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Evaluation of Rail Decarbonization Alternatives: Framework and Application
Abstract
The Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework is a comprehensive industry-oriented tool for simulating the deployment of new energy technologies including biofuels, e-fuels, battery-electric, and hydrogen locomotives. By classifying fuel types into two categories based on deployment requirements, the associated optimal charging/fueling facility location and sizing problem are solved with a five-step framework. Life-cycle analysis (LCA) and techno-economic analysis (TEA) are used to estimate carbon reduction, capital investments, cost of carbon reduction, and operational impacts, enabling sensitivity analysis with operational and technological parameters. The framework is illustrated on lower-carbon drop-in fuels as well as battery-electric technology deployments for the US Eastern and Western Class I railroad networks. Drop-in fuel deployments are modeled as admixtures with diesel in existing locomotives, while battery-electric deployments are shown for varying technology penetration levels and locomotive ranges. When mixed in a 50% ratio with diesel, results show biodiesel’s capacity to reduce emissions at 36% with a cost of $ 0.13 per kilogram of CO 2 reduced, while e-fuels offer a potential reduction of 50% of emissions at a cost of $ 0.22 per kilogram of CO 2 reduced. Battery-electric results for 50% deployment over all ton-miles highlight the value of future innovations in battery energy densities as scenarios assuming 800-mi range locomotives show an estimated emissions reduction of 46% with a cost of $ 0.06 per kilogram of CO 2 reduced, compared with 16% emissions reduction at a cost of $ 0.11 per kilogram of CO 2 reduced for 400-mi range locomotives. The NUFRIEND framework provides a systematic method for comparing different alternative energy technologies and identifying potential challenges and benefits in their future deployments.
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Nearly all US locomotives are propelled by diesel-electric drives, which emit 35 million tonnes of CO 2 and produce air pollution causing about 1,000 premature deaths annually, accounting for approximately US$6.5 billion in annual health damage costs. Improved battery technology plus access to cheap renewable electricity open the possibility of battery-electric rail. Here we show that a 241-km range can be achieved using a single standard boxcar equipped with a 14-MWh battery and inverter, while consuming half the energy consumed by diesel trains. At near-future battery prices, battery-electric trains can achieve parity with diesel-electric trains if environmental costs are included or if rail companies can access wholesale electricity prices and achieve 40% use of fast-charging infrastructure. Accounting for reduced criteria air pollutants and CO 2 emissions, switching to battery-electric propulsion would save the US freight rail sector US$94 billion over 20 years.
Light-duty battery electric vehicles (BEVs) can reduce both greenhouse gas (GHG) and criteria air pollutant (CAPs) emissions, when compared to gasoline vehicles. However, research has found that while today’s BEVs typically reduce GHGs, they can increase certain CAPs, though with significant regional variability based on the electric grid mix. In addition, the environmental performance of electric and gasoline vehicles is not static, as key factors driving emissions have undergone significant changes recently and are expected to continue to evolve. In this study, we perform a cradle-to-grave life cycle analysis using state-level generation mix and vehicle operation emission data. We generated state-level emission factors using a projection from 2020 to 2050 for three light-duty vehicle types. We found that BEVs currently provide GHG benefits in nearly every state, with the median state’s benefit being between approximately 50% to 60% lower than gasoline counterparts. However, gasoline vehicles currently have lower total NOx, urban NOx, total PM2.5, and urban PM2.5 in 33%; 15%; 70%; and 10% of states, respectively. BEV emissions will decrease in 2050 due to a cleaner grid, but the relative benefits when compared to gasoline vehicles do not change significantly, as gasoline vehicles are also improving over this time.
The rapid onset of the COVID-19 pandemic in March 2020 marked a challenging time for the country and the U.S. freight industry. Manufacturing slowed, consumer purchasing patterns changed, and for many, shopping moved online. The freight industry suffered a sharp decline in shipments, followed by a surprisingly quick rise. The movement of goods by freight rail had to quickly adapt to meet dynamically changing demand and volatile supply patterns. Despite this disruption, freight rail showed a great deal of resilience and reliability. This report addresses how the rail industry met the challenge of this whiplash in demand, explores impediments to performance during this period and looks beyond the crisis towards the future for the rail sector. The assessment outlined in this report was completed by researchers at Northwestern University’s Transportation Center (NUTC). The results show how the U.S. freight rail industry was an essential component of pandemic resilience, demonstrating a high level of adaptability to meet consumer and business demands.
This study reviews emission estimation models that aim at providing realistic estimates of the emitted greenhouse gases from rail freight transportation. Five models are considered: two models from the MEET project, the ARTEMIS model, the EcoTransIT World model, and the Mesoscopic model. For each of the five models, this paper describes the estimation principles, methodology, and procedure, as well as relevant input parameters. An experimental study demonstrates the impact of train and trip specific parameters on each model’s emission estimate. Results are presented for varying values of a train’s number of wagons, the payload per wagon, the average speed, the trip distance, the number of stops, and the altitude profile along the route. In so doing, given a specific transportation scenario, the paper supports decision makers from industry and researchers to find and apply an appropriate emission estimation model for evaluating the eco-friendliness of rail freight transportation.
This work develops an integrated model approach to estimate emissions from long-haul freight truck and rail transport in the U.S. between 2010 and 2050. We connect models of macroeconomic activity, freight demand by commodity, transportation networks, and emission technology to represent different pathways of future freight emissions. Emissions of particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and total hydrocarbon (THC) decrease by 60%-70% from 2010 to 2030, as older vehicles built to less stringent emission standards retire. Climate policy, in the form of carbon tax that increases apparent fuel prices, causes a shift from truck to rail, resulting in a 30% reduction in fuel consumption and 10%-28% reduction in pollutant emissions by 2050, if rail capacity is sufficient. Eliminating high-emitting conditions in the truck fleet affects air pollutants by 20% to 65%; although these estimates are highly uncertain, they indicate the importance of durability in vehicle engines and emission control systems. Future infrastructure investment will be required both to meet transport demand and to enable actions that reduce emissions of air and climate pollutants. By driving the integrated model framework with two macroeconomic scenarios, we show that the effect of carbon tax on air pollution is robust regardless of growth levels.
This paper proposes a methodology for freight traffic assignment in large-scale road-rail intermodal networks. To obtain the user-equilibrium freight flows, a path-based assignment algorithm (gradient projection) is proposed. The developed methodology is tested on the U.S. intermodal network using the 2007 freight demands for truck, rail, and road-rail intermodal from the Freight Analysis Framework, version 3, (FAF3). The results indicate that the proposed methodology’s projected flow pattern is similar to the FAF3 assignment. The proposed methodology could be used by transportation planners and decision makers to forecast freight flows and to evaluate strategic network expansion options.
This paper develops a dynamic freight network simulation-assignment platform for the analysis of multiproduct intermodal freight transportation systems. At the core of the platform is a model framework for the mode-path assignment problem in multimodal freight transportation networks. The framework consists of three main components: a multimodal freight network simulation component, a multimodal freight assignment component, and a multiple product intermodal shortest path procedure. The freight network simulation component incorporates a bulk queuing model to evaluate transfer delay experienced by shipments at intermodal transfer terminals, classification yards, and ports. The multimodal freight assignment component determines the network flow pattern from a mode-path alternative set calculated by the multiple product intermodal shortest path procedure, based on the link travel costs and node transfer delays from the multimodal freight network simulation component. This model can represent individual shipment mode-path choice behavior, consolidation policy, conveyance link moving, and individual shipment terminal transfer in an iterative solution framework.
The class of mathematical and geographical problems considered in this review is characterized by the following general structure: Suppose that there are given (a) a set of n points distributed in the plane, (b) a numerical weight to be attached to each point, and (c) a set of m indivisible centroids without predetermined locations; then, the location-allocation problem, in its most general form, is to find locations for the m centroids and an allocation of each point, or fraction of a point, to some centroid so as to optimize an objective function
Efforts to reduce energy use in freight transportation usually center around “mode-based” approaches, namely improving the energy efficiency of energy intensive modes, such as truck, and shifting more freight to energy efficient modes, such as rail. In the first part of this paper we review the recent trends and future prospects for these mode-based approaches, finding that despite substantial improvement in the technological efficiency of freight modes and robust growth in the use of intermodal rail since 1980, total freight energy use across all modes in the US has grown by approximately 33%, with proportional growth in carbon emissions. In the second part of the paper we propose use of a “commodity-based” approach, in which freight energy use is disaggregated by contribution of major commodity groups, in order to support efficiency improvement at the commodity level. Two potential applications of the commodity based approach, namely (1) life cycle analysis of energy use for major commodity groups and (2) spatial analysis of freight patterns, are demonstrated using the 1993 US Commodity Flow Survey data. Results of these preliminary findings suggest that commodity groups vary widely in the ratio of energy use in production to energy use in transport, and that for many commodity groups, there may be substantial opportunities for saving energy by redistributing flow patterns. Through development of the commodity-based approach, we also identify the collaborative involvement of shippers and carriers as a key point in improving energy efficiency, since it can be used to both make the mode-based approach more effective and address new issues such as the underlying growth in tonne-km. Benefits for air quality and other transportation issues are also discussed.
Freight transport has undesirable effects on the environment. The most prominent of these is greenhouse gas emissions. Intermodal freight transport, where freight is shipped from origin to destination by a sequence of at least two transportation modes, offers the possibility of shifting freight (either partially or in full) from one mode to another in the hope of reducing the greenhouse emissions by appropriately scheduling the services and routing the freight. Traditional planning methods for scheduling services in an intermodal transportation network usually focus on minimizing travel or time-related costs of transport. This paper breaks away from such an approach by addressing the issue of incorporating environment-related costs (greenhouse gases, to be specific) into freight transportation planning and proposes an integer program in the form of a linear cost, multicommodity, capacitated network design formulation that minimizes the amount of greenhouse gas emissions of transportation activities. Computational results based on an application of the proposed approach on a real-life rail freight transportation network are presented.
Since the rise of globalization, international trade has grown constantly and the demand of transportation services grew with it. Multimodal transportation is a natural evolution of the classical unimodal road transportation, and is a mandatory choice for intercontinental shipments. Optimization techniques always found a challenging but stimulating ground for applications in transportation, and the increase in the number of commodities that are transported every year all around the globe enhanced the interest and the usefulness of operational research methodologies, which are needed to properly manage complex transportation systems. The literature on multimodal transportation optimization is flourishing with interesting and multifaceted problems. Meanwhile, new emerging technologies generate new challenges for researchers. The objective of this literature review is to provide a picture of the state of the art on multimodal freight transportation optimization, for the different combinations of modes. An overview of recently published studies is given, identifying currently ongoing topics and emerging trends for future research.
The cost of hydrogen delivery for transportation accounts for most of the current H2 selling price; delivery also requires substantial amounts of energy. We developed harmonized techno-economic and life-cycle emissions models of current and future H2 production and delivery pathways. Our techno-economic analysis of dispensed H2 costs guided our selection of pathways for the life-cycle analysis. In this paper, we present the results of market expansion scenarios using existing capabilities (for example, those that use H2 from steam methane reforming, chlor-alkali, and natural gas liquid cracker plants), as well as results for future electrolysis plants that use nuclear, solar, and hydroelectric power. Reductions in greenhouse gas emissions for fuel cell electric vehicles compared to conventional gasoline pathways vary from 40% reduction for fossil-derived H2 to 20-fold for clean H2. Supplemental tables with greenhouse gas emissions data for each step in the H2 pathways enable readers to evaluate additional scenarios.
Road freight transportation literature increasingly concentrates on environmental aspects to reduce logistics contribution to carbon dioxide emissions. Some literature reviews highlight specific mitigation and adaptation strategies, but the overarching research directions to identify emerging areas and general trends of the field have not yet been clustered or synthesized. This paper presents a systematic quantitative review of the road freight transportation decarbonization literature leveraging bibliographic coupling and network analysis techniques. It contributes to the understanding of road freight decarbonization and provides recommendations for further investigations of the field by systematically mapping the literature body. This way, key research clusters are outlined and visualized to understand the underlying knowledge structure. The findings reveal a diverse and fast-growing research field, which in large parts focuses on route optimizations, last-mile solutions, and alternative fuels, while offering future research opportunities that address organizational barriers currently hindering collaboration and technological or operational measures for long- haul transportations.
Cost reduction of electric vehicles (EVs), which depends largely on their most cost-intensive component, the battery, is the prerequisite for their market success. To achieve this cost reduction, accurate and detailed cost forecasts are necessary to make the right operational and strategic decisions like focusing on the right technology, product design, or process steps. Driven by this, numerous cost models were developed in recent years. The study at hand gives a systematic overview of the scientific publications within this research field and therefore provides researchers, industry representatives and policy makers with an overview of the benefits, reliability and transparency of the models studied. A total of 633 publications were initially identified. Conducting a systematic pre-selection, we selected the 21 most relevant ones which were analyzed in detail based on 17 characteristics. These characteristics were categorized and discussed in 6 main areas, namely impact of cost models, used cost estimation technique, model architecture and transparency, technology parameters, technical and operational depth of the calculation model, as well as the reported costs. In addition, five major recommendations for future research were derived.
Fuel efficiency is an important consideration in evaluating public-sector investments in multimodal corridors. Two approaches are typically used in corridor studies to forecast railroad diesel fuel consumption: (1) system-average efficiency factors, and (2) detailed analytical estimates derived from train performance calculators. The former method is easy to apply but may not be reflective of the actual mix of trains used. The second method is data- and time-intensive and can only be effectively implemented with specialized software that is not publicly available. An intermediate method is introduced in this paper which allows distinctions among the types of trains that might be utilized in a corridor (e.g., unit, way, and through). A model is estimated from publicly available data that has excellent statistical properties and quantifies the absolute and relative fuel efficiencies of train options. The analysis demonstrates that using system-average fuel consumption factors may significantly understate railroad fuel economy when traffic moves in unit trains instead of mixed train service. The new method offers greater accuracy than system-average comparisons yet is much less data-intensive than train performance calculators or analytical methods.
Among the several candidates of hydrogen (H 2 )storage, liquid H 2 , methylcyclohexane (MCH), and ammonia (NH 3 )are considered as potential hydrogen carriers, especially in Japan, in terms of their characteristics, application feasibility, and economic performance. In addition, as the main mover in the introduction of H 2 , Japan has focused on the storage of H 2 , which can be categorized into these three methods. Each of them has advantages and disadvantages compared to the other. Liquid H 2 faces challenges in the huge energy consumption that occurs during liquefaction and in the loss of H 2 through boil-off during storage. MCH has its main obstacles in requiring a large amount of energy in dehydrogenation. Finally, NH 3 encounters high energy demand in both synthesis and decomposition (if required). In terms of energy efficiency, NH 3 is predicted to have the highest total energy efficiency, followed by liquid H 2 , and MCH. In addition, from the calculation of cost, NH 3 with direct utilization (without decomposition)is considered to have the highest feasibility for massive adoption, as it shows the lowest cost (20–22 JPY·Nm ³ -H 2 in 2050), which is close to the government target of H 2 cost (20 JPY·Nm ³ -H 2 in 2050). However, in the case that highly pure H 2 (such as for fuel cell)is needed, liquid H 2 looks to be promising (24–25 JPY·Nm ³ -H 2 in 2050), compared with MCH and NH 3 with decomposition and purification.
US railroads, under road competition, opted for speed and eventually for dieselisation. This affected the quantity and quality of labour used, the operational and cost characteristics of train services, the design and maintenance of rolling stock and track, and the allocation of capital investment within the railroad company. It was an event of almost revolutionary proportions, in contrast with generally evolutionary change on US railroads. The case of the Atchison, Topeka & Santa Fe Railroad - a leader in the US - from the 1930s to the 1950s is analysed in detail with 12 supporting Tables. The diesel-electric era in the US may prove to be a temporary one, as coal-burning steam locomotives and straight electric technology are now being re-evaluated. -J.H.Farrington
The demand for transportation services is a derived demand (Manheim, 1979). In other words, the demand for such services arises from the need to move goods and/or people from one location to another and not from some inherent desirability of transportation in and of itself. Transportation is necessary only because people and/or goods are not located where they need to be when they need to be there.
A commonly occurring problem in distribution system design is the optimal location of intermediate distribution facilities between plants and customers. A multi-commodity capacitated single-period version of this problem is formulated as a mixed integer linear program. A solution technique based on Benders Decomposition is developed, implemented, and successfully applied to a real problem for a major food firm with 17 commodity classes, 14 plants, 45 possible distribution center sites, and 121 customer zones. An essentially optimal solution was found and proven with a surprisingly small number of Benders cuts. Some discussion is given concerning why this problem class appears to be so amenable to solution by Benders' method, and also concerning what we feel to be the proper professional use of the present computational technique.
After labor expenses, the cost of fuel is the largest operating budget expense item for freight and passenger rail operations in the United States. Because fuel is such a costly component of operations, the railroad industry is constantly researching and testing new methods either to reduce the volume of fuel consumed or to switch to less-expensive fuels and sources of energy. An understanding of the factors that affect fuel consumption and their interactions is valuable for analyzing the feasibility of a given technology. Such knowledge allows for more intelligent extrapolation of simulation and laboratory results across a range of routes and in-service operating conditions. This research investigates the relative effects of infrastructure, equipment, and operating parameters on fuel efficiency for freight and passenger railroads on a mixed-use corridor. Partial factorial experiments investigate the effects of multiple factors on freight and passenger train fuel efficiency. Rail simulation software is used to run trial cases of single-track lines with heterogeneous traffic and to calculate energy-efficiency metrics. Results from the simulations are used to create two multivariate regression models for freight and passenger rail fuel efficiency. A sensitivity analysis identifies the relative effects of these factors on freight and passenger train fuel efficiency. By understanding the relative influence of various parameters on fuel efficiency, practitioners can focus data collection, modeling, and other fuel-saving efforts on the most significant factors.
Railroads have long been serving the largest portion of freight ton-miles in the U.S. freight shipment market. The rising demand for rail transportation is expected to continue in the foreseeable future, and considerable network congestion and inefficiency will result. Thus, a systematic methodology is needed to predict freight flow concentrations and congestion patterns in the rail network. A customized network assignment model for rail freight shipment demand is proposed; in this model, single- and double-track lines are represented by an equivalent directed graph. A railroad-specific link cost function adjusted for single and double tracks is developed to capture traffic delay, and an adapted convex combination algorithm is developed to find the shipment routing equilibrium. This model is applied to an empirical case study for the U.S. rail network with national freight shipment demand in 2007. The resulting freight flow pattern is visualized graphically and validated with empirical data. The proposed modeling framework could be used to help public agencies and private companies predict rail network traffic and develop infrastructure investment strategies to reduce adverse social impacts imposed by rail traffic delay.
This paper provides information about railroad fuel efficiency. Two methods of estimating railroad fuel consumption are demonstrated: (1)an analytical procedure based on train resistance factors, speeds, changes in elevation, and car weights; and (2)a statistical procedure that utilizes observed data. Both procedures are used to estimate the efficiencies of coal, grain, iron ore, and other commodity shipments. The two methods produce similar (but not identical) results. The analytical procedure illustrates the underlying forces that affect fuel efficiency and provides a benchmark for the statistical model.
Intermodal rail/road transportation is an instrument of green logistics, which may help reducing transport related greenhouse gas (GHG) emissions. In order to assess the environmental impact of road and rail transports, researchers have formulated very detailed microscopic models, which determine vehicle emissions precisely based on a vast number of parameters. They also developed macroscopic models, which estimate emissions more roughly from few parameters that are considered most influential. One of the goals of this paper is to develop mesoscopic models that combine the preciseness of micro-models while requiring only little more information than macro-models. We propose emission models designed for transport planning purposes which are simple to calibrate by transport managers. Despite their compactness, our models are able to reflect the influence of various traffic conditions on a transport’s total emissions. Furthermore, contrasting most papers considering either the road or the rail mode, we provide models on a common basis for both modes of transportation. We validate our models using popular micro- and macroscopic models and we apply them to artificial and real world transport scenarios to identify under which circumstances intermodal transports actually effect lower emissions. We find that travel speed and country-specific energy emission factors influence the eco-friendliness of intermodal transports most severely. Hence, the particular route chosen for a transnational intermodal transport is an important but so far neglected option for eco-friendly transportation.
Significant technical, regulatory and media attention has recently been given to the use of electrical storage batteries onboard a line-haul (long-distance) locomotive or “energy storage tender” (coupled adjacent to a locomotive) as a means of improving railroad fuel efficiency and reducing freight locomotive exhaust emissions. The extent to which electrical energy stored onboard could supplement or replace diesel generated power has yet to be quantified or proven. There are significant technical design, maintainability, logistical and safety challenges to making this technology commonplace, especially for over-the-road (line-haul) freight trains.
The use of electrical batteries to provide some amount of point-source fuel- and/or emissions-free locomotive power is not a new concept. Recent claims that onboard storage of locomotive propulsion energy is “new locomotive technology” are unfounded. The world’s first all-battery-powered locomotive was built in 1838 only 34 years after the world’s first steam locomotive operated. A total of 126 identifiable locomotives using onboard batteries to store propulsion energy have been built and operated to some extent in the United States (US) since 1920. Almost all were low-power switching locomotives and none are currently in revenue freight service. Two high-horsepower line-haul experimental engineering test locomotives with an experimental battery design and regenerative dynamic braking have been built (in 2004 and 2007) but very little revenue service testing has occurred.
This paper reviews propulsion battery-equipped locomotives over the past 95 years in the US, and discusses future options and possibilities including the technical and logistical challenges to such propulsion.
Capturing dynamic braking energy (developed by locomotive traction motors during deceleration or downhill operation) could be a source of onboard battery recharging, but will require significant additional locomotive control system development work to achieve practicality. New battery technologies are being developed but none are yet practical for large-scale locomotive applications. Retrofitting of large amounts of onboard battery storage on existing (or even future) diesel-electric locomotives will be limited by onboard space constraints. The development and use of energy storage “tenders” will bring complications to locomotive and train operations to make effective use (if commercialized) practical and safe.
This paper is also intended to provide technical background and clarity for various regulatory agencies regarding battery energy storage technologies for future locomotive propulsion.
Public transit structure is traditionally designed to contain fixed bus routes and predetermined bus stations. This paper presents an alternative flexible-route transit system, in which each bus is allowed to travel across a predetermined area to serve passengers, while these bus service areas collectively form a hybrid “grand” structure that resembles hub-and-spoke and grid networks. We analyze the agency and user cost components of this proposed system in idealized square cities and seek the optimum network layout, service area of each bus, and bus headway, to minimize the total system cost. We compare the performance of the proposed transit system with those of comparable systems (e.g., fixed-route transit network and taxi service), and show how each system is advantageous under certain passenger demand levels. It is found out that under low-to-moderate demand levels, the proposed flexible-route system tends to have the lowest system cost.
The current scarcity of public charging infrastruc- ture is one of the major barriers to mass household adoption of electric vehicles (EVs). Many drivers are reluctant to purchase EVs without convenient charging access away from home, but investors are also hesitant to build charging stations without underlying knowledge of EV demand realization. In this paper, an agent-based decision support system is presented for identifying patterns in residential EV ownership and driving activities to enable strategic deployment of new charging infrastructure. The Chicagoland area is used as a case study to demonstrate the model. I. INTRODUCTION
As organizations electrify their vehicle fleets in order to lower costs, reduce emissions, and improve their public image, they face the problem of providing charging access to their vehicles on the road. Electric vehicles (EVs) have a much shorter driving range than comparable gasoline-powered vehicles, and without charging capabilities away from the depot, an EV's route length is constrained by its battery capacity. This is especially problematic if customers are located far from the depot, since it may not be possible to visit a customer and return to the depot on a single charge. Because public charging access is not usually available for EVs, the fleet owner may need to install or rent charging stations away from the depot for the EVs to use.
This paper attempts to present the major methods, successful or interesting uses, and computational experience relating to integer or discrete programming problems. Included are descriptions of general algorithms for solving linear programs in integers, as well as some special purpose algorithms for use on highly structured problems. This reflects a belief, on the author's part, that various clever methods of enumeration and other specialized approaches are the most efficacious means existent by which to obtain solutions to practical problems. A serious try at gathering computational experience has been made—but facts are difficult to uncover.
The paper is written with intent to enable readers to read selected sections without having to read the whole.
The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current state-of-the-art. In particular, continuous location models, network location models, mixed-integer programming models, and applications are summarized.
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