The SNS logistics network design : location and vehicle routing.

Large-scale emergencies caused by earthquake, tornado, pandemic flu, terrorism attacks and so on can wreak havoc to communities. In order to mitigate the impact of the events, emergency stockpiles of food, water, medicine and other materials have been set up around the US to be delivered to the affected areas during relief operations. One type of stockpile is called the Strategic National Stockpile (SNS). The SNS logistics network is designed to have multiple stages of facilities, each of which is managed by different levels of governmental authorities - federal, state and local authorities. The design of a logistics network for delivery of the SNS materials within a state are explored in this dissertation. There are three major areas of focus in this dissertation: (1) the SNS facility location model, which is used to determine sites for locating Receiving, Staging and Storage (RSS) and Regional Distribution Nodes (RDNs) to form a logistics network to deliver relief material to Points of Demand (PODs), where the materials are directly delivered to the affected population; (2) the SNS Vehicle Routing Problem (VRP), which is used to assist the SNS staff in determining the numbers of various types of trucks, and the routing schedules of each truck to develop an operational plan for delivering the required relief materials to the assigned PODs within the required duration; (3) the location-routing analysis of emergency scenarios, in which the facility location model and the VRP solution are integrated through the use of a computer program to run on several assumed emergency scenarios. Using real data from the department of public health in the Commonwealth of Kentucky, a transshipment and location model is formulated to determine the facility locations and the transshipment quantities of materials; a multiple-vehicle routing model allowing split deliveries and multiple routes per vehicle that must be completed within a required duration is formulated to determine the routing and scheduling of trucks. The facility location model is implemented using Microsoft Solver Foundation and C#. An algorithm combining the Clark and Wright saving algorithm and Simulated Annealing is designed and implemented in C# to solve the VRP. The algorithm can determine whether there is shortage of transportation capacity, and if so, how many of various types of trucks should be added for optimal performance. All the solution algorithms are integrated into a web-based SNS planning tool. In the location-routing analysis of emergency scenarios, a binary location model and an algorithm for solving VRP solution are integrated as a computer program to forecast the feasibility of distribution plans and the numbers of required trucks of various types. The model also compares the costs and benefits of direct and indirect shipment. A large-scale emergency scenario in which a specific type of vaccine is required to be delivered to the entire state of Kentucky is considered. The experiments are designed based on the real data provided by the Kentucky state government. Thus the experimental results provide valuable suggestions for future SNS preparedness planning

On the dynamic inventory routing problem in humanitarian logistics: a simulation optimization approach using agent-based modeling

80 páginasIn the immediate aftermath of any disaster event, operational decisions are made to relieve the affected population and minimize casualties and human suffering. To do so, humanitarian logistics planners should be supported by strong decision-making tools to better respond to disaster events. One of the most important decisions is the delivery of the correct amount of humanitarian aid in the right moment to the right place. This decision should be made considering the dynamism of the disaster response operations where the information is not known beforehand and vary over time. For instance, the effect of the Word-of-Mouth and shortages in distribution points’ demand can impact the operational decisions. Therefore, the inventory and transportation decisions should be made constantly to better serve the affected people. This work presents a simulation-optimization approach to make disaster relief distribution decisions dynamically. An agent-based simulation model solves the inventory routing problem dynamically, considering changes in the humanitarian supply chain over the planning horizon. Additionally, the inventory routing schemes are made using a proposed mathematical model that aims to minimize the level of shortage and inventory at risk (associated to the risk of losing it). The computational proposal is implemented in the ANYLOGIC and CPLEX software. Finally, a case study motivated by the 2017 Mocoa-Colombia landslide is developed using real data and is presented to be used in conjunction with the proposed framework. Computational experimentations show the impact of the word-of-mouth and the frequency in decision making in distribution points’ shortages and service levels. Therefore, considering changes in demand over the planning horizon contributes to lowering the shortages and contributes to making better distributions plans in the response phase of a disaster.Después de la ocurrencia de cualquier desastre se deben tomar decisiones para aliviar a la población afectada minimizando las pérdidas humanas y el sufrimiento. Para ello, los responsables de la logística humanitaria deben contar con robustas herramientas para tomar decisiones acertadas que respondan adecuadamente ante esos eventos. Una de las decisiones más importantes es la entrega de ayuda humanitaria en el lugar, las cantidades y en el momento correcto. La anterior decisión debe ser tomada teniendo en cuenta el dinamismo de las operaciones de respuesta humanitaria en donde la información no es conocida de antemano y varía en el tiempo. Por ejemplo, el efecto del Voz a Voz y la escasez en los puntos de distribución de ayuda humanitaira pueden impactar las decisiones operacionales. Es por lo anterior, que las decisiones de transporte de ayuda humanitaria deben ser realizadas constantemente para servir de una mejor forma a la población afectada. Este trabajo presenta una propuesta de simulación-optimización para tomar las decisiones de ruteo de inventario de ayuda humanitaria de forma dinámica. A través de un modelo de simulación basado en agentes se resuelve dinámicamente el problema de ruteo de inventario considerando cambios en la cadena de suministro humanitaria. Adicionalmente, las decisiones de ruteo de inventario son tomadas mediante un modelo matemático propuesto que busca minimizar el nivel de inventario en riesgo y el nivel de escases simultáneamente. La propuesta computacional es implementada en los programas ANYLOGIC y CPLEX. Finalmente mediante un caso de estudio basado en la catastrofe de Mocoa-Colombia en 2017 se evaluará la propuesta. Experimentos computacionales muestran el impacto del voz-a-voz y frecuencia de toma de decisiones en la escasez y el nivel de servicio en los puntos de distribución. Por lo tanto, considerar cambios en la demanda contribuye a disminuir la escasez y hacer mejores esquemas de distribución de ayuda humanitaria.Maestría en Diseño y Gestión de ProcesosMagíster en Diseño y Gestión de Proceso

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake

International audienceIn the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus.To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simulations. Our approach supports determining what trade-offs actually matter to facilitate discussions in the presence of multiple stakeholders. To validate our proposal, we extend a location-allocation model and apply our approach to an actual data-set from the 2015 Nepal earthquake response. Our analyses show that with the relative importance of covering demands, the trade-offs between logistics costs and response time affects the numbers and locations of RDCs considerably. We show through a small experiment that the outputs of our approach can effectively support group decision-making to develop relief plans in disasters response

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

A distribution network design for fast-moving consumer goods

A distribution network design of fast-moving consumer goods ensures distribution of products in an effective manner by giving  maximum customers’ satisfaction and minimum distribution cost. The study evaluates the distribution through direct shipment and the use of intermediate shipment for distribution of products from plant to depots. A real-life case study in Southwestern Nigeria was defined and solved as a linear programming model to minimise total cost of distribution from plant to the depots with consideration of four routing options. The results show that distribution through intermediaries gives a better solution than routing option with  direct shipment. The best routing option with intermediate points when compared with the routing option with direct shipment gives a savings of 1,819,490.00 Naira which translates to 13.46% cost savings. The study shows that the location of intermediaries is a key decision in distribution network design and that the intermediaries add value to the distribution networks in supply chain. Keywords: Distribution network; Supply chain design; Fast-moving consumer goods; Linear programmin

Emergency Resource Layout with Multiple Objectives under Complex Disaster Scenarios

Effective placement of emergency rescue resources, particularly with joint suppliers in complex disaster scenarios, is crucial for ensuring the reliability, efficiency, and quality of emergency rescue activities. However, limited research has considered the interaction between different disasters and material classification, which are highly vital to the emergency rescue. This study provides a novel and practical framework for reliable strategies of emergency rescue under complex disaster scenarios. The study employs a scenario-based approach to represent complex disasters, such as earthquakes, mudslides, floods, and their interactions. In optimizing the placement of emergency resources, the study considers government-owned suppliers, framework agreement suppliers, and existing suppliers collectively supporting emergency rescue materials. To determine the selection of joint suppliers and their corresponding optimal material quantities under complex disaster scenarios, the research proposes a multi-objective model that integrates cost, fairness, emergency efficiency, and uncertainty into a facility location problem. Finally, the study develops an NSGA-II-XGB algorithm to solve a disaster-prone province example and verify the feasibility and effectiveness of the proposed multi-objective model and solution methods. The results show that the methodology proposed in this paper can greatly reduce emergency costs, rescue time, and the difference between demand and suppliers while maximizing the coverage of rescue resources. More importantly, it can optimize the scale of resources by determining the location and number of materials provided by joint suppliers for various kinds of disasters simultaneously. This research represents a promising step towards making informed configuration decisions in emergency rescue work