A Multi-echelon Network Distribution Model For Emergency Resource Planning

Resource planning in emergency management is a challenging task as it involves disaster situations where the demands are rapidly increasing and the resources are scarce. Conventional planning involves a centralized network structure where resources are distributed through a few prepositioning facilities located near to the disaster regions. In this research, we develop a novel multi-echelon network distribution structure for emergency resource planning. The structure at its highest echelon consists of a set of potential Supply Points (SPs), where resources are purchased and consolidated which is more practical in comparison to the conventional centralized structure. SPs are considered as typically large facilities in metropolitan cities in and around the potential disaster region from where the resources are distributed to the prepositioning facilities in order to be able to supply the materials immediately after the disaster in the area. The proposed structure also allows direct shipment of resources from SPs to the disaster regions in the response stage which is more close to the reality. Under the network structure, we formulate a new two-stage stochastic mixed integer programming model for an integrated emergency preparedness and response planning. The objective is to obtain the optimal allocations of the resources along with locations of the SPs and prepositioning facilities to satisfy the demand of disaster victims in a timely and cost-effective manner. We assume demand for supplies in the disaster hit areas are aggregated at locations called Aggregated Demand Points (ADPs). For the current study, the demands at the ADPs are obtained with a set of disaster scenarios each with a probability of occurrence. To develop the resource allocation model, we consider two distribution stages that are decided simultaneously: pre-disaster and post-disaster stages. In the pre-disaster stage, the analysis provides the location of SPs and the pre-disaster purchasing amounts to be acquired at the SPs. All or part of the purchased resources are positioned in prepositioning facilities located at selected ADPs. In the post-disaster stage, detail distribution of the resources to satisfy demands following the disaster event is considered. The demands in the post-disaster stage are met through pre-positioned resources at the prepositioning facilities and additionally, if required, through the direct shipments of resources from the SPs. We consider limited post-disaster purchasing opportunities at SPs as the quantities are to be purchased during a short and chaotic period. The optimization model proposed in this research consists of logistics and deprivation costs. The logistics costs include cost of provisioning, prepositioning, and delivering the resources. The deprivation costs represent the cost of not providing or delays in providing the supplies at the point of demand. The model is tested in a network for numerical analysis. The result shows that multiple SPs in the proposed network distribution structure helps to overcome the possible resource disruption that occurs with single sourcing in the centralized structure, resulting decrease of the demand shortage. Sensitivity of the model with different pre-disaster and post-disaster purchasing conditions at SPs are also discussed in order to represent realistic disaster scenarios. (Acknowledgement: Qatar/QNRF/NPRP Project: 5-200-5-027)qscienc

Locations of Temporary Distribution Facilities for Emergency Response Planning

Resource planning in emergency response phase is challenging primarily because the resources have to be delivered to the affected regions in a timely manner and in right quantities. Disasters such as the hurricanes, epidemics and chemical explosions in general impact large regions and emergency supplies are needed for several days. Demand for the resources in one location at a period may not exist in the next period; or, a particular location may have a very high demand in the subsequent period. This dynamic change in the demand patterns adds further challenges in the planning process. The change in demand both in terms of the location and the quantity is usually tackled through allocation of resources at the prepositioned facilities. However, prepositioned facilities may be small in numbers and distribution of resources to the affected area may require additional funds for transportation and other overhead costs. In such a case, distribution of resources through a number of temporary facilities, located near the demand centers can significantly improve the distribution process thereby decreasing the supply response time. Therefore, in this paper, we propose a network flow model for emergency response planning which provides location and allocation plans of temporary distribution facilities for short distribution periods in the planning horizon. We assume that the individual demands in close vicinity are grouped at so-called aggregated demand points (ADPs). The distribution process initiates from a central supply point (CSP) which is a collection point that continuously acquires the resources and prepares them for distribution. In each distribution period, resource available at the CSP is allocated to the temporary distribution centers (TDCs) to distribute to the ADPs. The model considers periodically changing demands at the ADPs and supply availability at the CSP. Therefore, the decision on location and allocation are the dynamic decisions carried out in each distribution period, and the TDCs located in a period are functional temporarily only for the period. The model allows delayed satisfaction of demand when resources in a planning period are insufficient, and allows transfer of excess resources from a relief facility to another in the next time period. The consideration of the dynamic decision, transfer of excess resources and provision of delayed satisfaction of demand make the proposed model unique and more representative to the actual relief distribution. The objective is to minimize the total social cost which is sum of the logistics and the deprivation costs of all distribution periods. The logistics cost consists of the fixed set up costs and the transportation costs. The deprivation cost is the penalty cost associated with the delayed satisfaction of the supplies. The model is tested in a network for numerical analysis. The analysis shows that the location of TDCs in a time period influences the total cost of response. The results show that relief response can be more effective if movement of excess resources from one period to next is allowed. When such a movement is not allowed, it can increase shortage cost and eventually the total cost of emergency response. The analysis also shows solvability of the model in large and complex problem instances within a short computation time which shows the models' robustness and applicability to solve practical size distribution problems.qscienc

危険物輸送の最適化のための多目的アントコロニーシステムに関する研究

京都大学0048新制・課程博士博士(工学)甲第15653号工博第3311号新制||工||1500(附属図書館)28190京都大学大学院工学研究科都市社会工学専攻(主査)教授 谷口 栄一, 教授 藤井 聡, 准教授 宇野 伸宏学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDA

Artificial Neural Network-based model to predict the International Roughness Index of national highways in Nepal

Reliable predictions of pavement performance are crucial for road maintenance, rehabilitation, and reconstruction planning. To facilitate predictions of the International Roughness Index (IRI) changes over time on national highways in Nepal, this study develops a comprehensive overall model, along with regional models that consider climatic and traffic variations among the highways. The study models IRI over time using the Artificial Neural Network (ANN) approach and compares the results with those obtained from a multiple linear regression-based model. The models are developed using pavement IRI, traffic, and climatic (rainfall and temperature) data specific to national highways of Nepal, encompassing 1745 sections and 3710 total observations. The ANN-based overall model has a coefficient of determination (R2) value of 0.82 and outperforms the regression-based model, which has an R2 value of 0.76. The regional models developed for the Terai, Hill, high volume Terai and low volume Terai highways have R2 values of 0.87, 0.91, 0.85 and 0.88, respectively, indicating a good fit. Analysis of the IRI trend over time, as observed from the performance curves generated from the ANN-based model, revealed an S-shaped pattern and lower Root Mean Square Error (RMSE) compared to the regression-based model. Sensitivity analysis highlighted the initial pavement IRI as the most significant parameter in all cases. High temperature days emerged as the second most influential parameter in most models, except for the high volume Terai model, where the number of commercial vehicles serves as the second most sensitive parameter after the initial IRI

A model for planning locations of temporary distribution facilities for emergency response

We propose a network flow model for dynamic selection of temporary distribution facilities and allocation of resources for emergency response planning. The model analyzes the transfer of excess resources between temporary facilities operating in different time periods in order to reduce deprivation. Numerical analysis shows that the location of temporary facilities is determined by the demand and supply points. This work contributes to the emergency response planning that requires a quick response for the supply of relief materials immediately after a disaster hits a particular area. 2015 Elsevier Ltd.This research is made possible by a NPRP award NPRP 5-200-5-027 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopus2-s2.0-8494755077

An integrated resource allocation and distribution model for pre-disaster planning

In this paper, a three-echelon network model is proposed for integrated emergency preparedness and response planning for the distribution of emergency supplies. The model minimizes the social cost to identify a set of potential supply points (SPs) at the highest echelon, where supply items are consolidated and sent to the prepositioning facilities. The sum of logistics and deprivation costs incurred by the population due to the lack of access to goods or services, is considered as the social cost in this model. The deprivation cost is assumed to increase exponentially with the deprivation time. The model also considers pre-disaster and post-disaster purchasing decisions at the SPs, and allows direct shipments from SPs and prepositioned facilities to the demand points. Numerical analysis shows that multiple supply sources can ensure efficient distribution of the supplies and reduce the deprivation costs. The results also indicate that partial prepositioning and post-disaster purchasing can reduce the shortage in emergency supplies.This research was made possible by a NPRP award NPRP 5 - 200 - 5 - 027 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu