Heuristics for Routing Heterogeneous Unmanned Vehicles with Fuel Constraints

This paper addresses a multiple depot, multiple unmanned vehicle routing problem with fuel constraints. The objective of the problem is to find a tour for each vehicle such that all the specified targets are visited at least once by some vehicle, the tours satisfy the fuel constraints, and the total travel cost of the vehicles is a minimum. We consider a scenario where the vehicles are allowed to refuel by visiting any of the depots or fuel stations. This is a difficult optimization problem that involves partitioning the targets among the vehicles and finding a feasible tour for each vehicle. The focus of this paper is on developing fast variable neighborhood descent (VND) and variable neighborhood search (VNS) heuristics for finding good feasible solutions for large instances of the vehicle routing problem. Simulation results are presented to corroborate the performance of the proposed heuristics on a set of 23 large instances obtained from a standard library. These results show that the proposed VND heuristic, on an average, performed better than the proposed VNS heuristic for the tested instances

Otimização de itinerários para a fiscalização de estacionamento

Mestrado em Métodos Quantitativos para a Decisão Económica e EmpresarialAo longo dos últimos anos os problemas de roteamento nos arcos têm vindo a ser estudados com grande intensidade. Neste tipo de problemas, o objetivo é atravessar determinadas ligações, habitualmente relacionadas com as ruas ou vias, representadas num grafo que, no presente trabalho, se integram em zonas de estacionamento na cidade de Lisboa, geridas pela EMEL (Empresa Municipal de Mobilidade e Estacionamento de Lisboa). A ideia central consiste em construir percursos de trabalho para cada fiscal de estacionamento que permitam atender todas as necessidades de fiscalização de estacionamento da melhor maneira possível. Os fiscais iniciam os seus percursos de modo a fiscalizar as ligações especificadas e, de seguida, retornam ao depósito, respeitando a capacidade. A restrição de capacidade de cada veículo corresponde à duração do turno de cada fiscal. Para avaliar a necessidade de fiscalização de cada rua foi introduzido um parâmetro, denominado por criticidade, que varia de acordo com a hora do dia. Assim, o objetivo do problema assenta na maximização da criticidade total associada a todos os percursos. São propostas uma heurística construtiva para obtenção de soluções admissíveis iniciais e uma abordagem metaheurística, baseada em Tabu Search (TS), para resolver instâncias de grande dimensão. Esta, por sua vez, inclui uma heurística melhorativa de pesquisa local, 2-opt. Os algoritmos propostos foram implementados com recurso ao Microsoft Excel Visual Basic for Applications e testes relativamente ao seu desempenho foram realizados em pequenos exemplos gerados aleatoriamente e também em instâncias da vida real baseadas em dados de ruas de Lisboa.Over the past years, arc routing problems have been studied intensively. In this type of problems, the main purpose is to cross certain connections that are related to streets or roads of a graph, which, in this case, represent the parking lot areas in the city of Lisbon, managed by EMEL (Empresa Municipal de Mobilidade e Estacionamento de Lisboa). The main idea is to build paths for each parking enforcement officer that may attend all the inspection needs in the best way possible. The officers start their routes in order to inspect the requested links and then return to the depot taking into account the capacity. The capacity constraint is related to the duration of the officers' shifts. In order to assess the inspection needs of each area, a parameter was assigned, called criticality, which changes throughout the day. Therefore, the objective of the problem is to maximize the total criticality of all routes.A constructive heuristic to obtain initial feasible solutions, and a metaheuristic approach based on a Tabu Search (TS) for solving larges instances are proposed. TS includes an improving local search heuristic, 2-opt. The proposed algorithms were implemented using Microsoft Excel Visual Basic for Applications and their performance were tested through randomly generated examples and also real life instances based data from streets of Lisbon.info:eu-repo/semantics/publishedVersio

Solving arc routing problems for winter road maintenance operations

For winter road maintenance, a fleet of snowplow trucks is operated by government agencies to remove snow and ice on roadways and spread materials for anti-icing, de-icing, or increasing friction. Winter road maintenance is essential for providing safe and efficient service for road users (Usman et al., 2010). It is also costly due to the high cost of equipment, crew, and materials. Optimizing winter road maintenance operations could result in significant cost savings, improved safety and mobility, and reduced environmental and social impacts (Salazar-Aguilar et al., 2012). The first topic in this study focuses on designing routes for winter maintenance trucks from a single depot. Real-world winter road maintenance constraints, including road segment service cycle time, heterogeneous vehicle capacity, fleet size, and road-vehicle dependency, are taken into consideration. The problem is formulated as a variation of the capacitated arc routing problem (CARP) to minimize total travel distance. A metaheuristic algorithm, memetic algorithm (MA), is developed to find nearly optimal solutions. This is the first study that developed the model that includes all the constraints listed. This is the first study that used the MA to solve the routing problem with all those constraints, and the first study that developed the route split procedure that satisfies all those constraints. In addition, a paralleled metaheuristic algorithm is proposed to enhance the solution quality and computation efficiency. The second topic of this study focuses on designing routes from multiple depots with intermediate facilities. The service boundaries of depots are redesigned. Each truck must start and end at its home depot, but they can reload at other depots or reload stations (i.e., intermediate facilities). This problem is a variation of the multi-depot CARP with intermediate facilities (MDCARPIF). The second topic includes all constraints employed in the first topic. Since the trucks can be reloaded at any stations, a constraint that restricts the length of work time for truck drivers is also included in this topic. This is the first study that developed the model that includes all the constraints listed. This is the first study that uses the MA to solve the problem and the first study that developed the route split procedure that satisfies all those constraints. The proposed algorithms are implemented to solve real-world problems. Deadhead (travelling without servicing) speed, service speed, and the spreading rate are estimated by the sample from historical winter road maintenance data. Eighteen traffic networks are used as instances for the first topic. The optimized route in the first topic reduced 13.2% of the deadhead distance comparing to the current practice. Comparing to the single core result, the parallel computation improved the solution fitness on 2 of the 18 instances tested, with slightly less time consumed. Based on the optimized result in the first topic, the reduction of the deadhead distance of the second topic is insignificant. This could be due to the network structure and depot location of the current operation. A test instance is created to verify the effectiveness of the proposed algorithm. The result shows 10.4% of deadhead distance can be saved by using the reload and multiple depot scenario instead of the single depot scenario on the test instance

Algorithms for Routing Unmanned Vehicles with Motions, Resource, and Communication Constraints

Multiple small autonomous or unmanned aerial and ground vehicles are being used together with stationary sensing devices for a wide variety of data gathering, monitoring and surveillance applications in military, civilian, and agricultural applications, to name a few. Even though there are several advantages due to the small platforms for these vehicles, they pose a variety of challenges. This dissertation aims to address the following challenges to routing multiple small autonomous aerial or ground vehicles: (i) limited communication capabilities of the stationary sensing devices, (ii) dynamics of the vehicles, (iii) varying sensing capabilities of all the vehicles, and (iv) resource constraints in the form of fuel restrictions on each vehicle. The dissertation formulates four different routing problems for multiple unmanned vehicles, one for each of the aforementioned constraints, as mixed-integer linear programs and develops numerically efficient algorithms based on the branch-and-cut paradigm to compute optimal solutions for practically reasonable size of test instances