An emergency logistics distribution approach for quick response to urgent relief demand in disasters

Quick response to the urgent relief needs right after natural disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas, which remains challenging in the field of logistics and related study areas. This paper presents a hybrid fuzzy clustering-optimization approach to the operation of emergency logistics co-distribution responding to the urgent relief demands in the crucial rescue period. Based on a proposed three-layer emergency logistics co-distribution conceptual framework, the proposed methodology involves two recursive mechanisms: (1) disaster-affected area grouping, and (2) relief co-distribution. Numerical studies with a real large-scale earthquake disaster occurring in Taiwan are conducted, and the corresponding results indicate the applicability of the proposed method and its potential advantages. We hope that this study can not only make the proposed emergency logistics system available with more benefits to the development of emergency logistics systems for the urgent needs of disaster areas around the world but also stimulate more excellent researches concerning emergency logistics management.     

A stochastic programming approach for floods emergency logistics

This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events.     

A strategic planning model for the railway system accident rescue problem

The present study examines the location of emergency rescue problems for urban ambulance and railway emergency systems. The proposed model considers probabilistic rescue demand, independent busy fractions of ambulances, and the corresponding risk levels in railway segments. We formulate the proposed model using fuzzy multi-objective programming and solve it using a generic algorithm and a non-dominated sorting genetic algorithm-II. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the rescue resource location improves rescue effectiveness for railway rescue and urban medical service demand.     

A statistical approach to small area synthetic population generation as a basis for carless evacuation planning

Natural or man-made hazards that require evacuation put already vulnerable populations in a more precarious situation. However, when plans and decisions about evacuation are made, the assumption of access to a private car is typically made and differences in income levels across a community is rarely accounted for. The result is that carless members of a community can find themselves stranded. Low income carless residents need alternative transportation means to reach shelters in case of an emergency. Thus, evacuation plans, decisions and models need necessary information that identifies and locates these populations. In this paper, data from the American Community Survey, US Census, Internal Revenue Services and the National Household Travel Survey are used to generate synthetic population for Anne Arundel County, Maryland using the copula concept. Geographic locations of low-income residents are identified within each subarea of the county (census tract) and their car ownership is estimated with a binomial logit model. The developed population synthesis method will allow officials to have a more accurate account of disadvantaged populations for emergency planning and identify locations of shelters, triage points as well as planning carless transportation services.     

An integrated modeling framework for design of logistics networks with expedited shipment services

This paper studied an integrated logistics network problem that determines optimal supplier locations, assignments of these suppliers to terminal facilities, expedited shipment configurations, and inventory management strategies in an uncertain environment. We studied the problem structure and proposed mathematical models to determine the optimal network design that minimizes the expected total system cost. We developed a customized solution approach based on Lagrangian relaxation that can solve these models efficiently and accurately. Numerical examples are conducted to draw managerial insights into how problem settings and key parameter values affect the optimal design results.     

德国铁路应急救援体系经验借鉴及启示

德国铁路以国家应急救援体系为基础,建立起制度完善、组织协调、分工合理、装备先进的铁路救援体系,为我国铁路应急救援体系建设提供借鉴。通过分析德国应急救援组织及机构、铁路应急救援体系架构、铁路应急响应流程和铁路救援装备的现状,从应急救援顶层设计、快速应急救援响应流程、装备利用效率等方面,提出德国铁路应急救援体系经验。我国应从强化应急救援社会协同能力,优化突发事件应急响应流程,提高铁路救援起重机综合利用效率、提升应急救援装备水平等方面构建铁路应急救援体系。     

Ant colony optimization for disaster relief operations

This paper presents a meta-heuristic of ant colony optimization (ACO) for solving the logistics problem arising in disaster relief activities. The logistics planning involves dispatching commodities to distribution centers in the affected areas and evacuating the wounded people to medical centers. The proposed method decomposes the original emergency logistics problem into two phases of decision making, i.e., the vehicle route construction, and the multi-commodity dispatch. The sub-problems are solved in an iterative manner. The first phase builds stochastic vehicle paths under the guidance of pheromone trails while a network flow based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. The performance of the algorithm is tested on a number of randomly generated networks and the results indicate that this algorithm performs well in terms of solution quality and run time.     

基于多节点的物流选址规划研究

物流节点选址是在一个具有若干需求点的经济区域内选择一个地址设置物流点的规划过程。物流节点的数量和分布直接影响到该物流系统的服务成本及服务范围,因此物流节点的选址是物流系统规划中至关重要的环节。对多物流节点的选址问题,可利用鲍摩-瓦尔夫模型求解,通过举例对模型的运用进行了说明。     

A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation

This paper focuses on the planning of road restoration efforts during disaster response and recovery. The primary objective is to maximize network accessibility for all locations in the area during the restoration process so that survivors are evacuated and road side debris is removed as soon as possible. We propose a dynamic path based mathematical model that identifies criticality of blockages and clears them with limited resources. This model is more efficient than link based models and can solve restoration problems for realistic size networks within reasonable time. Algorithm performance is demonstrated using two instances based on districts in Istanbul.     

Intermodal logistics: The interplay of financial,operational and service issues

This research addresses strategic planning for an interregional, hub based, intermodal logistics network operated by a logistics service provider. A tabu search meta-heuristic is used to solve a mathematical optimization model that extends the p-hub median model for interacting hub location-allocation problems to the domain of intermodal logistics. An empirical study based on a subset of US freight flows shows that intermodal logistics networks differ significantly from traditional over-the-road logistics networks in their hub locations, network structure, and their use of direct and inter-hub shipments. Furthermore, intermodal logistics networks are more sensitive to changes in service requirements and costs.     

An integrated approach to modeling the impact of floods on emergency services: A case study of Calgary,Alberta

Floods are becoming more frequent and the magnitude of direct consequences, relating to destruction of critical infrastructure and loss of life, has highlighted the importance of flood management. This study proposes a methodology for quantifying the impact of predicted and historic flood events on emergency services. The approach moves beyond simple flood inundation mapping by accounting for the relationship between flood depth and vehicular speed. A case study is presented for Calgary, Alberta, where the depths of a predicted 100-year flood and a historic 2013 flood event are modeled. The methodology applies geographic information systems to flood depth mapping, utilizing digital elevation models, flood extents, and hydrological data. Flood depths are then assigned to links comprising the road network, where the maximum vehicle speed is calculated as a function of the standing depth of water on a link. The flooded network is used to derive service areas for several types of emergency services (emergency medical services (EMS), fire, and police), following targeted response times. The results locate and quantify the residential and work populations that no longer meet the targeted response times. During both flood scenarios, EMS is found to have the greatest reduction in accessibility, with 23% and 47% of residents and workers, respectively, not served. Fire services are seen to be more resilient with only 3% and 9% of residents and workers, respectively, not served. The results for police services are similar to fire services. However, the former have a greater range of response times, meaning these areas represent those that are completely isolated during both flood events. Overall, the integrated methodology quantifies vulnerable populations on a partially degraded network, the results of which can be used to develop evacuation plans and emergency response strategies.     

铁路物流节点系统结构的建立方法

在简述物流节点的功能及作用的基础上,通过抽取对铁路物流系统结构有重要影响的12个要素,由系统可达矩阵进行层次级别划分和结构分解,得出反映铁路物流系统结构的4级递阶结构,以保证铁路物流节点系统规划和研究的有序性,并指出上层结构为下层结构的子系统,得出由总体框架到系统核心问题的研究方法.     

Generating a spatial coverage plan for the emergency medical service on a regional scale: Empirical versus random forest modelling approach

Adequate spatial coverage by the emergency medical service and the ability to reach any location in the area of interest in the shortest possible time are crucial for the survival of patients with serious conditions. Knowledge of blind spots (i.e. sites that cannot be reached within the required time) represents key information for improving the service quality and may lead, e.g. to a relocation of bases or to other active interventions. Spatial coverage can be derived from experience based on historical data. Such an approach may be problematic if a larger area is being analysed, especially if data is not available for some parts of such areas or if no data is available. To eliminate such problems, we created a prediction model utilising the random forest ensemble learning method. The model is capable of predicting the travel time based on available historical data on ambulance movements (GPS) and the geometric and construction characteristics of individual road segments. We therefore collaborated with the regional public administration and emergency medical service authorities to deliver a time- and resource-efficient solution for emergency spatial planning practice. The outputs from the newly built model were subsequently validated against data from an empirical model currently used by the regional authorities. The results from both models were compared from the perspective of performance in various seasonal and time-of-day conditions. The prediction of travel times using the new model improved according to all the evaluated validation metrics. The importance and applicability of the foregoing model lies in the fact that it can be incorporated into the current emergency medical service management system in a simple manner in terms of data availability and the required computational resources. We conclude that the dynamic model presented in this paper represents an improvement relative to the reference data, and discuss the possibilities of further improving the proposed model.     

Coordinated aviation network resource allocation under uncertainty

Congestion in the air traffic system, both recurrent and non-recurrent, is typically handled by rationing access rights to individual resources such as airports or important parts of the airspace. Under the planning paradigm employed in the US, this rationing process occurs independently at each resource. The stochastic integer programming model proposed in this paper brings coordination to this process while considering capacity uncertainty. Results of a case study suggest that the model is tractable, and generates capacity allocations that improve efficiency and enable greater responsiveness to changing capacity conditions.     

Location factors for logistics facilities: Location choice modeling considering activity categories

While many past studies analyze the changes in the spatial distribution of logistics facilities and provide insights on the factors that drive the migration of logistics facilities, only a few studies analyze the factors of location choice at the individual facility level. Furthermore, the differences among various facility types, with respect to the key locational characteristics that attract logistics facility developments, are still not clear. This research attempts to fill the research gap by analyzing the location choices for logistics facility development. Using the data from the Paris Region, we characterize the locations and activities of logistics facilities and estimate a logistics facility location choice model for each activity category. The analysis reveals the key locational characteristics that influence logistics facility locations, such as zoning regulations, wholesale job accessibility, population density, and the accessibility to autoroute (controlled-access highway), as well as the heterogeneity in the effects of these characteristics by activity category. Zoning and traditional clusters play a significant role for the locations of newly developed logistics facilities, which underlines the importance of public policies for logistics land use in the Paris region. The effect of the accessibility to population is identified for the group of facilities which serve for retail shops and end-consumers only under the assumption that alternative locations are limited to specialized economic activity/logistics zones, indicating that population accessibility is a secondary factor to the designation as the specialized zone. Zoning changes to/from economic activity/logistics zones may cause significant impacts on the spatial distribution of logistics facilities, and thus, the distribution of goods vehicle traffic flow.     

Dynamic network design for reverse logistics operations under uncertainty

The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements. In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time. This paper proposes dynamic location and allocation models to cope with such issues. A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties. A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research. A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method.     

A systems-based approach to address unintended consequences of demand-driven transportation planning in national parks and public lands

In most cases, transportation planning in national parks and public lands might most appropriately be termed “demand-driven.” In this approach, rigorous analyses of park visitation, traffic, and parking data are used as a basis for transportation planning to accommodate current and projected future visitor demand, within financial constraints. Performance measures used to assess the quality of transportation systems in national parks are generally related to “moving people” efficiently. This approach is based on well-established principles for transportation planning in urban and rural communities. However, a demand-driven approach to transportation planning may not be suitable in national parks and public lands because it may enable levels of visitation that cause visitor crowding, resource impacts, and other unintended consequences. This paper introduces a more sustainable, systems-based transportation planning approach developed in the Rocky Mountain National Park (ROMO) to help the park operate its shuttle bus system efficiently and conveniently, and according to thresholds for visitor crowding and resource impacts at sites serviced by the shuttle system. The transportation planning approach developed in this study for ROMO is more suitable and sustainable for national parks and public lands than a demand-driven approach, and is readily adaptable to other locations. Correspondingly, the approach is now being applied in several other national parks and public lands recreation areas.     

当前物流基础设施建设和发展中值得注意的几个问题

根据我国现代物流基础设施的建设和发展情况,对物流基础设施的认识问题,物流基础设施的规划与布局问题,资源整合与土地合理利用问题,物流基础设施的投资、效益与成本问题,以及物流基础设施的交通、运输组织问题进行了调查研究,为推进现代物流基础设施的健康发展,提出相关政策建议。     

Dynamic relief-demand management for emergency logistics operations under large-scale disasters

This paper presents a dynamic relief-demand management model for emergency logistics operations under imperfect information conditions in large-scale natural disasters. The proposed methodology consists of three steps: (1) data fusion to forecast relief demand in multiple areas, (2) fuzzy clustering to classify affected area into groups, and (3) multi-criteria decision making to rank the order of priority of groups. The results of tests accounting for different experimental scenarios indicate that the overall forecast errors are lower than 10% inferring the proposed method’s capability of dynamic relief-demand forecasting and allocation with imperfect information to facilitate emergency logistics operations.     

A reverse logistics cost minimization model for the treatment of hazardous wastes

This study presents a cost-minimization model for a multi-time-step, multi-type hazardous-waste reverse logistics system. A discrete-time linear analytical model is formulated that minimizes total reverse logistics operating costs subject to constraints that take into account such internal and external factors as business operating strategies and governmental regulations. Application cases are presented to demonstrate the feasibility of the proposed approach. By using the proposed model coupled with operational strategies, it is shown that the total reverse logistics costs for the applications cases can be reduced by more than 49%.