithm with local search operators) was presented and applied to gen-erate attractive tours in real public transport networks of Białystok and Athens. The method achieved very high-quality solutions in a short execution time