Online routing and scheduling of search-and-rescue teams

Shiri, Davood; Akbari, Vahid; Salman, Serdar

doi:10.1007/s00291-020-00594-w

Cited by 22 publications

(6 citation statements)

References 54 publications

(72 reference statements)

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“…Recently, Shiri et al [20] studied a discrete search-and-rescue problem defined on a network where multiple searching agents aim to find multiple hiders which are positioned at nodes of the network. The hiders in this problem represent victims of a natural or man-made disaster.…”

Section: Related Search Problems On Networkmentioning

confidence: 99%

Online Failure Diagnosis in Interdependent Networks

Shiri

Akbari

2021

SN Oper. Res. Forum

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In interdependent networks, nodes are connected to each other with respect to their failure dependency relations. As a result of this dependency, a failure in one of the nodes of one of the networks within a system of several interdependent networks can cause the failure of the entire system. Diagnosing the initial source of the failure in a collapsed system of interdependent networks is an important problem to be addressed. We study an online failure diagnosis problem defined on a collapsed system of interdependent networks where the source of the failure is at an unknown node (v). In this problem, each node of the system has a positive inspection cost and the source of the failure is diagnosed when v is inspected. The objective is to provide an online algorithm which considers dependency relations between nodes and diagnoses v with minimum total inspection cost. We address this problem from worst-case competitive analysis perspective for the first time. In this approach, solutions which are provided under incomplete information are compared with the best solution that is provided in presence of complete information using the competitive ratio (CR) notion. We give a lower bound of the CR for deterministic online algorithms and prove its tightness by providing an optimal deterministic online algorithm. Furthermore, we provide a lower bound on the expected CR of randomized online algorithms and prove its tightness by presenting an optimal randomized online algorithm. We prove that randomized algorithms are able to obtain better CR compared to deterministic algorithms in the expected sense for this online problem.

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Section: Related Search Problems On Networkmentioning

confidence: 99%

Online Failure Diagnosis in Interdependent Networks

Shiri

Akbari

2021

SN Oper. Res. Forum

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“…The problem of saving victims that are trapped in various critical locations due to a disaster that occurred by allocating and routing the SAR team in the post disaster phase is addressed in [146]. In reflecting a realistic scenario, both dynamic and stochastic elements are presented in the proposed model.…”

Section: Exact Methods In Rescue Vrpsmentioning

confidence: 99%

“…Some works make use of the dispersion of disaster effects, as can be seen in [157] that link the road's condition based on circled areas of the affected region. Some works assume binary capacity of road, such as [114,146]. On the other hand, uncertain road capacity could be observed in [67,75].…”

Section: Current Trendsmentioning

confidence: 99%

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

et al. 2021

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The growing field of humanitarian operations is driven by frequent events of disasters seen in the world today. Within this field, Operations Research (OR) plays a critical role in alleviating the suffering of victims that are impacted by disasters. This paper focuses on the branch of a well-known OR problem, known as the Vehicle Routing Problem (VRP), within the selected scope of humanitarian operations. A total of 123 papers of the last decade are reviewed and classified under the humanitarian operations of supply and delivery, evacuation as well as rescue operations. Besides specific disaster management phases and disaster types, various modelling challenges are highlighted, hinting towards a richer and more complex VRP seen under selected model characteristic classifications. Furthermore, established solution approaches, including hybrid solutions, are highlighted and classified, discussing how they are applied in the context of these humanitarian operations. The inclusion of a machine learning solution approach under the same classification is proposed. Finally, the trend and future outlook of VRP for the suggested humanitarian operations are discussed and highlighted.

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“…In this study, the relief distribution teams are not able to recover these blocked edges and should find a way to bypass them when they are observed. Shiri et al (2020) studied a post-disaster heterogeneous multi-team search-and-rescue problem modeled on an undirected graph involving non-recoverable online blocked edges. In this study, the search-and-rescue time for each critical node is also online.…”

Section: Literature Reviewmentioning

confidence: 99%