Mobile clinics: medical service strategy for disaster healthcare response operation

Purpose:Healthcare facilities disrupted due to disasters leave affected communities without access tosufficient health care. This study proposes the use of mobile health clinics (MHCs) to address the issuesfaced by medical service providers during disaster response.Design/methodology/approach:An MHC is a mobile facility that provides healthcare services from astationary location. The model was developed as a healthcare response strategy that considers demanduncertainties due to the nature of the disaster. Therefore, the objective of this study is to generate anMHC route and schedule simultaneously and determine how each MHC meets patient demands within aprespecified time horizon. A stochastic model is presented because the impact of a disaster variesaccording to its scale.Findings:An investigation of medical shelters in locations with high numbers of displaced people andthe routing of mobile clinics for several facility locations with a small number of people showed that ahybrid strategy comprising a medical shelter and MHC is the best option. MHCs can serve many locationswithin walking distance. They can also be routed to other locations when time constraints allow. When alarge number of people are in a shelter, building a medical facility provides better service.Originality/value:This study proposed MHCs as a medical service strategy to address the challengesfaced by communities during disaster responses. The aim of MHCs is to improve communities’ access tohealthcare servicesPeer Reviewe

Revisión del estado del arte del problema de ruteo de vehículos con recogida y entrega (VRPPD)

This paper presents a literature review of the state of the art vehicle routing problem with deliveries and collections (VRPPD: Vehicle Routing Problem with pickups and deliveries). Is performed a classification of the different variants of the problem, and the work and conducted research on the subject according to its authors, according to the models and the solution methods used. Also are analyzed future trends in modeling and solution techniques. The VRPPD is a problem of type MILP (Mixed Integer Linear Programming) involving whole and continuous quantities, and that turns out to be NP-Hard problems with a medium or large number of customers. The research does emphasis on variants of the problem involving variables associated with the environment, and in particular reducing the impact of greenhouse gases. The review notes that published until 2016.En este trabajo se realiza una revisión bibliográfica del estado del arte del problema de ruteo de vehículos con entregas y recogidas (VRPPD: Vehicle routing problem with pickups and deliveries). Se presenta una clasificación de las diferentes variantes del problema, y de los trabajos e investigaciones realizados sobre el tema según sus autores, los modelos utilizados y los métodos de solución usados. También se analizan las tendencias futuras en modelamiento y técnicas de solución. El VRPPD es un problema del tipo MILP (programación lineal entera mixta) que involucra cantidades enteras y continuas, y que resulta ser NP-Hard en problemas con un número mediano o grande de clientes. En la búsqueda se hace énfasis en las variantes del problema que involucran variables asociadas al medio ambiente, y en particular con la reducción del impacto de gases de efecto invernadero. La revisión observa lo publicado hasta el año 2016

A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles

The flow refueling location problem (FRLP) locates p stations in order to maximize the flow volume that can be accommodated in a road network respecting the range limitations of the vehicles. This paper introduces the charging station location problem with plug-in hybrid electric vehicles (CSLP-PHEV) as a generalization of the FRLP. We consider not only the electric vehicles but also the plug-in hybrid electric vehicles when locating the stations. Furthermore, we accommodate multiple types of these vehicles with different ranges. Our objective is to maximize the vehicle-miles-traveled using electricity and thereby minimize the total cost of transportation under the existing cost structure between electricity and gasoline. This is also indirectly equivalent to maximizing the environmental benefits. We present an arc-cover formulation and a Benders decomposition algorithm as exact solution methodologies to solve the CSLP-PHEV. The decomposition algorithm is accelerated using Pareto-optimal cut generation schemes. The structure of the formulation allows us to construct the subproblem solutions, dual solutions and nondominated Pareto-optimal cuts as closed form expressions without having to solve any linear programs. This increases the efficiency of the decomposition algorithm by orders of magnitude and the results of the computational studies show that the proposed algorithm both accelerates the solution process and effectively handles instances of realistic size for both CSLP-PHEV and FRLP. © 2016 Elsevier Lt

Designing the Liver Allocation Hierarchy: Incorporating Equity and Uncertainty

Liver transplantation is the only available therapy for any acute or chronic condition resulting in irreversible liver dysfunction. The liver allocation system in the U.S. is administered by the United Network for Organ Sharing (UNOS), a scientific and educational nonprofit organization. The main components of the organ procurement and transplant network are Organ Procurement Organizations (OPOs), which are collections of transplant centers responsible for maintaining local waiting lists, harvesting donated organs and carrying out transplants. Currently in the U.S., OPOs are grouped into 11 regions to facilitate organ allocation, and a three-tier mechanism is utilized that aims to reduce organ preservation time and transport distance to maintain organ quality, while giving sicker patients higher priority. Livers are scarce and perishable resources that rapidly lose viability, which makes their transport distance a crucial factor in transplant outcomes. When a liver becomes available, it is matched with patients on the waiting list according to a complex mechanism that gives priority to patients within the harvesting OPO and region. Transplants at the regional level accounted for more than 50% of all transplants since 2000.This dissertation focuses on the design of regions for liver allocation hierarchy, and includes optimization models that incorporate geographic equity as well as uncertainty throughout the analysis. We employ multi-objective optimization algorithms that involve solving parametric integer programs to balance two possibly conflicting objectives in the system: maximizing efficiency, as measured by the number of viability adjusted transplants, and maximizing geographic equity, as measured by the minimum rate of organ flow into individual OPOs from outside of their own local area. Our results show that efficiency improvements of up to 6% or equity gains of about 70% can be achieved when compared to the current performance of the system by redesigning the regional configuration for the national liver allocation hierarchy.We also introduce a stochastic programming framework to capture the uncertainty of the system by considering scenarios that correspond to different snapshots of the national waiting list and maximize the expected benefit from liver transplants under this stochastic view of the system. We explore many algorithmic and computational strategies including sampling methods, column generation strategies, branching and integer-solution generation procedures, to aid the solution process of the resulting large-scale integer programs. We also explore an OPO-based extension to our two-stage stochastic programming framework that lends itself to more extensive computational testing. The regional configurations obtained using these models are estimated to increase expected life-time gained per transplant operation by up to 7% when compared to the current system.This dissertation also focuses on the general question of designing efficient algorithms that combine column and cut generation to solve large-scale two-stage stochastic linear programs. We introduce a flexible method to combine column generation and the L-shaped method for two-stage stochastic linear programming. We explore the performance of various algorithm designs that employ stabilization subroutines for strengthening both column and cut generation to effectively avoid degeneracy. We study two-stage stochastic versions of the cutting stock and multi-commodity network flow problems to analyze the performances of algorithms in this context

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights