Redesign strategies of a comprehensive robust relief network for disaster management

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.     

A logistics model for emergency supply of critical items in the aftermath of a disaster

This paper proposes a logistics model for delivery of prioritized items in disaster relief operations. It considers multi-items, multi-vehicles, multi-periods, soft time windows, and a split delivery strategy scenario, and is formulated as a multi-objective integer programming model. To effectively solve this model we limit the number of available tours. Two heuristic approaches are introduced for this purpose. The first approach is based on a genetic algorithm, while the second approach is developed by decomposing the original problem. We compare these two approaches via a computational study. The multi-objective problem is converted to a single-objective problem by the weighted sum method. A case study is presented to illustrate the potential applicability of our model. Also, presented is a comparison of our model with that proposed in a recent paper by Balcik et al. [6]. The results show that our proposed model outperforms theirs in terms of delivering prioritized items over several time periods.     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.     

工程抗灾设防标准的优化

合理确定抗灾设防标准是抗灾工程设计中的关键问题。本文结合抗灾工程的失效特点，提出了模糊失效准则，并对灾害破坏等级进行模糊划分；同时，针对灾害发生的随机性，分析了任一设防标准下可能出现的损失情况，定义了过设损失，并提出最优设防标准化的目标函数中应同时考虑失效损失和过设损失。     

Filtering and identification of Heston's stochastic volatility model and its market risk

We study the filtering problem for the stochastic volatility model of Heston by using the nonlinear estimation theory. To solve the estimation problem for the stochastic volatility process, we use the random time change method. The derived basic equation for the filtering is the so-called Zakai equation and its numerically realized algorithm is proposed with the aid of the splitting-up method. Regarding the European call option problem, the identification of the market price of the volatility risk is also studied. Some numerical simulation studies are demonstrated to show the advantage of the proposed method.     

Debris removal during disaster response: A case for Turkey

Debris occurs from the ruin and wreckage of structures during a disaster. Proper removal of debris is of great importance because it blocks roads and prohibits emergency aid teams from accessing disaster-affected regions. Poor disaster management, lack of efficiency and delays in debris removal cause disruptions in providing shelter, nutrition, healthcare and communication services to disaster victims, and more importantly, result in loss of lives. Due to the importance of systematic and efficient debris removal from the perspectives of improving disaster victims quality of life and allowing the transportation of emergency relief materials, the focus of this study is on providing emergency relief supplies to disaster-affected regions as soon as possible by unblocking roads through removing the accumulated debris. We develop a mathematical model for the problem that requires long CPU times for large instances. Since it is crucial to act quickly in an emergency case, we also propose a heuristic methodology that solves instances with an average gap of 1% and optimum ratio of 80.83%.     

Hospital capacity planning for disaster emergency management

Hospitals are an integral part of a society's critical functions designed to respond to man-made and natural disasters. Effective hospital capacity planning can significantly enhance the capability and effectiveness of treatment for emergency patients with injuries resulting from a disaster. Such information can be used for patient/ambulance routing, resource planning, and emergency operations management. In the current paper, we develop a generic simulation model that is capable of representing the operations of a wide range of hospitals given an earthquake disaster situation. Using results from our simulations, generalized regression equations are fitted to obtain steady-state hospital capacities. A parametric metamodel is then developed to predict transient capacity for multiple hospitals in the disaster area in a timely manner, as demanded by emergency operations management for guiding the routing and treatment of injured people.     

A dynamic model for disaster response considering prioritized demand points

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.     

Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios.     

Using announcement options in the bid construction phase for disaster relief procurement

This paper presents an analysis of the bid construction phase of procurement auctions in disaster relief and humanitarian logistics. Substitution and partial fulfillment options are presented in formulations to allow bidders with fewer inventories to offer substitute item types and partial bids in auctions. During the auction announcement phase, a coordinating platform for disaster locations (i.e., auctioneer) allows substitution and partial fulfillment options to the relief suppliers (i.e., bidders) when acceptable. Thus, suppliers with fewer inventories can offer substitute item types and participate in more auctions by partially bidding. A genetic algorithm, a simulated annealing algorithm and an integer program are used for the analysis of the bid construction phase with different announcement options. Heuristic solution techniques and an IP formulation help understand the dynamics of the bid construction problem. It is shown that the addition of substitution and partial fulfillment options is essential to diversify and increase the usable capacity of the supplier base. Additionally, the partial fulfillment option enables better usage of supplier inventories in an environment with scarce supplies.     

Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments

In this paper, we study the problem of D-optimal experimental design under two linear constraints, which can be interpreted as simultaneous restrictions on the size and on the cost of the experiment. For computing a size- and cost-constrained approximate D-optimal design, we propose a specification of the “barycentric” multiplicative algorithm with sequential removal of redundant design points. We analytically prove convergence results for the proposed algorithm and numerically demonstrate its favorable properties compared to competing methods.     

Pre-positioning hurricane supplies in a commercial supply chain

Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.     

非线性互补问题计算机实现

分析了非线性互补问题求解困难，利用粒子群算法并结合极大熵函数法给出了该类问题的一种新的有效算法。该算法首先利用极大熵函数将非线性互补问题转化为一个无约束最优化问题，然后应用粒子群算法来优化该问题，计算机程序实现表明该算法是有效的。     

基于模拟退火遗传混合算法的指派问题研究

根据指派问题的特点建立数学模型．提出了以模拟退火算法和遗传算法相结合的思想及其解决方案。通过算例表明,使用此算法解决指派问题,提高了搜索效率,能够在短时间内找到最优分配方案,证明该算法是可行的。     

一类VRP问题的改进PSO算法求解

VRP问题是物流领域的热点研究问题。在对一类典型的VRP问题建立了数学模型,提出了一种改进粒子群优化算法以求解该模型。算法针对问题设计了顺序编码方案,并引入了局部搜索以提高算法的局部搜索能力。仿真结果表明了所提离散粒子群优化算法求解此类VRP问题的有效性。     

The complexity of linear programming

The simplex method for linear programming has always been very successful from a practical point of view. In the worst case, however, the method may require a computational effort that increases exponentially with problem size. Recently L.G. K hachian proposed an entirely different solution method whose running time is bounded by a polynomial function of problem size, thereby settling a major open problem in computational complexity theory. We review the developments preceding K hachian 's discovery, describe the algorithm and discuss its implications.     

小型水利工程在山地灾害防治中的运用

文章分析了山地灾害的特点以及山区小型水利工程建设中存在的主要问题,提出了加强山区小型水利工程管理的措施及避灾对策,介绍了山区山地灾害防治中常用的各类小型水利工程,并探讨了推广小型水利工程治理山地灾害需要解决的问题。     

基于混合微粒群优化算法的配送中心选址问题求解

将微粒群优化算法和模拟退火算法结合．针对配送中心选址问题．构造了微粒表达方法。提出了此问题的一种混合微粒群优化算法。通过整数规范化。微粒群能在整数空间内对问题进行优化求解。该算法能克服基本微粒群优化算法精度较低，易发散的缺点，有较高的搜索效率。经过实验仿真，与基本微粒群优化算法、遗传算法进行比较．证明了该算法的有效性。     

神经网络在农民收入预测中的应用

农村问题的核心是农民问题,而农民问题的焦点是收入问题。将神经网络的BP算法引入我国农民收入研究领域,并应用神经网络工具对我国农村居民家庭人均纯收入进行了预测研究。该预测模型在算法的收敛速度和预测精度方面都达到了较好的效果,对未来我国农民收入的研究具有重要意义,最后提提出了促进我国农民收入增长的各项对策和建议。