Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity

© 2016 Elsevier B.V. After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions

A Model-Based Heuristic to the Min Max K-Arc Routing for Connectivity Problem

We consider the post-disaster road clearing problem with the goal of restoring network connectivity in shortest time. Given a set of blocked edges in the road network, teams positioned at depot nodes are dispatched to open a subset of them that reconnects the network. After a team finishes working on an edge, others can traverse it. The problem is to find coordinated routes for the teams. We generate a feasible solution using a constructive heuristic algorithm after solving a relaxed mixed integer program. In almost 70 percent of the instances generated both randomly and from Istanbul data, the relaxation solution turned out to be feasible, i.e. optimal for the original problem

Integrated network routing and scheduling problem for salt trucks with replenishment before snowfall

Kar yağışı öncesinde ve sırasında yolların zamanında tuzlanması, trafik güvenliğini iyileştirmek ve trafik sıkışıklığını önlemek için önemli bir önleyici faaliyettir. Bu çalışmada, bir şehir yolu ağındaki tuz kamyonlarının rotalama ve çizelgeleme problemi ele alınmıştır. Ele alınan problem İstanbul Büyükşehir Belediyesinin yoğun kar yağışı durumlarında karşılaştığı bir operasyonel problemdir ve periyodik olarak çözülmelidir. Problemde, araç filosu tuz kapasitesi açısından heterojen araçlardan oluşmaktadır ve birden fazla tuz ikmal noktası bulunmaktadır. Hava şartları gerektirdiğinde, tuzlanması gereken yollar ve bu yollar için öncelik seviyeleri belirlenmektedir. Amaç, ağın farklı noktalarında konumlanmış olan araçların, tuzlanması gereken tüm yolları tuzlayacak şekilde ve yolların ağırlıklı tamamlanma süresini en küçükleyerek rotalanması ve çizelgelenmesidir. Tuza ihtiyacı olan her yol tek bir araç tarafından tuzlanmalıdır. Araçlar tuzlanması gereken bir yolu tuzlama yapmadan sadece geçiş yapmak amacıyla da kullanılabilir. Araçlar, tuzları bittiğinde tuz ikmal noktalarını ziyaret etmelidir. Problemin çözümü için ilk olarak bir karma tam sayılı programlama modeli geliştirilmiştir. Problem büyüklüğü arttıkça modelin performansının hızla düştüğü gözlemlenmiş ve iki aşamalı bir sezgisel yöntem geliştirilmiştir. Sezgiselin ilk aşamasında yapıcı algoritma ile olurlu bir başlangıç çözümü elde edilmektedir, ikinci aşamasında bulunan başlangıç çözümü bir komşuluk arama algoritması ile geliştirilmektedir. Çözüm yaklaşımımızın verimliliği, gerçek hayat yol ağlarını yansıtan rastgele oluşturulmuş örnekler üzerinde analiz edilmiştir.Timely salting of roads before the snowfall is an important preventive activity for improving traffic safety and avoiding traffic congestions. We study the problem of routing and scheduling of salt trucks on a city road network. The problem is motivated by the operational problem that the Istanbul Metropolitan Municipality face in case of a heavy snowfall, and thereby should be solved in a periodic manner.In this problem, the vehicle fleet consists of heterogeneous vehicles that differ in salt capacity and there are multiple salt replenishment points. At the beginning of the current planning horizon, given a set of salt needing roads with different urgency levels, the vehicles start from different points of the network (i.e., their final locations at the end of the former planning horizon) and should cover all salt needing roads with the objective of minimizing the total weighted completion time of salting operation of each service needing arc. Each service needing arc should be serviced by exactly one vehicle, however, can be traversed for deadheading by a vehicle as part of its route.Vehicles visit replenishment points when they run out of salt. We first develop a Mixed-Integer Programming model for the problem. Since the performance of the model degrades rapidly as the problem size increases, we propose a simulated annealing metaheuristic, which obtains an initial solution by a constructive heuristic in the first phase, and then improves the solution in the next phase. The efficiency of our solution approach is evaluated on randomly generated instances reflecting real life road networks

Multi-vehicle prize collecting arc routing for connectivity problem

© 2017 Elsevier Ltd For effective disaster response, roads should be cleared or repaired to provide accessibility and relief services to the affected people in shortest time. We study an arc routing problem that aims to regain the connectivity of the road network components by clearing a subset of the blocked roads. In this problem, we maximize the total prize gained by reconnecting disconnected network components within a specified time limit. The solution should determine the coordinated routes of each work troop starting at a depot node such that none of the closed roads can be traversed unless their unblocking/clearing procedure is finished. We develop an exact Mixed Integer Program (MIP) and a matheuristic method. The matheuristic solves single vehicle problems sequentially with updated prizes. To obtain an upper bound, we first relax the timing elements in the exact formulation and then solve its relaxed MIP, which decomposes into single vehicle problems, by Lagrangian Relaxation. We show the effectiveness of the proposed methods computationally on both random Euclidean and Istanbul road network data generated with respect to predicted earthquake scenarios

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: A review of the past, present, and future

[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberán, Á.; Eglese, R.; Hasle, G.; Plana, I.; Sanchís Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577

Multi-Scale Models for Transportation Systems Under Emergency Conditions

In recent years natural disasters have caused significant disruptions to transportation systems, which had to cascade negative impacts on humanitarian operations, related infrastructure, and associated industries in the affected areas. How to prepare for and respond to transportation system disruptions is a complex decision incorporating a variety of factors, from system use to system preparation. To address these challenges, the project team has developed optimization models for flight rescheduling and road restoration after a natural disaster and integrated the models as a decisionmaking tool. The data of North Carolina emergency response activities, air flights, and road closures during Hurricane Matthew were used to test the models and tool. The testing results show that the integrated tool can quickly find optimal sets and sequences for road restoration and flight schedules recovery at an affected airport and 50 counties. The tool can also visualize the damaged connections between counties, airports and resource centers, and the road restoration schedule and flight schedules recovery plan. The optimization models and decision-making tool developed in this project can support deploying effective restoration and recovery of transportation systems during an emergency event, which can improve the mobility of people and disaster relief under emergency