A multi-stage stochastic programming model for relief distribution considering the state of road network

Hu, Shaolong; Han, Chuanfeng; Dong, Zhijie; Meng, Lingpeng

doi:10.1016/j.trb.2019.03.014

Cited by 75 publications

(34 citation statements)

References 38 publications

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“…Additionally, different risk measures have been highlighted to show the reliability of solutions. A progressive hedging algorithm was applied by Hu et al [6] to solve a multistage stochastic programming model that considered uncertain and dynamic road capacity. Wang et al [18] investigated a time-dependent speed green vehicle routing problem.…”

Section: Related Workmentioning

confidence: 99%

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period postdisaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a realworld instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.INDEX TERMS Emergency service, uncertain environment, humanitarian logistics, Pareto optimization, robustness, multiphase scheduling.

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Section: Related Workmentioning

confidence: 99%

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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“…This idea is exploited among others in the progressive hedging algorithm (see e.g. [33], [14] and [22]) and in Lagrangian based heuristics (see e.g. [5] and [38]).…”

Section: Solving the Flow Refueling Location Problemmentioning

confidence: 99%

“…The charging stations have thus to be easily accessible when and where needed, and the charging time should be limited to a few minutes. Currently, the available fast charging technology allows a driver to recharge his battery within [20][21][22][23][24][25][26][27][28][29][30] minutes. However, it is far less widespread than the slow charging technology.…”

Section: Introductionmentioning

confidence: 99%

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Kadri

Perrouault

Boujelben

et al. 2020

Computers & Operations Research

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Electric vehicles (EVs) represent one of the promising solutions to face environmental and energy concerns in transportation. Due to the limited range of EVs, deploying a charging infrastructure enabling EV drivers to carry out long distance trips is a key step to foster the widespread adoption of EVs. In this paper, we study the problem of locating EV fast charging stations so as to satisfy as much recharging demand as possible within the available investment budget. We focus on incorporating two important features into the optimization problem modeling: a multi-period decision making horizon and uncertainties on the recharging demand in terms of both the number of EVs to recharge and the set of long-distance trips to cover. Our objective is to determine the charging stations to be opened at each time period so as to maximize the expected value of the satisfied recharging demand over the entire planning horizon. To model the problem, we propose a multi-stage stochastic integer programming approach based on the use of a scenario tree to represent the uncertainties on the recharging demand. To solve the resulting large-size integer linear program, we develop two solution algorithms: an exact solution method based on a Benders decomposition and a heuristic approach based on a genetic algorithm. Our numerical results show that both methods perform well as compared to a standalone mathematical programming solver. Moreover, we provide the results of additional simulation experiments showing the practical benefit of the proposed multi-stage stochastic programming model as compared to a simpler multi-period deterministic model.

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“…Deterministic (e.g., [6]) and stochastic optimization models (e.g., [7][8][9]) have been proposed for routing relief aid after a disaster, either comprising stand alone decisions (e.g., [6] and [9]) or together with facility location decisions (e.g., [7] and [8]). In particular, Noyan et al [7] proposed a stochastic programming model for designing last mile relief networks, which includes routing decisions, and the uncertainty is on road travel times and demand.…”

Section: Routing In Disaster Logisticsmentioning

confidence: 99%

Online optimization of first-responder routes in disaster response logistics

Shiri¹,

Salman²

2020

IBM J. Res. & Dev.

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After a disaster, first responders should reach critical locations in the disaster-affected region in the shortest time. However, road network edges can be damaged or blocked by debris. Since response time is crucial, relief operations may start before knowing which edges are blocked. A blocked edge is revealed online when it is visited at one of its end-nodes. Multiple first-responder teams, who can communicate the blockage information, gather initially at an origin node and are assigned to target destinations (nodes) in the disaster-affected area. We consider multiple teams assigned to one destination. The objective is to find an online travel plan such that at least one of the teams finds a route from the origin to the destination in minimum time. This problem is known as the online multi-agent Canadian traveler problem. We develop an effective online heuristic policy and test it on real city road networks as well as randomly generated networks leading to instances with multiple blockages. We compare the performance of the online strategy with the offline optimum and obtain an average competitive ratio of 1.164 over 70,100 instances with varying parameter values.

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A multi-stage stochastic programming model for relief distribution considering the state of road network

Cited by 75 publications

References 38 publications

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Online optimization of first-responder routes in disaster response logistics

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