Dynamic Vehicle Routing for Relief Logistics in Natural Disasters

Hsueh, Che-Fu; Chen, Huey-Kuo; Chou, Huey-Wen

doi:10.5772/5641

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…Vehicle routing, in particular, has been a point of interest and as such several optimization techniques have been proposed. Dynamic vehicle routing approach has been reported as efficient [3], but the instances tested are limited to 10 vehicles and hundred nodes. A similar size-related problem is seen in many other research papers [4], [5], [6].…”

Section: Nomenclaturementioning

confidence: 99%

Helicopter Mission Assignment in Disaster Relief Based on Particle Swarm Optimization

Andreeva-Mori

Kobayashi

Shindo

2015

AIAA Infotech @ Aerospace

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In the immediate aftermath of a large-scale disaster, optimal helicopter rescue mission assignment is critical to saving many lives. The current practice in the field in mostly human-centered, however. Japan Aerospace Exploration Agency has been developing a decision-support system for aircraft operation whose goal is prompt planning and execution of rescue missions. The current research focuses on evacuation missions in particular and investigates the potential of particle swarm optimization (PSO) in aircraft resource management. A robust particle model which can reflect various helicopter properties as well as evacuation mission characteristics is proposed. PSO parameters are modified and set based on numerical simulations and the values determined in this way are used in an optimization of disaster relief mission assignments based on real data obtained during the Great East Japan Earthquake and Tsunami in 2011. It is shown that PSO can be successfully adapted to disaster relief support systems and provide valuable analysis and decision-making information to the authorities in charge. Nomenclature= mission assignment coefficient 1,2,3 = acceleration coefficient ℎ = cost for the mission m being assigned to helicopter h (time [hours]) = distance from the helicopter base to mission m demand location [km] = total number of evacuation missions = number of evacuees in mission m who need to be transported = global best found at current iteration = global previous best found by the swarm so far = individual previous best found so far 1,2,3 = random functions with values uniformly distributed in [0,1] = acceleration coefficient s( ) = sigmoid function = rescue mission deadline [hours] ℎ = time necessary for one evacuee to board vehicle h [hours] ℎ = total time necessary for take-off and landing of helicopter h [hours]ℎ = Cruising speed of helicopter h [km/h] = particle velocity at iteration i = maximum particle velocity = particle position at iteration i = mission assignment model particle position, j th mission assigned to i th helicopter = inertia weight 1

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Section: Nomenclaturementioning

confidence: 99%

Helicopter Mission Assignment in Disaster Relief Based on Particle Swarm Optimization

Andreeva-Mori

Kobayashi

Shindo

2015

AIAA Infotech @ Aerospace

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“…Constraint that guarantee the whole supply for each location (except the distribution center) will be fulfilled by the available vehicles was given by (8).…”

Section: A Mathematicalmentioning

confidence: 99%

Robust Optimization for Handling Time Uncertainty in Capacitated Vehicle Routing Problem for Post-Disaster Aid Logistical Distribution

Achmad*,

Chaerani,

Lesmana

2020

IJRTE

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In post-disaster aid logistical distribution, time is the most important thing to minimize the number of victim. But, time travelling become uncertain as the impact of disaster occurrence. In this paper, Robust Capacitated Vehicle Routing Problem (RCVRP) for post-disaster aid logistical distribution under time uncertainty is discussed to handle the uncertain travelling time. The robust optimal solution derivation is presented using Robust Optimization. The time uncertainty is assumed to be lied in a box and a polyhedral uncertainty set. This assumption yields a Robust Counterpart (RC) of the RCVRP model which are computationally tractable. Case study and simulation presented in this paper and shows a robust optimal solution.

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“…Whenever network information is updated, a new VRP problem must be formulated as a 0-1 mixed integer programming problem and solved accordingly. Referring to Hsueh et al 2008 , the multidepot VRP problem at specific instant τ can be formulated as follows.…”

Section: Model Formulationmentioning

confidence: 99%

Real‐Time Vehicle Routing for Repairing Damaged Infrastructures Due to Natural Disasters

Chen¹,

Chou

et al. 2011

Mathematical Problems in Engineering

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We address the task of repairing damaged infrastructures as a series of multidepot vehicle-routing problems with time windows in a time-rolling frame. The network size of the tackled problems changes from time to time, as new disaster nodes will be added to and serviced disaster nodes will be deleted from the current network. In addition, an inaccessible disaster node would become accessible when one of its adjacent disaster nodes has been repaired. By the “take-and-conquer” strategy, the repair sequence of the disaster nodes in the affected area can be suitably scheduled. Thirteen instances were tested with our proposed heuristic, that is, Chen et al.'s approach. For comparison, Hsueh et al.'s approach (2008) with necessary modification was also tested. The results show that Chen et al.'s approach performs slightly better for larger size networks in terms of objective value.

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Dynamic Vehicle Routing for Relief Logistics in Natural Disasters

Cited by 20 publications

References 10 publications

Helicopter Mission Assignment in Disaster Relief Based on Particle Swarm Optimization

Helicopter Mission Assignment in Disaster Relief Based on Particle Swarm Optimization

Robust Optimization for Handling Time Uncertainty in Capacitated Vehicle Routing Problem for Post-Disaster Aid Logistical Distribution

Real‐Time Vehicle Routing for Repairing Damaged Infrastructures Due to Natural Disasters

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