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Battery electric vehicles as well as renewable energy are two key factors that can contribute significantly to sustainable development within the transportation and the energy sector. However, the market introduction of these technologies results in new challenges, especially with regard to the interaction between both sectors. So far, neither location models for charging stations nor load flow models for the electrical grid consider these interactions sufficiently. Thus, an integration of planning problems from both sectors is needed in order to exploit potential synergies and to avoid negative impacts. In this paper, we present such an integrated planning approach to locate charging infrastructure for battery electric vehicles considering interactions with the electrical grid. Herein, we combine a charging station location model and a power flow model with integrated energy stores. We aim at determining a network configuration that satisfies the charging demand of battery electric vehicles, herein maximizing the benefits and minimizing the negative impacts resulting from the interactions of the two sectors. To demonstrate the benefit of our integrated planning approach, we apply it to an illustrative case and present results of a sensitivity analysis. We derive managerial insights regarding the interdependencies of the number of sited charging stations and the installed storage capacity based on renewable energy generation and charging demand.

Plug-in electric vehicle (PEV) charging stations couple future transportation systems and power systems. That is, PEV driving and charging behavior will influence the two networks simultaneously. This paper studies optimal planning of PEV fast-charging stations considering the interactions between the transportation and electrical networks. The geographical targeted planning area is a highway transportation network powered by a high voltage distribution network. First, we propose the capacitated-flow refueling location model (CFRLM) to explicitly capture PEV charging demands on the transportation network under driving range constraints. Then, a mixed-integer linear programming model is formulated for PEV fast-charging station planning considering both transportation and electrical constraints based on CFRLM, which can be solved by deterministic branch-and-bound methods. Numerical experiments are conducted to illustrate the proposed planning method. The influences of PEV population, power system security operation constraints, and PEV range are analyzed.

This paper investigates the optimal locations for refueling stations for electric vehicles. Electric vehicles have been successfully introduced into the market. However, their use seems to be limited to urban transport since recharging facilities are readily available only near home and work. Planning recharging infrastructure for electric vehicles is highly relevant because this will enable longer trips, including inter-state travel which requires multiple battery charges. Among various models to determine optimal locations of recharging stations, a flow refueling location model (FRLM) is considered in this study. It determines locations for recharging stations to maximize the flow that can travel between origin and destination pairs by refueling at built facilities. FRLM is extended by introducing a probabilistic consideration of the travel range which might vary depending on various factors including road conditions. We develop a mixed integer nonlinear programming formulation and propose a Benders-and-Price algorithm by combining the Benders decomposition and column generation to solve the proposed formulation. The proposed algorithm is validated using extensive computational experiments on two transport networks, including a real-life Texas highway network.

Product remanufacturing is one of the most profitable activities in reverse logistics. Running a business plan, in which companies take responsibility for the waste generated at their end-of-life products, involves making important strategic decisions. One of the challenges in planning the reverse flow of products is decide where installing the reprocessing facilities. This decision influences directly the transport variables costs and the facilities installation fixed costs. This paper proposes a model for the Capacitated Plant Location Problem in Reverse Logistics (CPL-RL), in which we assume that offered material in each collection center is aimed at a single facility for reprocessing. This restriction includes specific cases where there is no logistic availability in the network to send the collected material to different locations. The Mixed Integer Problem (MILP) is solved using an algorithm in two steps. In the first step, reduction tests are performed, which ones determine a priori which facilities are opened/closed. If all facilities are fixed opened or closed then the solution is optimal. Although not all facilities can have their status defined that way, the resultant problem has a less number of variables and it is solved using Benders method. The dataset was randomly generated and the results showed that the applied techniques are appropriate, achieving the optimal solution for all test problems.

The aim of this paper is to propose a location model of earthquake emergency service depot on the basis of hybrid multi-attribute decision-making method. The advantage of the proposed method is that practical mixed uncertainty of location decision information is considered, and the corresponding factors that affect the location of transfer stations are contained. To solve the location problem, a hybrid multi-attribute decision procedure without information transformation is developed. Besides, a novel weighting method and aggregation process is given. Finally, a numerical example is provided to show the feasibility and validity of the proposed method.

This paper presents a model for “Domestic Facility and Hub Location” in transportation network. Number of hubs and facilities are unknown and the objective is to minimize the transportation cost, lost demand penalty and facility set up costs. Also the distribution of demand among domestic facilities and hubs adheres to gravity rule. To solve the problem, sequential and integrated approaches were undertaken. In the sequential approach, domestic facilities were allocated and hubs were selected afterwards. However the integrated approach deals with both concurrently, with a more sophisticated steps reaching to the solution. This is mainly because the demand itself is highly dependent to where the facilities are going to be located. Heuristic solutions were employed and was tested in a set of Turkish network and validated by CPLEX software in small cases.

We develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number of stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.

Fault location is an effective way to ensure quickly power recovery after a failure in power distribution system. Bad operation environment of FTU, damaged elements or lost information usually cause variations in fault information for correlation analysis in fault location of power distribution system. A data mining (DM) correlation analysis model based on rough sets (RS) theory and immune algorithm (IA) is proposed in this paper. Firstly, using RS theory to extract domain knowledge, a set of the given variant fault pattern is converted into a decision table in RS. Secondly, an attribute reduction of the decision table is made by using the IA theory, and the intrinsic correlation rules between input vector (condition attribute) and output vector (decision attribute) are mined. Then, the data mining method is used to deal with the distortion of FTU real-time input information. According to the current limit information sequence of section switches, line fault states in each section are judged to realize the fault location in power distribution network. Finally, the feasibility and effectiveness of the fault location model of distribution network based on RS-IA data mining model is verified by simulation.

The smooth and fast evacuation of a pedestrian group is dependent on guidance services provided by signage systems. This paper investigates the location method of an evacuation signage system in a public space. We proposed a calculation method to determine the guidance efficiency of signage and further present a piece-wise probability function to explain interactions between pedestrians and signage. The interaction between one pedestrian and one signage was extended to the interaction between a pedestrian crowd and a signage system. A location model of the signage system was proposed to determine the minimum number of signs necessary to meet guidance demands. The location model was based on the Cooperative Location Set Cover Problem (CLSCP) and was correspondingly solved by a proposed exponential binary heuristic search algorithm (EBHS), i.e., a combined exponential binary search method and a heuristic search algorithm for solving the Cooperative Maximum Cover Location Problem (CMCLP). Finally, the proposed model was applied to determine the location of an evacuation signage system in a hall. The parameters used in the location model were calibrated based on experimental data. The model results showed that the proposed model can suggest the optimal number and best locations of signs. A sensitivity analysis showed that the guidance capacity of the signage system can be increased by improving the attractiveness of signage and pedestrian trust and familiarity with environment. The same number of signs is suggested for evacuation scenarios wherein crowd following behavior is present.

Most of the existing biomass power plant location models and supply chain designs are developed only for the minimization of logistics cost, having neglected the pollutant emissions during biomass feedstock transportation. How to achieve a win-win situation between the two for optimal power plant locations has become a main research direction. This paper, integrating the economic and environmental dimensions of sustainable development, develops a bi-objective 0–1 integer programming model for optimal locations and corresponding feedstock supply chain designs of biomass power plants. Having made improvements to the existing location models, this model is developed basing on the relevant research data of Chinese biomass power generation. A numerical example analysis is done in order to validate the model and to highlight the practicality and effectiveness. It is expected that this model may provide scientific guidance for biomass power plant siting and improve their environmental performance while still remaining economically competitive.

To facilitate the transition to alternative-fuel vehicles (AFVs), researchers have developed models for optimally locating an initial refueling infrastructure for AFVs with limited driving range. Recently, clustering of stations has emerged as a strategy to encourage consumers to purchase AFVs by building a critical mass of stations. Clustering approaches, however, have focused on serving demands represented as nodes or arcs rather than origin-destination (O-D) trips. This study proposes a Threshold Coverage extension to the original Flow Refueling Location Model that focuses on the percentage of a zone's O-D trips that can be successfully completed given a typical driving range and location of stations. It is motivated by the idea that drivers in an area will not purchase an AFV unless a critical mass of the trips they regularly make can be completed. Therefore, the new model optimally locates p refueling stations on a network to maximize the sum of weighted demand of covered origin zones, where “covered” means that the zone exceeds a specified threshold percentage of their total outbound round trips that are refuelable. The model is tested on networks for Orlando and the state of Florida. As the threshold percentage is raised, fewer zones can surpass the threshold. Covered nodes increasingly cluster together, as do stations for serving their O-D flows. The model's policy implementation will provide managerial insights for some key concerns of the industry, such as geographic equity vs. critical mass, from a new perspective.

Transportation facility and automotive service enterprise location is an interesting and important issue. In practice, such factors as customer demand, allocations, even locations of customers and facilities are usually changing, thus making facility location problematic with uncertainty. To account for it, some researchers have addressed stochastic/fuzzy models for locating an automotive service enterprise. However, probabilistic/fuzzy models are not suitable to describe all kinds of uncertainty, but only randomness or fuzziness. In fact, the uncertain environment of locating an automotive service enterprise is a mixed one with both randomness and fuzziness. To handle this issue in a practical manner, this work proposes fuzzy random tradeoff issues for it. Moreover, some regional constraints can greatly influence its location. By taking the vehicle inspection station as a typical example, this work presents new fuzzy random cost–profit tradeoff models of its location problem with regional constraints. A hybrid algorithm integrating fuzzy random simulation and genetic algorithms is adopted to solve the proposed models. Additionally, some risk factors have a great impact on decision making when faced with a location problem. This work thus conducts a risk performance analysis for locating an automotive service enterprise. Some numerical examples are given to illustrate the proposed models. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A new traffic sensor location problem is developed and solved by strategically placing both passive and active sensors in a transportation network for path reconstruction. Passive sensors simply count vehicles, while active sensors can recognize vehicle plates but are more expensive. We developed a two-stage heterogeneous sensor location model to determine the most cost-effective strategies for sensor deployment. The first stage of the model adopts the path reconstruction model defined by Castillo et al. (2008b) to determine the optimal locations of active sensors in the network. In the second stage, an algebraic framework is developed to strategically replace active sensors so that the total installation cost can be reduced while maintaining path flow observation quality. Within the algebraic framework, a scalar product operator is introduced to calculate path flows. An extension matrix is generated and used to determine if a replacement scheme is able to reconstruct all path flows. A graph model is then constructed to determine feasible replacement schemes. The problem of finding the optimal replacement scheme is addressed by utilizing the theory of maximum clique to obtain the upper bound of the number of replaced sensors and then revising this upper bound to generate the optimal replacement scheme. A polynomial-time algorithm is proposed to solve the maximum clique problem, and the optimal replacement scheme can be obtained accordingly. Three numerical experiments show that our proposed two-stage method can reduce the total costs of transportation surveillance systems without affecting the system monitor quality. The locations of the active sensors play a more critical role than the locations of the passive sensors in the number of reconstructed paths.

The traditional approach to origin–destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor detected it. Based on these data, the subset of sensors that detected a given vehicle and the order in which they detected it are available. In this paper, four location models are proposed, all of which consider the order of the sensors. The first model always yields the minimum number of sensors to ensure the uniqueness of path flows. The second model yields the maximum number of uniquely observed paths given a budget constraint on the sensors. The third model always yields the minimum number of sensors to ensure the uniqueness of OD flows. Finally, the fourth model yields the maximum number of uniquely observed OD flows given a budget constraint on the sensors. For several numerical examples, these four models were solved using the GAMS software. These numerical examples include several medium-sized examples, including an example of a real-world large-scale transportation network in Mashhad.

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.

This paper proposes a sensor location model to identify a sensor configuration that minimizes overall freeway performance monitoring errors while considering the consequences of probabilistic sensor failures. To date, existing sensor location models for freeway monitoring inherently assume that either deployed sensors never fail or the consequences of sensor failure are trivial matters. However, history has revealed that neither assumption is realistic, suggesting that ignoring failures in sensor allocation models may actually produce a significantly suboptimal configuration in the real world. Our work addresses this dilemma by developing a probabilistic optimization model that will minimize the error expectation by examining all possible failure scenarios, each with an occurrence probability. To ensure the scenario completeness and uniqueness, a sensor failure scenario is represented by using a binary string with 1 indicating an operational sensor at a given site and 0 for sensor failure or no sensor deployed. When applied to a case study network, it is shown that an optimal configuration that considers sensor failure is significantly different from an optimal configuration that ignores sensor failure, revealing that sensor failures pose non-trivial consequences on performance monitoring accuracy.

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.

A multi-period multipath refueling location model is developed to expand public electric vehicle (EV) charging network to dynamically satisfy origin–destination (O–D) trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost-effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites.

In this study, we consider field hospital location decisions for emergency treatment points in response to large scale disasters. Specifically, we developed a two-stage stochastic model that determines the number and locations of field hospitals and the allocation of injured victims to these field hospitals. Our model considers the locations as well as the failings of the existing public hospitals while deciding on the location of field hospitals that are anticipated to be opened. The model that we developed is a variant of the P-median location model and it integrates capacity restrictions both on field hospitals that are planned to be opened and the disruptions that occur in existing public hospitals. We conducted experiments to demonstrate how the proposed model can be utilized in practice in a real life problem case scenario. Results show the effects of the failings of existing hospitals, the level of failure probability and the capacity of projected field hospitals to deal with the assessment of any given emergency treatment system’s performance. Crucially, it also specifically provides an assessment on the average distance within which a victim needs to be transferred in order to be treated properly and then from this assessment, the proportion of total satisfied demand is then calculated.

Internal guidance signs are critical for the normal operation of a comprehensive passenger transportation hub. In the present paper, the procedures, methods and restrictions on the development of guidance sign locations are summarized in terms of the planning and design method of guidance signs and an evaluation of guidance sign service manuals after a sorting and analysis of relevant literature. The results show that the existing location model fails to quantitatively describe the behavior characteristics of massive passenger flows and results in the spatial planning and design of guidance signage being inaccessible for many in mass passenger transit and spatial topological networks resulting from 3D hub architecture make the constraint conditions of location models more complex. Sophisticated guidance signs and location models responding to behavioral characteristics and spatial topological networks will be a trend of interest in the optimal layout of future comprehensive passenger transportation hubs.

A facility location problem considers locating a certain number of facilities with the objective of finding their best locations. For most real life situations, it is more realistic to consider the requirement of satisfying a demand with multiple facilities in order to ensure a backup supply. The back-up supply is necessary especially for public or emergency service location problems where a covered demand may not be serviced if its designated facility is engaged serving other demands. Moreover, this issue is more likely to occur when the number of demand locations is much higher. In this study we consider two classic location models, the p-median and maximal coverage location, and compare their performances with respect to five decision criteria under Q-coverage requirement. For this purpose we generate random problem instances and solve each instance with both models for different Q-coverage values. Our comparisons reveal the tradeoffs in selecting the location model for a given problem when the decision maker assesses the performance with multiple criteria.

In this article, we extend the modified Box–Meyer method and propose an approach to identify both active location and dispersion factors in a screening experiment. Since several candidate models can be simultaneously considered under the framework of Bayesian model averaging, the proposed method can overcome the problem of missing the identification of some active factors caused by either the alias structure or misspecification of the location model. For illustration, three practical experiments and one synthetic data set are analyzed.

In this paper, we introduce a two-stage stochastic refueling station location model, where the first stage locates permanent stations and the second locates portable stations. The portable alternative fuel stations are an innovative feature in transportation network. The models are applied to an intercity network for Arizona. Computational results show that the permanent stations locate in and around heavily populated nodes. In addition, the results obtained for the portable stations can be utilized to set up permanent stations when the investor intends to increase the number of such stations. The computational results of the exact and greedy approach are reported.

The article deals with the possibilities of optimizing the postal transportation network with respect to planned road infrastructure. The research adopted in this article uses allocation models within graph theory to obtain results for addressed optimization problem. The article presents and compares two types of these models – p-median and uncapacitated fixed charge facility location model. The latter is subsequently applied on the postal network to determine the optimal location of postal facilities while minimizing costs. Moreover, the article describes the possibilities of identifying and calculating input variables of the used model, creating the underlying network, as well as possible further improvements of obtained solution. The results can serve as a basis for modification of the used model for the simulation of networks in the postal sector.

Central business district(CBD)construction is in rapid development phase at present, therefore, the firefighting work in CBD becomes an important issue for safety. In this paper, a fire risk assessment index system is established from the perspective of regional characteristics, possible sources and factors which influence the occurrence of fire. Analytic hierarchy process(AHP)is used to obtain the weights of different indexes so as to reflect their effects on the final fire risk assessment. Then, the fire risk of CBD in Binhai New Area of Tianjin is assessed with the help of the proposed model and ArcGIS technique. Finally, the fire station layout is optimized based on the discrete location model, realizing the reasonable allocation of firefighting resources. According to the analysis, super high-rise buildings and underground spaces are main factors that cause high fire risk; furthermore, five firstlevel fire stations can satisfy the requirement of rescue response time.

In this study, the optimal locations of a specific type of charging facilities for electric vehicles (EVs), wireless power transfer facilities, are investigated. A mathematical model has been developed to address this problem. The objective of the model is to locate a given number of wireless charging facilities for EVs out of a set of candidate facility locations for capturing the maximum traffic flow on a network. The interaction between traffic flow patterns and the location of the charging facilities is incorporated explicitly by applying the stochastic user equilibrium principle to describe electric vehicle drivers’ routing choice behavior. Firstly, the problem is formulated into a mixed-integer nonlinear program, secondly a solution method is developed to obtain the global optimal solution of the linearized program. Numerical experiments are presented to demonstrate the model validity.

Internet is changing customers’ consumption patterns and the manufactures’ sale model. With the development of the computer network technology and the electronic commerce, more and more firms establish the electronic sale channel and get great profits. The huge supply chain network is established through the new idea and the technology. In this paper, we propose an improved local search algorithm- clustering local search algorithm (CLSA) to solve the hybrid location problem. We apply this method to distribution center location model and get the optimal solution. Result shows that this method can avoid the exponential explosion and get a good solution. In numerical analysis, we compare this method with simulated annealing method and ant searching algorithm. The numerical analysis demonstrates that this CLSA method not only classifies simply and flexibly, but also has the characteristic of fast search speed and small search space.

Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the "big probability" of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.

This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the characteristic of social vulnerability, must be included in the system. In addition to the theoretical contribution to the reverse logistics network problem, this study aids in decision-making for public managers who have limited technical and administrative capacities for the management of solid wastes.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Dominance of fossil fuels in the transportation sector has led to environmental and economical problems and transition to Alternative Fuel Vehicle (AFV) has become a necessity. Electric Vehicle (EV) is one of the AFVs that deliver clear benefit of zero tailpipe emissions. The EVs have a limited range and their use is influenced by the location of recharging facilities. The charging time of EV is long and recharging in stations take a lot of time. In addition, the capacity of recharging stations is limited and if the capacity is occupied the vehicles must wait in queues. In this paper, a recharging station location model with queue is proposed which considers capacity, recharging time, and waiting time. The presented model is a set covering and can easily become a maximum cover model with or without predefined activities. A heuristic algorithm to solve the model is proposed and the model is evaluated using two networks under various conditions; a random generated and a network of Penghu County. The obtained results indicate that the model is able to identify the location and the number of the recharging stations and provide a good travel plan especially for tourism.

In order to solve the location problem of the city disaster emergency rescue facilities, it applied the multi-objective decision method to resolve the problem. Firstly it described the types of emergency facilities in the city and proposed the basic principles of the location decision. Then it analyzed the four factors of the location decision, and it established the multi-objective decision model of the emergency rescue facilities location, which based on the integration of traditional location models. And then it used the linear weighted sum law to solve the model, and can readjust the weights of the goals to reach the satisfying plan. Finally, it applied the model to solve the emergency shelter location problem of the new area in the Zhengzhou. The result shows the feasibility and the effectiveness of the model which used the multi-objective decision method to solve the location problem of the city disaster emergency rescue facilities.

The lack of sufficient public fueling stations for Alternative Fuel Vehicles (AFVs) has greatly hindered their adoption. In this paper, we describe a novel Alternative Fueling Station (AFS) location model by considering the behaviors of AFV users who are willing to deviate slightly from their most preferred routes to ensure that their AFVs with limited travel ranges can be refueled en route to their destinations. The model considers multiple deviation paths between each of the origin–destination (O-D) pairs. It relaxes the commonly adopted assumption that travelers only take a shortest path between any O-D pairs. The model provides the most cost-effective deployment strategy of siting AFSs that are needed on the network to satisfy AFV demand between all O-D pairs. We examine the model on two test networks, the Sioux Falls network and a 25-node network, and draw insights into the numerical tradeoffs between station deployment, vehicle ranges, and route deviations. The results show that deviation paths can greatly reduce the cost of establishing AFSs on networks without compromising user convenience much. In addition, an “elbow point” rule is used to identify the most cost-effective AFV travel range in terms of the total cost of building AFSs.

We apply a flow-based location model, called Multipath Refueling Location Model (MPRLM), to develop an electric vehicle (EV) public charging infrastructure network for enabling long-haul inter-city EV trips. The model considers multiple deviation paths between every origin-destination (O-D) pairs and relaxes the commonly adopted assumption that travelers only take a shortest path between O-D pairs. This model is a mixed-integer linear program, which is intrinsically difficult to solve. With greedy-adding based heuristics, the MPRLM is applied to optimally deploy EV fast charging stations along major highway corridors in South Carolina. Compared to engineering methods, the optimization model reduces the capital cost of establishing a fast charging network by two thirds. We also explore the interplay between the spatial distributions of cities, vehicle range, and routing deviation tolerance as well as their impacts on the locational strategies.

Shipping information-based management is rapidly developing, which results in increasingly research on location-sensing technologies for shipboard environment. However, these technologies have not been extensively applied on shipboard environment for two fatal limitations. Firstly, they waste resources of node when sometimes fewer nodes are asking for the positioning requirements. Secondly, they ignore the privacy protection while improving the positioning accuracy. In this paper, a new method called Multi-scale Localization Method is proposed, which is based on RSS information. A multi-precision location model is constructed from step level to room level to meet different requirements and protect privacy of people. Meanwhile, a Filtering algorithm is utilized to reduce the localization error by decreasing RSSI noise. Computer simulations and real experiments have been conducted to evaluate the method on shipboard environment, the result shows that it performs well with better scalability, lower cost, and customized privacy protection.

In life-threatening situations where every second counts, the timely presence of firefighter services can make the difference between survival and death. Motivated by this, in collaboration with Fire Department Amsterdam-Amstelland in the Netherlands, we developed a mathematical programming model for determining the optimal locations of the vehicle base stations, and for optimally distributing firefighter vehicle types over the base stations. The model is driven by practical considerations. It (1) allows for fixing any subset of existing base locations that cannot be relocated (e.g., for historical reasons); (2) includes multiple vehicle types, each of which may have a type-dependent response-time target; and (3) includes crews that consist of arbitrary mixtures of professional (i.e., career) and volunteer firefighters. Extensive analysis of a large data set obtained from the Fire Department Amsterdam-Amstelland demonstrates: (1) that a reduction of over 50 percent in the fraction of firefighter late arrivals can be realized by relocating only three of the current 19 base locations; and (2) that adding new base locations to improve performance is unnecessary: optimization of the locations of the current base stations is as effective, and saves money. The results show an enormous potential for substantially reducing the fraction of late arrivals of firefighter services, with little investment in relocating a small number of stations.

Location Model is a classification model that capable to deal with mixtures of binary and continuous variables simultaneously. The binary variables create segmentation in the groups called cells whilst the continuous variables measure the differences between groups based on information inside the cells. It is important to note that location model is biased and even impossible to be constructed when involving some empty cells. Interestingly from previous studies, smoothing approach managed to remedy the effects of some empty cells. However, numerical analysis has demonstrated that the performances of the location model based on smoothing approach are good in most situations except if there are outliers in the sample. Thus, the presence of outliers has alarmed us to do further investigation towards the performance of the location model. Instead of transformations or truncation, many researchers used various robust procedures to protect their data from being distorted by outliers. Therefore, in this paper, we develop a new methodology of the location model through new estimators resulting from an integration of robust estimators and smoothing approach to address both issues of outliers and empty cells simultaneously. It is expected that this new methodology will offer another potential tool to practitioners, which is possible to be considered in classification problems when the data samples contain outliers and at the same time could resolve the crisis of some empty cells of the location model.

A multipath refueling location model is developed to take into account the effects of vehicle range and multiple deviation paths. It is formulated as a mixed integer linear program, which is intrinsically difficult to solve with increase in the number of deviation paths and network size. This study is focused on developing heuristic approximation solutions, specifically the greedy-adding and greedy-adding with extension algorithms. These algorithms are shown to be efficient and effective to solve the model for the Sioux Falls network. The heuristics are also applied to locate electric vehicle charging stations in the state of South Carolina.

Concrete mixing station location occupies a very important position in the supply chain of the commercial concrete, location not only affects the cost, but the level of quality of concrete delivery service. In view of the traditonal location model only pursuing the cost minimization and neglecting serive level, the reliability is introduced into location model as a standard of concrete delivery service, to establish a multi-objective model which uses the cost minimization and reliability maximization as the objective function. Finally, the effectiveness of the model was confirmed by a numerical example.

In this paper we present a series of more rigorous steps, using a location model and take into account the actual roads’ condition, rents and other relevant factors to solve the network location problem. And we put forward a new idea to solve the "the last kilometer" problem of e-commerce’s network optimization.

The paper builds the model of direct reuse reverse logistics center location followed by the uncertainty random variables of the demand of retailers and the recovery of collection points. This model assumes that the enterprises are expanding on the traditional network. For the random variables, they will be solved by using stochastic simulation, genetic algorithm and linear programming, and numericai exampie is presented.

As a promising transport in the future, electric vehicles plays an important role in people's lives and energy conservation. Planning of electric vehicle charging stations has a far-reaching significance for the popularity of electric vehicles. In this paper, we discuss the siting problem of electric vehicle charging station and propose a two-step method of optimization method. Firstly, we establish a charging station location model, then use Voronoi diagram to determine the preliminary zone, finally we get this problem optimally solved by immune algorithm.The example verifies feasibility of this model.

It is recognized widely that the electric vehicle is of vast potential as an environment-friendly traffic tool. And the construction of the charging and battery swap stations is of critical importance for popularizing it. However, the deployment of the charging station is far from mature even though the construction is being undertaken in some cities. In order to reduce the cost and land use, many researchers and practitioners advocate an oil-electricity union, i.e., we can build the charging and battery swap stations at the existing gas stations. This paper proposes a new location model based on the set cover model taking the existing traditional gas station network as the candidate sites to determine the distribution of the charging and battery swap stations. A numerical experiment is presented to illustrate and verify the proposed model. Our model can help governments to promote the development of the electric vehicle.

Distribution networks have been facing an increased exposure to the risk of unpredicted disruptions causing significant economic losses. The current literature features a limited number of studies considering fortification of network facilities. In this paper, we develop a reliable uncapacitated fixed-charge location model with fortification to support the design of distribution networks. The model considers heterogeneous facility failure probabilities, one layer of supplier backup, and facility fortification within a finite budget. Facility reliability improvement is modelled as a nonlinear function of fortification investment. The problem is formulated as a nonlinear mixed integer programming model proven to be -hard. A Lagrangian relaxation-based heuristic algorithm is developed and its computational efficiency for solving large-scale problems is demonstrated.

The emergency service facilities location is one of the most important issues in the emergency management. This study focused on the location set covering problem (LSCP) for emergency service facilities under fuzzy environment. On the basis of defining the signed distance of trapezoidal fuzzy number, the ranking rules of trapezoidal fuzzy number are defined, the model of emergency service facilities location with fuzzy restraints is proposed, and the solution algorithm for the emergency service location based on the ordering rules of trapezoidal fuzzy number is given. Finally, a numerical simulation is used to illustrate the application of the method, which shows that the algorithm is feasible and advantageous.

A model was developed for the location of rapid charging stations for electric vehicles (EVs) in urban areas, taking into account the batteries’ state of charge and users’ charging and traveling behaviors. EVs are one means of preparing for the energy crisis and of reducing greenhouse gas emissions. To help relieve range anxiety, an adequate number of EV charging stations must be constructed. Rapid charging stations are needed in urban areas because there is inadequate space for slow-charging equipment. The objective function of the model is to minimize EVs’ travel fail distance and the total travel time of the entire network when the link flow is determined by a user equilibrium assignment. The remaining fuel range (RFR) at the origin node is assumed to follow a probabilistic distribution to reflect users’ charging behavior or technical development. The results indicate that the model described in this paper can identify locations for charging stations by using a probabilistic distribution function for the RFR. The location model, which was developed on the basis of user equilibrium assignment, is likely to consider the congested traffic conditions of urban areas, to avoid locating charging stations where they could cause additional traffic congestion. The proposed model can assist decision makers in developing policies that encourage the use of EVs, and it will be useful in developing an appropriate budget for implementing the plan.

The demand for rail transportation in North America is expected to increase significantly in the coming decades. Additional capacity will be required to accommodate the new traffic demand for both passenger and freight services. The majority of the network is single track with passing sidings, which trains use for meets and passes; therefore, allocating sidings properly can significantly increase line capacity and reduce train delay. Railroads usually rely on experienced personnel to determine new siding locations to improve line capacity through infrastructure upgrades. Experienced railroaders often identify good solutions; however, this method does not guarantee that all good alternatives have been evaluated or that the best one is implemented. In this research, an optimal siding location model is developed with consideration of infrastructure and traffic characteristics to determine the optimal number and locations of sidings. The empirical results demonstrate that this model is able to generate an optimal plan for the number of additional sidings and their respective locations. This tool can therefore help railroads maximize their return on investment from capacity expansion projects and achieve the service quality desired by customers.

The rational layout of emergency facilities has a critical effect on the reduction of the damage caused by the disasters of hazardous waste leakage. This paper proposes an emergency covering location model adopting arc covering continuous decline function. According to the features of the arc covering emergency location model, a heuristic algorithm is adopted to solve the model. Numerial examples are used to verify the validity of the proposed model and algorithm. It is concluded that building more emergency points should combine with enhancing network coverage.

This paper proposes a continuous covering location model with risk consideration. The investigated model is an extension of the discrete covering location models in continuous space. The objective function consists of installation and risk costs. Because of uncertain covering radius, customer satisfaction degree of covering radius is introduced by fuzzy concept. Since, the uncertainty may cause risk of uncovering customers; the risk cost is added to the objective function. The installation cost is assigned to a zone with a predetermined radius from its center. The model is solved by a fuzzy method named α -cut. After solving the model based on different α -values, the zones with the largest possibilities are determined for locating new facilities and the best locations are calculated based on the obtained possibilities. Then, the model is solved to determine the best covering values. This paper, also introduces a risk analysis method based on Response Surface Methodology (RSM) to consider risk management in the location models. Finally, a numerical example is expressed to illustrate the proposed model.

With a particular emphasis on the end-to-end travel time prediction problem, this paper proposes an information-theoretic sensor location model that aims to minimize total travel time uncertainties from a set of point, point-to-point and probe sensors in a traffic network. Based on a Kalman filtering structure, the proposed measurement and uncertainty quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors. By considering only critical paths and limited time intervals, this paper selects a path travel time uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework with a unified modeling of both recurring and non-recurring traffic conditions. An analytical determinant maximization model and heuristic beam-search algorithm are used to find an effective lower bound and solve the combinatorial sensor selection problem. A number of illustrative examples and one case study are used to demonstrate the effectiveness of the proposed methodology.