Coordinating debris cleanup operations in post disaster road networks

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. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(3), 144–194 2017

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“…Debris clearance in an arc routing context is also addressed in references [3,4,37,117,153,171,199], as detailed below.…”

Section: Post-disaster Operationsmentioning

confidence: 99%

“…Ozdamar and Aksu develop a constructive heuristic to identify the routes for a limited number of bulldozers to unblock roads within time‐dependent travel times based on road conditions. A cumulative network accessibility is used to account for dynamic road conditions.…”

Section: Arc Routing Applicationsmentioning

confidence: 99%

An updated annotated bibliography on arc routing problems

Mourão

Pinto

2017

Networks

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“…Two goals are considered, one imaximizing the cumulative network accessibility during the debris clearing, and the other one minimizing time to debris clearing. They formulate this NP-hard problem as a recursive Mixed Integer Program and suggest some heuristic methodologies [19]. Note that the study focuses on debris removal rather than emergency relief transportation.…”

Section: Emergency Relief Transportation Literaturementioning

confidence: 99%

Debris removal during disaster response: A case for Turkey

Sahin

Kara

Karaşan

2016

Socio-Economic Planning Sciences

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Debris occurs from the ruin and wreckage of structures during a disaster. Proper removal of debris is of great importance because it blocks roads and prohibits emergency aid teams from accessing disasteraffected regions. Poor disaster management, lack of efficiency and delays in debris removal cause disruptions in providing shelter, nutrition, healthcare and communication services to disaster victims, and more importantly, result in loss of lives. Due to the importance of systematic and efficient debris removal from the perspectives of improving disaster victims quality of life and allowing the transportation of emergency relief materials, the focus of this study is on providing emergency relief supplies to disasteraffected regions as soon as possible by unblocking roads through removing the accumulated debris. We develop a mathematical model for the problem that requires long CPU times for large instances. Since it is crucial to act quickly in an emergency case, we also propose a heuristic methodology that solves instances with an average gap of 1% and optimum ratio of 80.83%.

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“…In addition, there are various measure indicators to help allocate resources. The effectiveness of limited resource allocation can be measured by minimizing system cost and maximizing system flow [ 17 ]; maximizing network accessibility [ 18 ]; minimizing user travel costs [ 19 ]; minimizing the rescue costs of primary and secondary disasters [ 20 ]; maximizing cumulative network accessibility and minimizing make span [ 21 ]; optimizing accessibility [ 22 ]; minimizing the travel time of travelers, total working time, and idle time between work troops [ 23 ]; minimizing combinatorial indicators [ 24 ]; maximizing the performance of emergency rehabilitation; minimizing the risk of rescuers and maximizing the saving of lives [ 25 ]; and minimizing unsatisfied demands for resources, time to delivery, and transportation costs [ 26 ] among others.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm

Xiong

Ding

et al. 2016

PLoS ONE

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The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration.

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Coordinating debris cleanup operations in post disaster road networks

Cited by 65 publications

References 16 publications

An updated annotated bibliography on arc routing problems

An updated annotated bibliography on arc routing problems

Debris removal during disaster response: A case for Turkey

An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm

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