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

Combining parallel computing and biased randomization for solving the team orienteering problem in real-time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.Peer ReviewedPostprint (published version

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

[EN] In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an 'agile' optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.This work has been partially supported by the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T). We also acknowledge the support of the Erasmus+ Program (2019-I-ES01-KA103-062602)Panadero, J.; Ammouriova, M.; Juan-Pérez, ÁA.; Agustin, A.; Nogal, M.; Serrat, C. (2021). Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time. Applied Sciences. 11(24):1-18. https://doi.org/10.3390/app112412092118112

Orienteering Problem: A survey of recent variants, solution approaches and applications

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

An updated annotated bibliography on arc routing problems

The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio

Problèmes de tournées de véhicules et application industrielle pour la réduction de l'empreinte écologique

Dans cette thèse, nous nous sommes intéressés à la résolution approchée de problèmes de tournées de véhicules. Nous avons exploité des travaux menés sur les graphes d'intervalles et des propriétés de dominance relatives aux tournées saturées pour traiter les problèmes de tournées sélectives plus efficacement. Des approches basées sur un algorithme d'optimisation par essaim particulaire et un algorithme mémétique ont été proposées. Les métaheuristiques développées font appel à un ensemble de techniques particulièrement efficaces telles que le découpage optimal, les opérateurs de croisement génétiques ainsi que des méthodes de recherches locales. Nous nous sommes intéressés également aux problèmes de tournées classiques avec fenêtres de temps. Différents prétraitements ont été introduits pour obtenir des bornes inférieures sur le nombre de véhicules. Ces prétraitements s'inspirent de méthodes issues de modèles de graphes, de problème d'ordonnancement et de problèmes de bin packing avec conflits. Nous avons montré également l'utilité des méthodes développées dans un contexte industriel à travers la réalisation d'un portail de services mobilité.In this thesis, we focused on the development of heuristic approaches for solvingvehicle routing problems. We exploited researches conducted on interval graphsand dominance properties of saturated tours to deal more efficiently with selectivevehicle routing problems. An adaptation of a particle swarm optimization algorithmand a memetic algorithm is proposed. The metaheuristics that we developed arebased on effective techniques such as optimal split, genetic crossover operatorsand local searches. We are also interested in classical vehicle problems with timewindows. Various pre-processing methods are introduced to obtain lower boundson the number of vehicles. These methods are based on many approaches usinggraph models, scheduling problems and bin packing problems with conflicts. Wealso showed the effectiveness of the developed methods with an industrial applicationby implementing a portal of mobility services.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Exact Algorithm for the Capacitated Team Orienteering Problem with Time Windows

The capacitated team orienteering problem with time windows (CTOPTW) is a problem to determine players' paths that have the maximum rewards while satisfying the constraints. In this paper, we present the exact solution approach for the CTOPTW which has not been done in previous literature. We show that the branch-and-price (B&P) scheme which was originally developed for the team orienteering problem can be applied to the CTOPTW. To solve pricing problems, we used implicit enumeration acceleration techniques, heuristic algorithms, and ng-route relaxations