A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

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.     

Airfreight forwarder’s shipment planning under uncertainty: A two-stage stochastic programming approach

This paper addresses the shipment planning problem with random processing times in intermodal logistics via transfer ports. Shipment activities are divided into two groups according to regional settings. Activity processing times in region A are assumed to be random while those in region B are deterministic. At the beginning (stage 1), the forwarder assigns agents to all job activities (planning decision). In case a shipment delay is observed, an in-process adjustment (recourse decision) is implemented (stage 2). A two-stage stochastic programming model is established and an illustrative example is discussed. Managerial insights are presented in a simulation study.     

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.     

Environmental complexity related information for the assessment of country logistics environments: Implications for spatial transaction costs and foreign location attractiveness

With on-going fragmentation and distribution of production and consumption across geographical space, information about national logistics systems is becoming increasingly important to decision makers in internationally operating firms and organisations. While continuing research on country logistics assessment generates some of this information, its relevance for the decision makers, and relationship to their unpredictability from foreign national logistics systems remains indefinite.This paper identifies and categorises the relevant, available information on country logistics environments by using a content analysis approach. We demonstrate the immensity and nature of this information, are able to confirm the changing spatial transaction cost structures, and to reflect upon the overall conditions of information-related complexity and globalisation in the environment. Besides making a contribution to the further development of the spatial impedance factors and measures that form the basis for decision maker unpredictability, the paper offers an alternative approach for measuring the importance of transportation costs in industrial location, and offers a useful lens to distinguish between the emerging literatures on national logistics systems. Finally the paper also offers practical implications on location attractiveness for investment promotion agencies and other public sector agencies for business development and cluster growth.     

应急物流配送问题的蚁群聚类算法研究

提出了一种用于解决突发事件下,物流配送多目标优化问题的蚁群聚类优化算法。突发事件下的物流配送规划一般包含两方面内容,将救灾物资运往受灾地区和将灾区的伤员及时送至各医疗点。将多目标问题转化为单目标问题,结合蚁群的墓地构造行为特点,利用改进LF蚁群聚类模型,以节点需求未得到满足的不满意度最小和路由时间最短为优化目标,用LF蚁群聚类方法按约束条件进行聚类,最终确定车辆路由线路。     

Real-time mobilization decisions for multi-priority emergency response resources and evacuation groups: Model formulation and solution

This paper presents a model formulation and solution for simultaneous mobilization destination, traffic assignment, and departure schedule for multi-priority groups (SMDTS-MPG) for real-time emergency response in no-notice disasters. The presented approach addresses the decision context in which multiple emergency responses and evacuation flow groups with different destinations and varying priorities coexist in the same traffic network, within which simultaneous mobilization strategies must consider this requirement. The proposed modeling technique and the cell transmission model (CTM)-based linear-programming model provides a mechanism to accomplish this goal in an intuitive and coherent manner. The proposed matrix formulation of the SMDTS-MPG model permits the rapid deployment of the model to large networks. Model formulation and numerical examples are presented in detail in this paper.     

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 multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions.     

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%.     

A three-stage stochastic facility routing model for disaster response planning

This paper presents a three-stage mixed-integer stochastic programming model for disaster response planning, considering the opening of local distribution facilities, initial allocation of supplies, and last mile distribution of aid. The vehicles available for transportation, the state of the infrastructure and the demand of the potential beneficiaries are considered as stochastic elements. Extensive computational testing performed on realistic instances shows that the solutions produced by the stochastic programming model are significantly better than those produced by a deterministic expected value approach.     

A robust hybrid decision model for evaluation and selection of the strategic alliance partner in the airline industry

Owing to the cut throat competition and economic uncertainty in the market, airlines are focusing on strategic alliances for satisfying customer needs, especially in the current time which is dominated by global integration, demanding customer and fast changing technologies. This strategy is widely adopted by airlines. However, the selection of strategic alliance partner is a very decisive decision, and this selection process engrosses a number of complex processes which is result of compound reflection of associated various factors. In addition, the decision makers may be inconsistent in their views and preferences, arising due to imperfect information or intrinsic conflict between various departments. This paper presents a model based approach of an analytic hierarchy process (AHP) for evaluation of criteria and fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) for the selection of strategic alliance partner. A case of Indian airline industry demonstrates the application of the proposed approach. Eventually, robustness of the model is demonstrated by sensitivity analysis.     

Demand clustering in freight logistics networks

Demand clustering in freight logistics networks is an important strategic decision for carriers. It is used to incorporate new business to their networks, detecting potential economies, optimizing their operation, and developing revenue management strategies. A specific example of demand clustering is truckload combinatorial auctions where carriers bundle lanes of demand and price them taking advantage of economies of scope. This research presents a novel approach to cluster lanes of demand. Community detection is used to cluster the emergent network finding profitable collections of demand. Numerical results show the advantages of this method.     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

Multi-level inventory management decisions with transportation cost consideration

In this article we address specific inventory management decisions with transportation cost consideration in a multi-level environment consisting of a supplier–warehouse–retailers. We develop two models – namely, decentralized ordering model and centralized ordering model to investigate the effect of collective ordering by retailers on the total inventory cost of the system. A numerical study shows that the proposed model is robust and generates reasonable cost savings. The models have potential in several multi-level applications such as fresh or frozen food delivery to stores of different supermarkets or the supply of medicine to a number of hospitals from a wholesaler.     

面向电子供应链的企业物流信息系统模型研究

面向电子供应链的企业物流信息系统的开发和建设,有助于实现企业、供应商、销售商之间的协调发展,减少库存,降低操作成本,缩短交易时间等。根据电子供应链的网络环境特点,在研究制造业企业物流一般流程的基础上,根据主、辅制造商物流流程,提出企业物流信息系统的参考模型结构设计。     

Consolidation effects and inventory portfolios

This paper presents a formal analysis of the consolidation effect in a wider perspective. While demonstrating that the stock saving structure depends on the interaction between the coefficient of variation of demand and the ratio between inventory ordering and holding costs, this research indicates that the ratio between the standard deviations of lead time at potential facilities is the key variable for consolidation. Sensitivity analyses are also performed to address common managerial issues, which can arise during the consolidation decision, such as the impact on total costs and the assumption of uncorrelated demands.     

The economic importance of the Straits of Malacca and Singapore: An extreme-scenario analysis

This paper proposes a decision tree model to estimate the loss to global economy on the hypothesis of an extreme scenario of blockade of the Straits of Malacca and Singapore. The insurance surcharges, inventory costs and the time values of cargoes, and Time Charter Equivalent rate are used to estimate the psychological loss, the loss to industries, and the loss to carriers, respectively. Interestingly, there is a pseudo-paradoxical phenomenon with respect to the loss to carriers. An illustrative example is also provided to explain the “Malacca Paradox”.     

Toll financing in Norway: The success,the failures and perspectives for the future

Public funds for road investments have been subjected to constraints in many countries over the past three decades. Many governments have therefore resorted to financing road infrastructure using tolls. This article investigates what the future may offer with respect to toll financing in Norway. The changing environment in which transport planning takes place requires adaptations such as transforming tolls into congestion pricing schemes. Lessons from Norway may be useful for decision makers in Europe and elsewhere who are concerned with toll financing.     

Replenishment and lead time decisions in manufacturer–retailer chains

This study investigates the decisions of replenishment and lead-time reduction for a single-manufacturer multiple-retailer integrated inventory system in which the probability distribution of demand for each retailer is unknown but its mean and variance are given. A decision model is presented and a minimax distribution free procedure is applied to determine the lead time, the common shipment cycle time, the target levels of replenishments and the number of shipments per production cycle so that the expected total system cost can be minimized. A decision support system has been implemented on a personal computer to illustrate the application of the model.