Determining the optimal decision time of relief allocation in response to disaster via relief demand updates

Relief allocation has recently attracted increased interest in research. Most studies in the literature have not focused on the trade-off between demand forecast accuracy and relief allocation efficiency. Thus, we employ the Bayesian updating framework, which allows the decision-maker to postpone his decision until sufficient accurate demand information is available. Different from the previous studies, we propose a novel method of relief demand updating, and devise a lost function as a function of time delay. Then, we develop a relief allocation model as an optimal stopping problem, to determine the optimal delay in response to typhoon disaster. Finally, the proposed model is applied to a case of post-disaster rescue. The computational results indicate the potential advantages of the model.     

基于需求更新的救灾品配送公平与效率协调模型

鉴于需求准确度与救灾品配送效率之间的悖反关系,研究了基于需求更新的救灾品配送效率与公平问题.通过对某市民政局进行调研,了解到台风灾害发生时的需求变化规律,设计一种通过已知需求灾区对未知需求灾区的需求进行贝叶斯更新的需求更新方式,由此建立救灾品配送模型.将该模型应用于某台风救灾实例,通过实例分析表明了该模型的求解速率与精度,并找到了最优"决策点",实现了救灾品配送公平与效率的平衡.     

Supply chain planning for hurricane response with wind speed information updates

This paper introduces a stochastic inventory control problem that is relevant to proactive disaster recovery planning as it relates to preparing for potential hurricane activity. In particular, we consider a manufacturing or retail organization who experiences demand surge for items such as flashlights, batteries, and gas-powered generators, where the magnitude of demand surge is influenced by various characteristics of an ensuing storm. The planning horizon begins during the initial stages of storm development, when a particular tropical depression or disturbance is first observed, and ends when the storm dissipates. Since hurricane characteristics can be predicted with more accuracy during the later stages of the planning horizon relative to the earlier stages, the inventory control problem is formulated as an optimal stopping problem with Bayesian updates, where the updates are based on hurricane predictions. A dynamic programming algorithm is described to solve the problem, and several examples involving real hurricane wind speed data are presented to illustrate the methodology.     

Bi‐objective stochastic programming models for determining depot locations in disaster relief operations

This paper presents two‐stage bi‐objective stochastic programming models for disaster relief operations. We consider a problem that occurs in the aftermath of a natural disaster: a transportation system for supplying disaster victims with relief goods must be established. We propose bi‐objective optimization models with a monetary objective and humanitarian objective. Uncertainty in the accessibility of the road network is modeled by a discrete set of scenarios. The key features of our model are the determination of locations for intermediate depots and acquisition of vehicles. Several model variants are considered. First, the operating budget can be fixed at the first stage for all possible scenarios or determined for each scenario at the second stage. Second, the assignment of vehicles to a depot can be either fixed or free. Third, we compare a heterogeneous vehicle fleet to a homogeneous fleet. We study the impact of the variants on the solutions. The set of Pareto‐optimal solutions is computed by applying the adaptive Epsilon‐constraint method. We solve the deterministic equivalents of the two‐stage stochastic programs using the MIP‐solver CPLEX.     

需求与物流网络不确定下的应急救援选址问题

针对应急物流中需求与物流网络的不确定性特征,对应急救援中应急需求和物流网络均不确定条件下的应急配送中心选址问题进行研究,以成本最小化为目标,建立基于集合覆盖的应急救援设施选址的随机规划模型,采用期望值法和随机模拟两种方法处理数学模型中的不确定性。通过算例与仿真研究,获得应急救援下的配送中心选址最优方案。结果表明,相对于传统的期望值方法处理随机参数,随机模拟方法具有较明显优势。     

Vehicle routing and resource distribution in postdisaster humanitarian relief operations

After a disaster, supplies must be efficiently and equitably distributed to those in need, wounded persons must be evacuated to triage centers, and relief workers must be transported to affected areas. This complex humanitarian relief problem requires the coordination of numerous vehicles of varying capacities to transport goods, disaster victims, and volunteer workers through a network of roads, some of which may be impassable. To address this problem, a detailed mathematical programming model is presented. Owing to the complexity of this formulation, only small‐scale problem instances may be solved optimally via commercial solver software. Therefore, a new heuristic approach is proposed to solve problems of practical size within acceptable time restrictions. The performance of the heuristic is evaluated for numerous representative test instances.     

流域型灾害特征信息提取及动态预警算法

针对目前流域型灾害特征信息提取和动态预警方法不能满足实践需要的现实问题,为了提升流域型灾害特征信息提取和灾害动态预警技术水平,以典型流域型灾害为研究对象,剖析了流域型灾害性能参数计算及动态预警实现的核心环节,提出了基于无线传感网络(WSN)的流域型灾害特征信息计算方法,设计了流域型灾害动态预警信息处理算法.以典型流域型灾害的性能参数采样值为数据,运用Matlab仿真平台对其核心性能参数进行了仿真分析.实验结果表明,所提算法能够有效捕捉流域型灾害的核心动态特征信息,提高了流域型灾害动态预警指示的准确度.     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

Post disaster adaptation management in airport: A coordination of runway and hangar resources for relief cargo transports

Air traffic congestions for processing relief cargos under post-disaster relief scenarios are common, due to high transport demands within a short time. To enhance the resilience of relief operations at airport, an optimization problem of relief air cargo transportations involving aircraft sequencing and loading/unloading within a designated hangar is studied in this paper. The objective is minimizing the tardiness in fulfilling inbound and outbound relief cargos. A mixed-integer linear programming model is formulated, which incorporates aircraft sequencing and hangar parking planning. To resolve the practical problem efficiently, we propose a two-stage optimization approach, which reduces complexity in solving the original model by coordinating the decisions of aircraft landing and take-off schedule and cargo hangar parking arrangement through iterations. The efficiency of the proposed method is examined through the computational results. High-quality solutions can be obtained by the two-stage optimization method within a reasonable time for practical implementation, which enhances the responsiveness and resource utilization of airport operations management under disaster relief situations.     

Cross-docking and milk run logistics in a consolidation network: A hybrid of harmony search and simulated annealing approach

In this paper, we develop a novel integer programming model for the transportation problem of a consolidation network where a set of vehicles are used to transport goods from suppliers to their corresponding customers via three transportation systems: direct shipment, shipment through cross-dock (indirect shipment) and milk run. Since the proposed problem formulation is NP-hard, we offer a hybrid of harmony search (HS) and simulated annealing (SA) based heuristics (HS-SA algorithm) in order to solve the problem. The objective of this problem is to minimize the total shipping cost in the network, so it is tried to reduce the number of required vehicles using an efficient vehicle routing strategy in the algorithm. Solving several numerical examples demonstrates that our solving approach performs much better than GAMS/CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for large size problem instances.