Experiments on the Node, Edge, and Arc Routing Problem

The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP

Experiments on the Node, Edge, and Arc Routing Problem

-The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP

Routing Applications in Newspaper Delivery

-The goal of this report is to give an up-to-date account of routing applications in the newspaper business. We describe the newspaper supply chain, and focus on the “last mile” distribution that has been advocated as an application of arc routing in the literature. A literature survey is provided, followed by a discussion of the arc routing model and its adequacy to newspaper applications. A more general and normally more adequate model: The Node, Edge, and Arc Routing Problem, is discussed. Characteristics of routing problems in carrier delivery are presented, together with a case study from the development of a web-based route design and revision system. Finally, summary, conclusions, and prospects for the future are given

The node edge and arc routing problem with stochastic customers and service times

This presentation addresses a node, edge, and arc vehicle routing problem with stochastic customers and service times. A maximum duration is defined for each route, where the duration of each route is a random variable in this stochastic environment. We thus model the maximum duration constraint of each route as a chance constraint. In the defined methodology, the solutions obtained with the probabilistic knowledge at hand cover all customers with non-zero occurrence probability. After the demands are realized, only a subset of customers will actually be served. As a recourse strategy, a customer that does not require to be served is skipped in its corresponding route, while keeping the rest of the visiting sequence unchanged. This research is motivated by a real application in the context of postal delivery services. Those services are provided by couriers who perform daily rounds in fixed geographic areas, called districts, typically defined for a long period of time based on the expected demand. However, daily demand variations can negatively impact the couriers' workload, creating extra working hours. We address here the problem of designing routes so that the workload of couriers respects the imposed maximum working duration for most demand realizations.Combinatorial Optimization for Postal Services (COPS)9. Industry, innovation and infrastructur

An Adaptive Iterated Local Search for the Mixed Capacitated General Routing Problem

We study the mixed capacitated general routing problem (MCGRP) in which a fleet of capacitated vehicles has to serve a set of requests by traversing a mixed weighted graph. The requests may be located on nodes, edges, and arcs. The problem has theoretical interest because it is a generalization of the capacitated vehicle routing problem (CVRP), the capacitated arc routing problem (CARP), and the general routing problem. It is also of great practical interest since it is often a more accurate model for real-world cases than its widely studied specializations, particularly for so-called street routing applications. Examples are urban waste collection, snow removal, and newspaper delivery. We propose a new iterated local search metaheuristic for the problem that also includes vital mechanisms from adaptive large neighborhood search combined with further intensification through local search. The method utilizes selected, tailored, and novel local search and large neighborhood search operators, as well as a new local search strategy. Computational experiments show that the proposed metaheuristic is highly effective on five published benchmarks for the MCGRP. The metaheuristic yields excellent results also on seven standard CARP data sets, and good results on four well-known CVRP benchmarks, including improvement of the best known upper bound for one instance

Application of Simulated Annealing to Routing Problems in City Logistics

The R & D activities to realize systems which provide road traffic information and route guidance have been conducted as core systems of Intelligent Transport Systems (ITS). However, the implementation of these systems will have less effect on freight transport unless logistics operation is rationalized in parallel to the development of ITS. On the other hand, according to the expansion of internet, information has been exchanged with extremely high speed and low cost. Nevertheless, goods must be moved in the real space. Ecommerce has caused the increase of door-to-door deliveries. The demands for high-quality delivery services such as small-amount high frequency deliveries with time windows have been made by many clients (including companies and individuals). The loading rate of trucks has decreased and the rate of freight transportation in total road traffic has increased. The rationalization in terms of increasing the loading rate and decreasing the total travel time is aimed not only for reducing operational costs in each freight carrier but also for relieving traffic congestion, saving energy and reducing the amount of CO2. Freight transportation in urban areas that is described above is called city logistics (Taniguchi et al. 2001). Many researches on routing problems have been appeared in the literature. Comprehensive and detailed explanations of theoretical models and solutions of them are given by Toth & Vigo (Toth & Vigo, 2002). On the other hand, in the context of city logistics, real routing problems should not be based under the assumption on the symmetry of the link costs of visiting customer j after customer i or customer i after customer j, pij=pji, and other related mathematical properties, as triangular property etc. This is due to the fact that in an urban environment routes using the streets have to account for one way streets, issues related to regulations at intersections. In addition, travel time might vary according to traffic conditions, that is to say, it might be time dependent. Moreover, in urban road networks, demands might be located on not only spots on streets but also streets themselves. This chapter is aimed for describing the original solution, which has been invented by the authors of this chapter, to routing problems in city logistics. At the beginning of this chapter, a variety of routing problems will be introduced and followed by the explanation of features of routing problems in city logistics. And then, a practical solution method, which is composed of a data model, transformation rules of a solution on the data model and an overall algorithm using Simulated Annealing for solving O pe n A cc es s D at ab as e w w w .ite ch on lin e. co

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

Arc routing problems with drones

La tecnología emergente de vehículos aéreos no tripulados, comúnmente conocidos como drones, ha brindado nuevas oportunidades para los profesionales de la logística urbana en la última década. El transporte ha jugado siempre un papel crucial en la sociedad y en la economía, y un motor fundamental del desarrollo económico en los últimos tiempos ha sido la inversión en sistemas de transporte cada vez más eficientes. Los drones presentan ventajas atractivas en comparación con los vehículos terrestres estándar, como evitar la congestión en las redes viales, eliminar el riesgo del personal en operaciones de difícil acceso u obtener una mayor precisión de medición en la inspección de infraestructuras. Muchas empresas comerciales han mostrado recientemente interés en utilizar drones para realizar entregas de última milla más rentables y rápidas. Amazon anunció a finales de 2013 que entregaría paquetes directamente en cada puerta a través de Prime Air usando pequeños drones 30 minutos después de que los clientes presionaran el botón “comprar”. Unos años más tarde, lanzaría una versión de su dron de entrega Prime Air que era una aeronave híbrida robusta capaz de despegar y aterrizar verticalmente que podía volar hasta 15 millas y entregar paquetes de menos de cinco libras a los clientes en menos de 30 minutos. Junto con Amazon, otros servicios de entrega como UPS o Google han estado probando el uso potencial de drones para la entrega de paquetes. Dado que los drones aéreos no están restringidos por la infraestructura local, también se pueden utilizar de manera rentable en la distribución rural, la vigilancia y la intralogística, así como en el mapeo geológico y ambiental en 3D para la recopilación de datos. El uso de drones dentro de todos estos escenarios enfrenta múltiples problemas (y desafíos) que pueden ser abordados mediante problemas de rutas, cuyos modelos de solución apuntan a encontrar la ruta (o rutas) más eficiente relacionada con un recurso explícito como la distancia, el tiempo o la energía.The emerging technology of drones has provided new opportunities for practitioners in urban logistics in the last decade. Drones present attractive advantages compared with standard ground vehicles in transportation, such as avoiding the congestion on road networks, eliminating the risk of personnel in difficult access operations or getting higher measurement accuracy in infrastructure inspection. The use of drones within distribution, surveillance or intralogistics scenarios faces multiple issues (and challenges) that can be addressed by routing problems, whose solution models aim to find the most efficient route (or routes) related to an explicit resource such as distance, time or energy.This thesis focuses on the study of some extensions of arc routing problems in which drones are used to optimize a certain service. Given a graph representing a network, arc routing problems (ARPs) consist of finding a tour, or a set of tours, with total minimum cost traversing (servicing) a set of links (arc or edges) of the graph, called required links, and satisfying certain conditions. The use of drones to perform the service in ARPs involves significant changes in the traditional way of modeling and solving these problems. Since aerial drones have the capability to travel directly between any two points of the network, not necessarily between vertices of the graph, arc routing problems with drones are continuous optimization problems with an infinite and uncountable number of feasible solutions. One mathematical approach for their solution consists on approximating each curved line in the plane of a drone ARP instance by a polygonal chain with a finite number of segments, and solving the problem as a discrete optimization problem, where vehicles are allowed to enter and leave each curved line only at the points of the polygonal chain. Once discretized, the set of non-required edges of the instance forms a complete graph, and the deadheading cost between any pair of points is given by the Euclidean distance. In this context, we address three variants of arc routing problems with drones, which are modeled as combinatorial optimization problems and addressed with heuristic and exact mathematical approaches: the length constrained K-drones rural postman problem, where a fleet of K drones with limited autonomy has to jointly traverse a set of lines on the plane, the multi-purpose K-drones general routing problem, where a fleet of K multi-purpose drones (aerial vehicles that can both make deliveries and conduct sensing activities) has to jointly visit a set of nodes to make deliveries and also map one or more continuous areas, and the load-dependent drone general routing problem, an extension of the classical general routing problem in which the traversal time of each edge of the graph depends on the cargo carried by the drone

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Experiments on the Node, Edge, and Arc Routing Problem

-The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP