Prompt and efficient intervention is vital in reducing casualty figures during epidemic outbreaks, disasters, sudden civil strife or terrorism attacks. This can only be achieved if there is a fit-for-purpose and location-specific emergency response plan in place, incorporating geographical, time and vehicular capacity constraints. In this research, a comprehensive emergency response model for situations of uncertainties (in locations' demand and available resources), typically obtainable in low-resource countries, is designed. It involves the development of algorithms for optimizing pre-and post-disaster activities. The studies result in the development of four models: (1) an adaptation of a machine learning clustering algorithm, for pre-positioning depots and emergency operation centers, which optimizes the placement of these depots, such that the largest geographical location is covered, and the maximum number of individuals reached, with minimal facility cost; (2) an optimization algorithm for routing relief distribution, using heterogenous fleets of vehicle, with considerations for uncertainties in humanitarian supplies; (3) a genetic algorithm-based route improvement model; and (4) a model for integrating possible new locations into the routing network, in real-time, using emergency severity ranking, with a high priority on the most-vulnerable population. The clustering approach to solving dept location-allocation problem produces a better time complexity, and the benchmarking of the routing algorithm with existing approaches, results in competitive outcomes.

abstract: Due to environmental and geopolitical reasons, many countries are embracing electric vehicles (EVs) as an alternative to gasoline powered automobiles. Other alternative-fuel vehicles (AFVs) powered by compressed gas, hydrogen or biodiesel have also been tested for replacing gasoline powered vehicles. However, since the associated refueling infrastructure of AFVs is sparse and is gradually being built, the distance between recharging points (RPs) becomes a crucial prohibitive attribute in attracting drivers to use such vehicles. Optimally locating RPs will both increase demand and help in developing the refueling infrastructure. The major emphasis in this dissertation is the development of theories and associated algorithms for a new set of location problems defined on continuous network space related to siting multiple RPs for range limited vehicles. This dissertation covers three optimization problems: locating multiple RPs on a line network, locating multiple RPs on a comb tree network, and locating multiple RPs on a general tree network. For each of the three problems, finding the minimum number of RPs needed to refuel all Origin-Destination (O-D) flows is considered as the first objective. For this minimum number, the location objective is to locate this number of RPs to minimize weighted sum of the travelling distance for all O-D flows. Different exact algorithms are proposed to solve each of the three algorithms. In the first part of this dissertation, the simplest case of locating RPs on a line network is addressed. Scenarios include single one-way O-D pair, multiple one-way O-D pairs, round trips, etc. A mixed integer program with linear constraints and quartic objective function is formulated. A finite dominating set (FDS) is identified, and based on the existence of FDS, the problem is formulated as a shortest path problem. In the second part, the problem is extended to comb tree networks. Finally, the problem is extended to general tree networks. The extension to a probabilistic version of the location problem is also addressed. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

This dissertation presents spatial partitioning algorithms to solve location-allocation problems. Location-allocations problems pertain to both the selection of facilities to serve demand at demand points and the assignment of demand points to the selected or known facilities. In the first part of this dissertation, we focus on the well known and well-researched location-allocation problem, the "p-median problem", which is a distance-based location-allocation problem that involves selection and allocation of p facilities for n demand points. We evaluate the performance of existing p-median heuristic algorithms and investigate the impact of the scale of the problem, and the spatial distribution of demand points on the performance of these algorithms. Based on the results from this comparative study, we present guidelines for location analysts to aid them in selecting the best heuristic and corresponding parameters depending on the problem at hand. Additionally, we found that existing heuristic algorithms are not suitable for solving large-scale p-median problems in a reasonable amount of time. We present a density-based decomposition methodology to solve large-scale p-median problems efficiently. This algorithm identifies dense clusters in the region and uses a MapReduce procedure to select facilities in the clustered regions independently and combine the solutions from the subproblems. Lastly, we present a novel greedy heuristic algorithm to solve the contiguity constrained fixed facility demand distribution problem. The objective of this problem is to create contiguous service areas for the facilities such that the demand at all facilities is uniform or proportional to the available resources, while the distance between demand points and facilities is minimized. The results in this research are shown in the context of creating emergency response plans for bio-emergencies. The algorithms are used to select Point of Dispensing (POD) locations (if not known) and map them to population regions to ensure that all affected individuals are assigned to a POD facility.

Due to growing population over the past decades, settlements are scattered in sparse as well as dense clusters across Dalarna County. However, irrespective of any physical, social or economic conditions, free public health care must be available at a minimum and equal distance of travel for all citizens of a region. In the current scenario in Dalarna, around 16% of the population travels beyond 10 km to reach their nearest medical facility. The aim of this study is to suggest the most favorable locations for Mobile Hospital services across Dalarna County, based on spatial analysis of accessibility, population coverage, and importantly, in a way that travel distance, is minimized and equal for all. This study makes use of Multi Criteria Analysis methods. The problem of mobile hospital site selection is broken down into criteria, and Analytic Hierarchical Process is used to evaluate weights for each criterion. Then, a weighted overlay results in regions with score-based suitability for a mobile hospital. Maximum population coverage based Location Allocation analysis results in generating a proposed Facility and Demand Coverage output. The results show an increase in coverage of population, while meeting the requirements of criteria in the aim.

The location of distribution centers is an important strategic decision in supply chain design, particularly as it relates to service quality, productivity, and profitability of the firm. There has been extensive research performed on distribution location models which require the use of complex algorithms and assumptions that make use of these models difficult in practice for small and medium enterprises (SMEs) that have limited capital and resources. Studies have also failed to capture and quantify potential business results of using more sophisticated methods. In this study, a deterministic and static location-allocation model is designed using a prototype software tool. The tool is a collection of Excel/VBA programs formulated as a mixed integer programming (MIP) model. Research was done in conjunction with a personal care products company that provided a unique opportunity to evaluate the manual methods typically used in SMEs with the results of the software tool and the potential business impact. Both quantitative data, including customer locations and order information, as well as qualitative data were collected from the company. A total of five models were simulated using the prototype software tool, including one model of the current supply chain for use as a base comparison, and four future-state models of potential distribution center (DC) location scenarios. The objective in each of these models was to minimize transportation costs while maintaining the desired service fulfillment levels. The use of the prototype software tool resulted in a more optimal supply chain solution. The optimized DC location resulted in a network design with a 6.5% reduction in transportation costs from the base model, and a 0.8% reduction in transportation costs from a location previously chosen by the company. The results also provided insight into considering weighted shipping volume in location analysis as it can serve as a magnifier of business impact and rapid diminishing returns when shipping product below an average of 10 pounds. The use of an optimization tool was shown to mitigate many issues SMEs encounter in attempting to synthesize multiple variables in the DC location problem.

Doctor of Philosophy / Department of Grain Science & Industry / Dirk E. Maier / The feasibility of utilizing cellulosic biomass as an energy feedstock is dominated by factors such as facility location, feedstock availability, and transportation cost. The main goal of this research was to develop a GIS-based method that will generate more accurate biomass residue availability data as input data to biomass supply chain logistics models. This research was carried out in four objectives to ensure that, as improvement parameters were implemented, the methodology remained valid and became more accurate. The first objective compared an existing method to a proposed method to quantify feedstock availability given a facility’s location using a geographical information system. The proposed method proved to be more robust (by a factor of 1.45) than the existing method because it calculates the distance from the facility to farm fields using a real road network, and the acreage of crop-specific fields in a given service area based on crop season specific satellite images. The second objective implemented two improvement parameters to the previously proposed constant removal rate (CRR) method. It examined the effect of field-level yield variance and variable removal rates (VRR) on quantification of the feedstock availability supply for a biorefinery. The new VRR method predicted on average 113,384 ± 38,770 dry tons (DT) of additional residue per service area compared to the CRR method. The third objective further improved the VRR method by utilizing multiple crops as biomass sources and estimating VRR based on crop rotation. On average a 3,793 ± 5,733 DT per service area difference resulted when increasing the number of crop-specific VRR rates used to estimate feedstock quantification. The supplementary use of crop-specific VRR rates affected residue availability given a crop’s residue removal rate is influenced by crop yield, crop rotation, soil characteristics, as well as field location and management. The fourth objective assessed the suitability of potential feedstock storage locations (FSL) to store multi-crop biomass remotely based on a spatial and location-allocation analysis. The sensitivity analysis showed that scenario 2 (16-km; 10-mile service area) appeared to be the more cost-effective option given fewer FSLs (35) were needed and more demand points could be serviced (98.1%) compared to scenario 1 (8-km; 5-mile service area; 62.1% demand points; 50 FSLs), despite presumably higher transportation costs.

Le secours à personne (SAP) correspond aux missions que réalisent les pompiers français pour porter secours en urgence aux victimes. Comme la démographie médicale, le nombre de pompiers volontaires est en baisse, particulièrement dans les espaces périurbains et ruraux. Parallèlement, le nombre d’interventions de type SAP est en augmentation constante, ce qui engendre localement des tensions entre l’offre et la demande en secours. Une des solutions pour remédier à cette situation consiste à optimiser la localisation des moyens existants. Cependant, les méthodes mises en œuvre pour cela, par les professionnels, ne sont pas suffisamment intégrées, et celles développées par les chercheurs, pas suffisamment opérationnelles. L’objectif de cette recherche est donc de proposer une méthode d’optimisation des localisations, à la fois intégrée et opérationnelle. Le choix s’est porté vers les modèles de localisation-allocation, car ils permettent de prendre en compte, de manière globale, l’ensemble des paramètres entrant en jeu dans la localisation des secours, ainsi que les conséquences en chaîne que les choix de localisation peuvent entraîner. La démarche se décompose en trois étapes. La première consiste à réaliser un diagnostic du SAP. La deuxième consiste à choisir et préparer les données d’entrée du modèle et le paramétrer. La troisième consiste à réaliser différents grands scénarios d’optimisation possibles. Cette démarche a permis de mettre en exergue les besoins de méthodes et d’outils d’optimisation opérationnels et de démontrer l’intérêt des modèles de localisation-allocation comme aide à la réflexion pour l’optimisation de la localisation des moyens de SAP. / In France, emergency rescue for victims is an integral part of the fire brigade missions. This is what we called the secours à personne (SAP). These last years, the number of volunteer firemen is in decline, particularly in the peri-urban and rural areas, which is not without consequence on the coverage of operational resources, especially since these sectors are already experiencing a decline in medical demography. At the same time, the number of SAP type of interventions is increasing. In this context, tensions between supply and demand for SAP tend to increase. One of the solutions envisaged to solve this situation is to optimize the location of existing resources. However, the methods implemented to this, by professionals, don’t prove to be sufficiently integrated, and those developed by researchers, not sufficiently operational. The main objective of this research is to propose an optimization method for localized SAP resources, both integrated and operational.The choice fell on the location-allocation models, because they take into account, overall, the set of parameters that come into play in the location of the emergency, and the consequences chain as location choices can result. The approach consists of three steps. The first is to make a diagnosis of SAP. The second is to select and prepare input data and configure the model. The third is to carry out various great scenarios optimization possible. This approach allowed, on one hand, to highlight the needs and methods of operational optimization tools, and on the other hand, to demonstrate the interest of the location-allocation models as an aid to reflection for optimizing localization of SAP resources.

Este estudo integrou sistemas de informação geográfica e métodos analíticos geoespaciais baseados em modelos de acessibilidade espacial e de locação-alocação com o objetivo de aprimorar o planejamento espacial de diferentes programas de saúde pública em áreas urbanas. Para estimar a acessibilidade espacial foi desenvolvido o modelo de três passos de área de influência flutuante (E3SFCA - Enhancement three-step floating catchment área) baseado em uma função Gaussiana, considerando um coeficiente de atrito e distâncias ao longo de uma rede de transporte, utilizando o algoritmo de Dijkstra. A metodologia foi aplicada e validada usando os programas de vacinação contra a raiva e de esterilização para cães e gatos da cidade de Bogotá, Colômbia. A escolha do método de cálculo da distância resolveu o problema de superestimação associado com a metodologia clássica que aplica zonas buffer em torno dos locais de serviço baseado em uma distância Euclideana. Em geral, não se encontrou uma adequada acessibilidade espacial aos dois programas. As zonas norte, central e periférica da cidade revelaram baixa ou nula acessibilidade aos serviços. Para determinar a alocação e realocação efetiva dos programas foram utilizados os problemas de máxima cobertura com demanda finita e de p-mediana ou de mínima impedância. A realocação proposta pelo modelo de máxima cobertura forneceu uma melhor distribuição dos serviços nas áreas mais povoadas com cães e gatos e garantiu uma acessibilidade espacial potencial a estes programas. O desenvolvimento deste trabalho pode trazer benefícios diretos para a sociedade em geral auxiliando no planejamento estratégico e melhorando a efetividade das ações públicas em áreas urbanas da América Latina. / This study integrated geographic information systems and geospatial analytical methods based on spatial accessibility and location-allocation models in order to improve the spatial planning of different public health programs in urban areas. To estimate the spatial accessibility, a Gaussian-based three-step oating catchment area (E3SFCA) method was developed, including a friction coeffcient and using distances along a street network based on Dijkstra\'s algorithm. The methodology was applied and valiated using the rabies vaccination and sterilization programs for dogs and cats in the city of Bogotá, Colombia. The choice of the distance calculation method solve the overestimation associated with the classic methodology that applies buffer zones around vaccination sites based on Euclidean (straight-line) distance. In general it was not observed an adequate spatial accessibility to both programs. The zones north, central and peripheral of the city revealed low or no access to services. To determine the effective allocation or reallocation of these programs the maximum coverage with finite demand and the p-median or minimum impedance problems were used. The relocation proposed by the maximum coverage model provided a better distribution of the services in the most populated areas and proportioned a potential spatial accessibility to these programs. The development of this work can provide direct benefits to society assisting in the strategic planning and improving the effectiveness of public policies in urban areas of Latin America.

The p-median problem is often used to locate p service centers by minimizing their distances to a geographically distributed demand (n). The optimal locations are sensitive to geographical context such as road network and demand points especially when they are asymmetrically distributed in the plane. Most studies focus on evaluating performances of the p-median model when p and n vary. To our knowledge this is not a very well-studied problem when the road network is alternated especially when it is applied in a real world context. The aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the density in the road network is alternated. The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 service centers we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000. To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when nodes in the road network increase and p is low. When p is high the improvements are larger. The results also show that choice of the best network depends on p. The larger p the larger density of the network is needed.

The emphasis on minimizing the logistics costs to decrease overall system costs has led the researchers to consider the interdependencies between the decisions of locating facilities and planning the routes from those facilities. The location-routing problems considering this issue are the subject of this thesis study. A two-level hierarchical distribution system is considered in which goods are delivered from the sources (plants) to the facilities (depots) and then from the facilities to the customers. The facilities are uncapacitated and operate within the shift times defined. The goods are to be delivered to the customers within their time windows by the vehicles that are capacitated. Both a mathematical model and a genetic algorithm based heuristic solution approach are proposed for this problem. We discuss the problem specific issues integrated with the general framework of the genetic algorithm applications. The computational studies are realized on a number of test problems. The results indicate that the genetic algorithm based heuristic gives satisfactory results compared with a sequential solution methodology.