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Two important areas of decision-making in distribution system design involve facility location and inventory policy determination. Facility location analyzes questions such as how many facilities should be opened, where they should be located, and which customers should be assigned to which DCs. Inventory policy determination involves more tactical decisions such as the order quantities and frequencies at each level or echelon in the network. It is believed that these two decisions can influence each other significantly. Including a multi-echelon inventory policy decision in a location analysis allows a user to capitalize on the strengths that each DC has to offer (e.g., lower labor rates, land costs, etc.). Likewise, when the locations of two facilities are known, a multi-echelon inventory policy can be designed better to incorporate the exact lead times and fixed costs between the facilities at each level of the system. Despite this, the two problems are typically solved independently. This research addresses these problems together and investigates different heuristic methods for solving a combined inventory-location model. We begin by presenting the background and formulation for each problem. These formulations are then combined to show how the two problems can be mathematically formulated together. Rather than solve the problem exactly, two heuristic methods using different philosophies are tested. We apply these heuristic methods to the combined inventory-location problem to determine how much we can improve distribution network design solutions and what type of heuristic methodology is most effective in gaining these improvements. Our results show that the combined inventory-location model is capable of improving on the solutions obtained by a location model with a fixed inventory policy. The improvement based on the data sets tested in this research was approximately $60,000. However, in cases where the inventory costs are a larger portion of the total cost, the improvement made by the inventory-location model increased to over $1,000,000. We also found that our second heuristic method tested provided statistically significant improved results over our first heuristic method. Moreover, the second heuristic method typically ran 67% faster. The improved results, although small in a relative sense (the average improvement was 0.18%), would still represent a large absolute improvement in supply chain costs. As much as $174,000 was saved in the data sets tested for this research. / Master of Science

<p> This paper is concerned with developing a methodology for identifying and measuring the dimensions of community opposition to externality-generating public facilities. It critically reviews the traditional modelling approaches to public facility location. The methodology, by which the dimensions of facility impact might be established for incorporation into political decision-making models of facility location, is then proposed. The results of a pilot empirical test of this methodology, using techniques of non-metric Multidimensional Scaling for the analysis of individuals' perception's, indicate the types of dimensions which might be derived from the application of the methodology to questions concerning public facility location. </p> / Thesis / Doctor of Philosophy (PhD)

[pt] Ao longo dos últimos anos o desenvolvimento da Pesquisa Operacional foi fundamental para o crescimento da indústria aérea. No Brasil, o órgão responsável pela fiscalização da aviação civil é a Agência Nacional de Aviação Civil (ANAC). O objetivo da dissertação é desenvolver um modelo de otimização para localização-alocação de pessoal (servidores) e aplicá-lo à um estudo de caso da ANAC, no contexto de Safety Oversight. Uma revisão sistematizada de literatura foi conduzida para identificar os gaps e soluções recentes na literatura de problemas de facility location. O objetivo descrito foi alcançado e o modelo matemático foi validado pelo Estudo de Caso proposto. O modelo alocou 31 porcento dos servidores da ANAC na Região Sudeste do Brasil, 25 porcento na Região Nordeste, 17 porcento na Região Norte, 17 porcento na Região Sul e 10 porcento na Região Centro-Oeste; reduzindo em 66 porcento a quantidade total de inspetores. Obteve-se ainda uma matriz de distribuição de capacitações por agência da ANAC, de forma que o tomador de decisão possa analisar o perfil ótimo de habilitações dos funcionários de cada agência. Uma análise de sensibilidade foi conduzida para avaliar a flexibilidade do modelo, que se mostrou eficiente para aplicações em problemas reais. / [en] Over the last years, Research Operations development has become fundamental for Aviation Industry. In Brazil, the agency responsible for Civil Aviation inspection is the National Agency of Civil Aviation (ANAC). This work aims the development of an optimal personnel location-allocation model and application in a case study at ANAC in Safety Oversight context. One Literature Review has been done for gaps identification and to find the most recent solution techniques for facility location problems. The research objective has been achieved, and the proposed case study has validated the model. The model located 31 percent of ANAC personnel in Brazilian Southeast Region, 25 percent in Northeast Region, 17 percent in North Region, 17 percent in South Region and 10 percent in Central-West Region; decreasing in 66 percent the total quantity of allocated inspectors. A capacities matrix has been constructed with model results; decision-makers can analyze the optimal distribution of personnel capacities in each facility. Finally, a sensitivity analysis has been done to test the model flexibility, which prove the model is efficient for real problems application.

A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. While utilizing the branch and bound framework, machine and deep learning models enhance either the computational efficiency or performance of the model. This thesis examines how imitating different variable selection strategies of classical MIP solvers behave on a state-of-the-art deep learning model. A recently developed deep learning algorithm is used in this thesis, which represents the branch and bound state as a bipartite graph. This graph serves as the input to a graph network model, which determines the variable in the MIP on which branching occurs. This thesis compares how imitating different classical branching strategies behaves on different algorithm outputs and, most importantly, time span. More specifically, this thesis conducts an empirical study on a MIP known as the facility location problem (FLP) and compares the different methods for imitation. This thesis shows that the deep learning algorithm can outperform the classical methods in terms of time span. More specifically, imitating the branching strategies resulting in small branch and bound trees give rise to a more rapid performance in finding the global optimum. Lastly, it is shown that a smaller embedding size in the network model is preferred for these instances when looking at the trade-off between variable selection and time cost. / En ny typ av maskin och djupinlärningsmodeller har utvecklats inom villkors optimering, specifikt för så kallade blandade heltalsproblem (MIP). Dessa modeller hämtar inspiration från tidigare lösningsmetoder, främst en heuristisk som kallas “branch and bound”. Genom att använda “branch and bound” ramverket förbättrar maskin och djupinlärningsmodeller antingen beräkningshastigheten eller prestandan hos modellen. Denna uppsats undersöker hur imitation av olika strategier för val av variabler från klassiska MIP-algoritmer beter sig på en modern djupinlärningsmodell. I denna uppsats används en nyligen utvecklad djupinlärningsalgoritm som representerar “branch and bound” tillståndet som en bipartit graf. Denna graf används som indata till en “graph network” modell som avgör vilken variabel i MIP-problemet som tas hänsyn till. Uppsatsen jämför hur imitation av olika klassiska “branching” strategier påverkar olika algoritmutgångar, framför allt, tidslängd. Mer specifikt utför denna uppsats en empirisk studie på ett MIP-problem som kallas för “facility location problem” (FLP) och jämför imitationen av de olika metoderna. I denna uppsats visas det att denna djupinlärningsalgoritm kan överträffa de klassiska metoderna när det gäller tidslängd. Mer specifikt ger imitation av “branching” strategier som resulterar i små “branch and bound” träd upphov till en snabbare prestation vid sökning av den globala optimala lösningen. Slutligen visas det att en mindre inbäddningsstorlek i nätverksmodellen föredras i dessa fall när man ser på avvägningen mellan val av variabler och tidskostnad.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Jessica L. Heier Stamm / Coordination between humanitarian organizations is critical during the response effort to a disaster, as coordinating aid improves efficiency, reduces duplication of efforts, and ultimately leads to better outcomes for beneficiaries. One particular challenge arises when temporary facilities must be established post-disaster due to the destruction of buildings. For example, the 2015 Nepal earthquakes created a need for the placement of over 4,000 temporary learning facilities after several school buildings were damaged or destroyed. It is important that humanitarians coordinate well to fill these needs efficiently and effectively, while maintaining equity among beneficiaries in the affected areas. This means ensuring that enough facilities are provided in a timely manner, and are distributed fairly to all in need. The goals of this thesis are to study coordination strategies focusing primarily on the placement of temporary educational facilities for children following a disaster. This research also aims to gather useful data by surveying active humanitarians in order to better understand their decisions made in the field. This work uses the results of this survey, along with publicly available data published after the 2015 Nepal earthquakes to create an agent-based simulation model, and uses the Nepal case study to demonstrate the efficacy of the model framework. This research finds that organizations' initial location of operation can greatly impact the number of facilities they are collectively able to establish, the geographic disparity across the region, and the organizations' utilization. Specifically, while focusing efforts on the districts with the most need is most efficient and effective, a more uniform approach yields a more equitable response. This work also finds that there can be a trade-off between overall effectiveness and the number of partnerships established in the field. These findings show a need for further study into the intricacies of coordination between humanitarian workers. This author advocates for the use of information sharing mechanisms among practitioners, as well as further utilization of agent-based modeling as a means of studying the complex nature of disaster response. Specifically there is a need to further study educational needs as a logistical problem, and strategies for solving the post-disaster facility location problem.

Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

Hazardous Materials (HazMat) are transported throughout Canada in a great number of road shipments. The transportation of HazMat poses special risks for neighboring population and environment. While HazMat accidents are rare events, they could be catastrophic in nature and could result in substantial damage to nearby communities. Effective emergency response plays an important role in the safe transportation of HazMat. Transportation of HazMat involves different parties, including shippers, regulators, and surrounding communities. While the shipping party is responsible for safe delivery of HazMat shipments, it is the responsibility of local emergency service agencies to respond to accidents occurring within their jurisdictions. In this research, the emergency response to HazMat transport accidents is assumed to be delegated exclusively to specially trained and equipped HazMat teams. This research proposes a new comprehensive systematic approach to determine the best location of HazMat teams on regional bases utilizing HazMat transport risk as a location criterion. The proposed model is the first to consider emergency response roles in HazMat transport risk analysis, and was intended as an optimization tool to be used by practitioners for HazMat emergency response planning. Additionally, the proposed model can be used to assess risk implications in regards to current locations of HazMat teams in a region, and to develop effective strategies for locating HazMat teams, such as closing and/or relocating teams in the region. The model investigates how HazMat team locations can be tailored to recognize the risk of transporting HazMat and would provide a more objective set of input alternatives into the multi-criteria decision making process of regionally locating HazMat teams. The proposed model was applied to the region of southwestern Ontario in effort to illustrate its features and capabilities in the HazMat emergency response planning and decision making process. Accordingly, the model provided very useful insights while reviewing several HazMat team location strategies for the southwestern Ontario region and investigating tradeoff among different factors. This research contributes to a better understanding of emergency response roles by reducing HazMat transport risks, and will greatly benefit both researchers and practitioners in the field of HazMat transport and emergency response.

Master of Science / Department of Industrial and Manufacturing Systems Engineering / Jessica L. Heier Stamm / Gifts-in-kind (GIK) donations negatively affect the humanitarian supply chain at the point of receipt near the disaster site. In any disaster, as much as 50 percent of GIK donations are irrelevant to the relief efforts. This proves to be a significant issue to humanitarian organizations because the quantity and type of future GIK are uncertain, making it difficult to account for GIK donations at the strategic planning level. The result is GIK consuming critical warehouse space and manpower. Additionally, improper treatment of GIK can result in ill-favor of donors and loss of donations (both cash and GIK) and support for the humanitarian organization. This thesis proposes a robust facility location approach that mitigates the impact of GIK by providing storage space for GIK and pre-positions supplies to meet initial demand. The setting of the problem is strategic planning for hurricane relief along the Gulf and Atlantic Coasts of the United States. The approach uses a robust scenario-based method to account for uncertainty in both demand and GIK donations. The model determines the location and number of warehouses in the network, the amount of pre-positioned supplies to meet demand, and the amount of space in each warehouse to alleviate the impact of GIK. The basis of the model is a variant of the covering facility location model that must satisfy all demand and GIK space requirements. A computational study with multiple cost minimizing objective functions illustrates how the model performs with realistic data. The results show that strategic planning in the preparedness phases of the disaster management cycle will significantly mitigate the impact of GIK.

This project investigates how remanufacturing of robotic lawn mowers can be incorporated into an existent forward supply chain. The project is conducted as a single case study on Husqvarna where an interview study and a literature study provide the empirical data and theory, respectively. Alternatives are proposed for potential remanufacturing cases at various locations, where different parties ranging from original equipment manufacturers to independent manufacturers perform the remanufacturing process. SWOT analyses are conducted to identify the most promising alternatives for a further economic analysis. The economic evaluation is based on net present values and a sensitivity analysis which together determines the feasibility of the alternatives. The results of the project answer three research questions. The first concludes that out of seven defined production systems there are only two that are not suitable for remanufacturing in a general case mainly due to the low flexibility of these systems. The results of the second identifies labor, logistics, and operational prerequisite factors that must be considered when implementing remanufacturing for case specific alternatives. The conclusion of the third research question lists the feasibility of the alternatives from which the recommendations for Husqvarna are presented. This project recommends Husqvarna to implement a remanufacturing process for their robotic lawn mowers either by enlisting their current dealers or by themselves at a location nearby the spare parts warehouse in Torsvik. Which alternative is the most profitable depends mainly on the expected quantity of the acquired cores, i.e. Husqvarna as a centralized remanufacturer benefits more from higher quantities while the decentralized dealer alternative would comparably be more profitable if the quantities were lower. As it is perceived that initial collected quantities will be low, and possibly even somewhat higher for the dealers, a decentralized remanufacturing process could be the most profitable alternative to start with. Using a third-party remanufacturer is also feasible but considered risky and therefore not recommended as they could have the same core acquisition problem as Husqvarna while having lower profitability.