Robust post-disaster route restoration

Caunhye, Aakil M.; Aydin, Nazli Yonca; Düzgün, Şebnem

doi:10.1007/s00291-020-00601-0

Cited by 19 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therein, the authors propose a robust optimization framework for planning routes to assess the status of the situation accounting for travel time uncertainty. In [9], the authors also propose a robust optimization framework for selecting routes to restore in efforts to accommodate humanitarian relief operations. Another task that has attracted interest from the robust optimization community is the pre-positioning and locating of inventory that can be used to provide relief to affected areas [54,57].…”

Section: Review Of Relevant Researchmentioning

confidence: 99%

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

Faiz,

Vogiatzis,

Liu

et al. 2024

Networks

View full text Add to dashboard Cite

Providing first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.

show abstract

Section: Review Of Relevant Researchmentioning

confidence: 99%

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

Faiz,

Vogiatzis,

Liu

et al. 2024

Networks

View full text Add to dashboard Cite

show abstract

“…Sun, Li [32] proposed a novel scenario-based robust bi-objective optimization model to formulate the medical facility location, casualty transportation, and relief commodity allocation problem under uncertainty. Caunhye and Alem [33] developed a robust stochastic optimization to solve a realistic location-allocation model for humanitarian operations in Brazil under uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Social Sustainability-Oriented Multi-Period Medical Supply Adjustment in Response to Disasters

Zhang,

Chen

2023

IEEE Access

View full text Add to dashboard Cite

Before the occurrence of disasters, medical supplies should have been prepared at warehouses to support the infected cases. As the possible mismatch between initial medical supply preparedness and actual disaster situation, it would trigger off serious social outcomes. Thus, taking medical supply adjustment operations among the warehouses is an imperative task to ensure the sustainable development of society. However, a single-period logistics action with fully uncertain information usually leads to sub-optimal schemes. In this sense, multi-period planning for medical resource adjustment is required with the concern of social sustainability, where the uncertain information is promptly adjusted following real-time cases in different periods. With the socially sustainable goals, a multi-objective stochastic optimization model is developed to facilitate the multi-period sustainable relief supply adjustment problem, which can be divided into two subproblems. The first sub-problem contains two conflicting objective functions, where a specific ε-constraint method is developed. After that, a linearization approach is applied due to the nonlinearity of the proposed model. Finally, Yushu Earthquake is considered to validate the effectiveness of the proposed model. It is found that the multi-period planning for the relief supply adjustment problem outperforms compared with a single-period one in terms of social sustainability.

show abstract

“…While some researchers have considered only the minimization of the total evacuation distance (Yenice & Samanlioglu, 2020), other researchers considered the minimization of both the total evacuation distance and the individual evacuation distance (Xu et al, 2018). Since strong earthquakes could cause the damage of the road and the disruption of the traffic, route restoration was considered to be one of the most important priorities in disaster relief (Caunhye, Aydin & Duzgun, 2020). Recovery time is an important aspect of the route restoration research.…”

Section: Introductionmentioning

confidence: 99%

A Multi-objective Model for the Location of the Emergency Facilities and the Selection of the Rescue Path Based on the Restoration of the Damaged Edges

WANG¹,

Xu²,

Filip³

2022

STUD INFORM CONTROL

View full text Add to dashboard Cite

In this paper, an equivalence theorem is given, that is, in a weighted graph, the maximum value of the minimum edge weights of all minimal edge cut sets between two points is equal to the minimum value of the maximum edge weights of all paths between the two points. On this basis, the recovery time between two points in the network is defined from the perspective of the minimal edge cut set and the path, respectively, and the equivalence of the two definitions is shown. Two models are established to optimize the partial recovery of network functions, by taking the damaged road network after the earthquake as the scene, based on the definition of the network recovery time and the total network time. Model 1 includes three objective functions, which are the sum of the failure levels between all pairs of the demand points and the facilities serving them, the total network time and the total cost. Different priorities were set for solving these three objectives. Since such a hierarchical model is not conducive to the optimization of the objective functions with lower priorities, model 1 is modified appropriately. The sum of the failure levels is combined with the total network time into an integrated objective function by weighting, and the minimization of this new function is taken as the highest-priority objective and the minimization of the total cost is taken as the secondary-priority objective, in order to achieve the balance between the sum of the failure levels and the total network time. Thus, model 2 is obtained. A case study is used to test the two models and verify their effectiveness.

show abstract

Robust post-disaster route restoration

Cited by 19 publications

References 40 publications

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

Social Sustainability-Oriented Multi-Period Medical Supply Adjustment in Response to Disasters

A Multi-objective Model for the Location of the Emergency Facilities and the Selection of the Rescue Path Based on the Restoration of the Damaged Edges

Contact Info

Product

Resources

About