Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake

Wang, Haijun; Du, Lizhi; Ma, Shenglin

doi:10.1016/j.tre.2014.06.006

Cited by 179 publications

(121 citation statements)

References 30 publications

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“…(d) Accurate, real-time relief demand information is required but almost inaccessible. Therefore, the existing studies are classified into two categories: (1) humanitarian or DOM during the response stage, especially for emergency logistics planning (Caunhye et al 2016;Gutjahr and Nolz 2016;Wang et al 2014) and (2) disaster dynamic prediction and evaluation of the emergency level (Sheu 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Emergency logistics for wildfire suppression based on forecasted disaster evolution

2017

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This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces).

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Section: Literature Reviewmentioning

confidence: 99%

Emergency logistics for wildfire suppression based on forecasted disaster evolution

2017

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“…In terms of material shortage in the initial stage of emergency material dispatch, Najafi et al [16] proposed a stochastic model to manage emergency materials and developed a robust optimization method to ensure that the dispatching scheme was applicable to all earthquake situations. Wang et al [17] constructed a nonlinear integer programming model by considering emergency time, emergency cost, and batch transportation reliability. The model adopted a non-dominant-sorted genetic algorithm and non-dominant sorting of differential evolution algorithm.…”

Section: State Of the Artmentioning

confidence: 99%

Relief Supplies Delivery Based on Path Connectivity Reliability

Hu¹,

Fu²,

Cheng³

et al. 2017

JESTR

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To guarantee the safe and reliable delivery of relief supplies to disaster areas, a relief delivery model based on path connectivity reliability is proposed. In the study, the connectivity reliability of each path under various disruption levels and road grades was identified with Delphi method, and the possibility Goal Oriented (GO) method was introduced to identify the connectivity reliability of the delivery network. Based on the connectivity reliability of the network, a penalty parameter was introduced. Considering the uncertain influence of disruption on road network reliability, the interval number and triangular fuzzy number were used to indicate the uncertain delivery time and unit material delivery cost, respectively. A multi-objective model based on path connectivity reliability was established. Through the case study and result comparison, the study's improved model was proved to be superior. Results show that the delay times of the model and traditional model is 0. Moreover, the delivery costs of the new model and traditional model are 17,586 and 17,340, respectively. Although the delivery cost of the new model is greater, its demand satisfaction rate is much higher than that of traditional models. Thus, the new model can significantly guide relief delivery when the road condition is affected by disruptions.

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“…Constraints (4) and constraints (5) represents that in addition to the last service point for each vehicle, each disaster point can only receive a rescue service; Constraints (6) represents that the amount of material taken away by vehicles is not greater than the amount of material from rescue points; Constraints (7) represents that the amount of material taken away by vehicles must be more than the demand of all disaster points; Constraints (8) represents the amount of material that the vehicle unloads each time; Constraints (9) represents the demand for the last service point of each vehicle must be met; Constraints (10 )represents the time the vehicles arrive at disaster points.…”

Section: Problem and Model Buildingmentioning

confidence: 99%

“…According to the analysis of the change in demand and road network, Bard proposed the method of vehicle routing choice based on nonlinear utility function [1]; Nesrin considered the effects of dynamic traffic information in emergency rescue route selection [2]; Based on bi-objective model, Victor put forward the vehicle selection strategy [3]; In order to minimize the transportation cost and distribution fairness, Burcu set up a mixed integer programming model [4]; Wang studied the open vehicle routing problem under emergency logistics [5]; Alberto studied the location and vehicle routing problem in drug delivery [6]; Sascha studied the dynamic problem of emergency rescue vehicle routing [7]; Lei studied vehicle routing problem in random demand environment [8]. Based on the discussion above, there are few studies on the rescue effect in the present literature.…”

Section: Introductionmentioning

confidence: 99%

Model for Bi-objective emergency rescue vehicle routing optimization

Yang¹

2017

AIP Conference Proceedings

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Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake

Cited by 179 publications

References 30 publications

Emergency logistics for wildfire suppression based on forecasted disaster evolution

Emergency logistics for wildfire suppression based on forecasted disaster evolution

Relief Supplies Delivery Based on Path Connectivity Reliability

Model for Bi-objective emergency rescue vehicle routing optimization

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