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The speed and robustness of fault section location directly affect the resilience of the distribution network when the latter is exposed to extreme events. This article is proposing a fast and robust fault section location method for distribution system considering the outage reports from smart meters in low-voltage network and notifications from remote fault indicators (RFIs) in medium-voltage network. To make section location faster, a proposed location model moves underreporting variables and misreporting variables to offline recognition. To make section location more robust, the location model proposes new constraints of expected RFI states to remove the limitation of multiple faults on different paths to all sources. Simulation results on a utility feeder with distributed generations demonstrate that the proposed method has faster location and better robustness, which is helpful to the resilience enhancement of the distribution network.

Electrified transportation systems and renewable energy resources have been recognized as effective environmental-friendly technologies against global warming contributed by greenhouse gas (GHG). Remarkably, hydrogen fuel cell-powered electric vehicles (FCEVs) can outperform battery-powered electric vehicles (BEVs) largely in the sense of the driving range and the refueling time. However, both of them requires a better coordination of infrastructure system and renewable energy resources to achieve a significant reduction of GHG emissions. This paper aims to maximize the long term profitability for the planning model of hydrogen refueling stations where the Capacitated Flow Refueling Location Model (CFRLM) is leveraged for maximal traffic flow coverage. Furthermore, we discuss various real-world constraints, such as Traffic Network (TN) constraints, Distribution Network (DN) constraints, hydrogen balance constraints, and energy constraints for EVs, to make the planning model more practical. By considering the uncertainty of the short-term refueling demand across the city, an approach for geographically redistributing hydrogen among the stations is also presented where the minimal cost of redistribution is modelled by One-commodity Pickup-and-Delivery Traveling Salesman Problem. A real-life case study of western Sydney is adopted to testify the efficiency of the planning model under current and future cost levels. Finally, a numerical simulation is utilized to demonstrate the validity of the hydrogen redistribution method.

Society 5.0 refers to an advanced society based on big data, artificial intelligence, sensors, and robots to improve many aspects of life in a smart city. The role of sensors in Society 5.0 is critical. Sensors and the Internet of Things can be considered to work as a service system. Specifically, sensors can track millions of objects to support city security. This article considers the competitive sensor networks in terms of Quality of Service, which can be quantified by service price. The sensors are modeled as an $M/M/1/n$ queueing service system with a location on a secured grid area. A competitive admission fee is considered for tracking orders from society, which makes the arriving tracking demands price-sensitive. Moreover, a bi-level nonlinear program is developed wherein the first level maximizes the sensors’ revenue and social benefit revenue while also minimizing the wait time of tracking orders from society; in the second level, the expected damage cost is minimized from a disruptive scenario toward valuable infrastructure. Moreover, the second-level model is linearized, and it solves the problem by a branch and bound and enumeration algorithm. Finally, an illustrative example of the proposed model is presented.

We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights., Science Academy of Turkey (TÜBA) BAGEP Program

In order to reduce the influence of monitoring blind area caused by the uncertainty of fault resistance on voltage sag source location in transmission line short-circuit fault, a voltage sag source location estimation method based on optimal configuration of system monitoring points and fault parameter estimation is proposed. Considering the observability of voltage sag in various short-circuit faults, the importance index of measuring points in the system is proposed, so as to construct the optimal configuration model of monitoring points. Based on the optimal configuration scheme of monitoring points, the sag source location model is established, and the adaptive cuckoo algorithm is used to estimate the parameters of fault line, fault resistance and fault distance. Finally, the simulation is verified by IEEE-30 node standard test system. The results show that the proposed method has higher fault location accuracy and more engineering applicability than the traditional method.

Reasonable layout of logistics centers in the ASEAN countries has important practical significance for improving logistics network efficiency and reducing logistics transportation costs. This paper based on the current status of the ASEAN logistics network and the characteristics of the hub-and-spoke network, taking the minimum total cost as the optimization goal, establishing the central location model, and the design self-adaptive genetic algorithms to solve it. Therefore, the number of central nodes in the ASEAN logistics network has been determined, the radiation range between nodes, and route allocation. The results indicate that the ASEAN network layout model is a hub-and-spoke logistics network. With Singapore as the logistic center hub node. Jakarta logistics development belt will form the main radiating logistics demand cities of Kuching and Ho Chi Minh city. Kuala Lumpur logistics development belt’s main radiating logistics demand cities will be East Kalimantan and Da Nang city. Chon Buri logistics development belt mainly radiates the cities of Phnom Penh and Riau city. Bangkok logistics development belt mainly radiates the cities of Hanoi, Vientiane, and Yangon city.

This paper develops a location model of media bias with asymmetric media quality. In the model, media quality is defined as an ability to reduce the boundedly rational consumers’ efforts in reasoni...

Purpose The urban-rural divide in developing countries such as India often finds focus in every economic analysis. This paper aims to find the existing gap and to suggest an action plan to reduce the gap identified therein. With an aim to find a good leader in furtherance the group performance operating in rural areas, a multi-plant location model is tested taking its weighted assessment method on assumptions that the unorganized sector is devoid of accessing any scientific model for its growth and sustenance. Design/methodology/approach In this research, two different business groups in the same city location were taken as samples and the multi-plant location (Brown–Gibson) model was used to test the impact of any changes in leadership on the group. Findings The result in the first sample group indicated incremental profitability which was under observation for three years. The second group witnessed a varied trend of profitability under two different leaders which was studied for a four-year period. Research limitations/implications Purposive behavioural alignment under a controlled research environment often dampens the real objective of the study. A meticulous effort was meted out to remove it from research. Practical implications The research aims at providing a long-standing solution to leadership issues in the unorganized sector that contributes to the national economy but usually kept neglected. Originality/value Scientific model experimentation on human resources is unique and innovative.

This paper studies the problem of cooperative searching for dynamical moving targets by multiple unmanned aerial vehicles (UAVs). The environmental information possessed by UAVs is inconsistent due to packet losses of shared environmental information in communication channels and the discrepancies of detected information among different UAVs. To unify the environmental information among UAVs, the lost information is compensated for by an improved Least Square Method (LSM) which incorporates the target location model into the fitting function to enhance data fitting precision. The Weighted Averaging Method (WAM) is used to merge multiple source information where the weight coefficients are set based on the uncertain values of environmental information. To search for dynamic targets and then automatically re-enter into search areas for UAVs, a Modified Genetic Algorithm (MGA) and rolling optimization techniques are utilized to generate real-time paths for UAVs. Simulation results and comparison studies with existing methods validate the effectiveness of the above cooperative searching strategy.

As a new logistics mode, overseas warehouse can save transportation time for cross-border logistics and also solve the problem of customer return and exchange as soon as possible. This paper takesmanufacturing cross-border e-commerce enterprise logistics as the starting point, considering cost, market, environment, society and other factors, establishes a multi-objective overseas warehouse location model to set up alternatives, analyzes the location model by analytic hierarchy process, selects the optimal solution of overseas warehouse location in the alternatives, and verifies the method by an example. Finally, it puts forward targeted suggestions for manufacturingcross-border e-commerce enterprises to establish overseas warehouses, and then promotes the new logistics mode of manufacturingcross-border e-commerce overseas warehouses to mature and standardize.

Background: This study aims to design a multi-objective model for locating emergency relief bases with maximum coverage and minimum costs. As a result, this model is expected to reduce mortality by increasing the efficiency of relief services. Materials and Methods: Based on the ambulance distribution in the east of Tehran City, Iran, and the statistical information on demand (at least 30 samples for data normalization) in the last six months of 2018, we introduced and implemented a definitive mathematical model. We also evaluated the model with GAMS software. Using previous studies and interviews, we identified key and practical indicators of site locations. These indicators are being easy to access, locating in high-demand areas, such as an urban area, and not being too close to another relief base. These factors were then prioritized using the hierarchical method, and the output indicates the high weight of the factor of “being in a place with high demand”. The objective functions are to maximize coverage, minimize costs, and provide equity in relief time. To deal with the uncertainty of the parameters, we used the robust optimization approach. To initially select potential proposed sites to establish the database, we used Geographic Information System (GIS). To test the above mathematical model in the real world, we conducted a case study in East Tehran. Results: Based on the designed model, the initial points proposed by GIS were identified. Finally, it was found that the number of stations in East Tehran must increase from 27 to 34 bases. Conclusion: By implementing this model, the emergency medical service can provide the highest level of coverage. Also, the maximum relief time at stations will be reduced to 8 minutes.

The purpose of emergency medical systems (EMS) is to save lives and reduce injuries with a quick response in emergencies. The performance of these systems is highly dependent on the locations of ambulances and the policy for dispatching them to the customers (i.e., patients). In this study, two new mathematical models are presented to combine the decisions about the location and dispatching policy by integrating the location and hypercube queuing models. In the presented models, the flow-balance equations of the hypercube queuing model are considered as the constraints of the location model. In the first model, the status of each server is idle or busy at any moment, as in the classic hypercube queuing model. In the second model, the travel time is considered independent of the on-scene time, and the status of each server is idle, busy, and traveling, or busy and serving a customer on the incident site. To verify the models, some small-sized examples are first solved exactly. Then, an optimization framework based on the genetic algorithm is presented due to the complexity of the models for solving larger-sized examples. Two approaches (i.e., the exact and simulation-optimization) are used to extend the framework. The results demonstrate that the proposed optimization framework can obtain proper solutions compared to those of the exact method. Finally, several performance measures are considered that can only be calculated using the second model.

Improving customer satisfaction and shortening the manufacturing cycle have become common concerns of current manufacturers. This paper presents a multi-objective location model considering the maximization of collaborative manufacturing capabilities and service benefits. This method first uses the two dimensions of customer share and market consumption to segment customers, and identify the weight of various customer groups. Secondly, the space vector model (VSM) is used to calculate the matching between manufacturing capabilities and manufacturing requirements. Then build a multi-objective location model based on the two goals of collaborative manufacturing capabilities and service benefits. Finally, the model was tested with simulation data, which proved the validity and feasibility of the model. According to the simulation results, managers can accurately select the optimal manufacturing base from multiple candidate manufacturing bases with regard to less costs, shorter lead times, better manufacturing capabilities, better service benefits. In this paper, Fuzzy theory, Logit model and VSM are combined to salve the problem of manufacturing base location. Considering resources and service benefits of each manufacturing base, it is helpful to optimize the location of enterprises. From the academic and practical points, this study provides a new perspective for the location problem.

Purpose This paper aims to theoretically investigate an online company’s optimal decision on its offline expansion strategy. In the past five years, many large online retailers and internet-based companies such as Amazon, Google, Alibaba, Tencent and JD.com have expanded their offline market but it was observed that they adopted different expansion strategies. Specifically, some of them expand the offline market by acquiring offline retailers, while some do so by purchasing a portion of offline retailer’s stake. This difference leads to a quite different structure in post-expansion market, having an impact on profit, consumer surplus and social welfare. The goal of this paper is to model such expansion strategies in a general way and complete studies on profits and welfare. Design/methodology/approach By constructing a Salop model with two offline retailers and one online company, this paper analyzes the case where the online company can expand its offline market by either acquiring or jointing (e.g. stakeholding) with one offline retailer. The former strategy (named Strategy A) allows the online company to fully control and capture residual claims of the offline retailer. With the adoption of the latter strategy (named Strategy C), on the other hand, the online company can obtain a fixed proportion of its offline partner’s quasi rent. In the price competition, the online company chooses its optimal offline expansion strategy by predicting its profit in the post-expansion market. Findings This paper found that the equilibrium crucially depends on the synergy effect due to online–offline integration, and such synergy also influences both consumer and social welfare. This study shows the various conditions on the synergy that affect an online company moves toward offline markets. Accordingly, this finding can assist online companies with or without retailing business to choose an optimal strategy when expanding offline markets. Moreover, by doing some necessary welfare analysis, this study shows that the online company’s offline expansion is not always benefiting consumers nor be socially desirable, which may shed some lights on the possible competition policy in the case where online companies practice in offline expansion. Originality/value Different from conventional wisdom in online-offline integration, the theory indicates that the offline expectation of online company may not always benefit consumers nor be socially desirable. Moreover, the findings also shed some lights on the possible competition policy in the case where online companies practice in offline expansion.

The issues of food safety and environmental protection are attracting more and more attention. Everyday, a large number of cold chain products are delivered from suppliers to customers. The cold chain products require refrigeration equipment in delivery and should be delivered to customers as soon as possible. Therefore, the challenge of reducing carbon emission and improving the customer satisfaction should be solved. This study presents the impact of carbon emission, customer satisfaction, construction cost, and operation cost on the location of cold chain logistics distribution center. A multi-objective location model for cold chain logistics distribution center considering carbon emission is established. The carbon emission equivalent cost model considers the dynamic carbon emission during transportation and the static carbon emission of the distribution center. The penalty cost under the time window is introduced into the penalty cost model of customer satisfaction, which represents a multi-objective mixed-integer linear programming problem. A non-dominated sorting genetic algorithm II (NSGA-II) is used to design the program through double-layer composite coding. NSGA-II uses a fast non-dominated sorting approach to reduce the computational complexity of non-dominated sorting. This algorithm uses the elitist control strategy, which does not need to share parameters and is more efficient in the multi-objective optimization process. The numerical results show that the proposed algorithm can generate appropriate Pareto solutions for all objectives.

Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a non-linear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.

Determining the optimum location of facilities is critical in many fields, particularly in healthcare This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic The used model is the most appropriate among the three most common location models utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model) The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt In this case study, a discrete binary gaining–sharing knowledge-based optimization (DBGSK) algorithm is proposed The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life The DBGSK algorithm mainly depends on two junior and senior binary stages These two stages enable DBGSK to explore and exploit the search space efficiently and effectively, and thus it can solve problems in binary space

As traffic congestion has become a common problem plaguing Chinese cities, a strong coverage of taxi stops with reasonable number distribution can not only improve the operating efficiency of taxis, but also make rational use of taxi resources. It can also...

This study proposes an integrated approach to plan tank-truck shipments of hazardous materials in intercity road networks. Considering the conventional damage and the diffusion of volatile toxicants separately, we define the risk indicators of tank-truck hazardous materials transportation. Moreover, we study the network consisting of a local distribution sub-network and a long-distance transportation sub-network. They can be implemented by a capacitated p-hub location model and a bi-level programming model. Furthermore, an exact algorithm and an improved imperialist competitive algorithm are developed to solve the bi-level programming model, respectively. Besides, there is a case study completed in a local region of East China. The numerical results demonstrate the research advantages in method innovation and engineering operation.

Eye state evaluation is crucial for vision-based driver fatigue detection. With the outbreak of COVID-19, many proposed models for eye location and state evaluation based on facial landmarks are unreliable due to mask coverings. In this paper, we proposed a robust facial landmark location model for eye location and state evaluation. First, we develop an existing lightweight face alignment model for eye key point locations that is robust in large poses. Then, to develop the performance of our model in a complex driving environment such as an environment with mask coverings, changing illumination, etc., we design a method to augment the training data set based on the original landmark data set without any extra cost. Finally, some facial landmarks around the eyes are extracted, and the eye aspect ratio (EAR) is introduced to evaluate the eye state based on eye key points. The experiment shows that our model achieves significantly improved landmark location performance on a driving simulation data set due to data augmentation. We tested our model on the BioID data set to measure the eye state evaluation performance, and the results showed that our model obtained satisfactory performance with an accuracy of approximately 97.7%. Further testing on the driving simulation data set shows that our model is robust in different driving scenarios with an average accuracy of approximately 93.9%.

The feeders in distribution networks can be generally classified into two types: homogenous and hybrid feeders. Hybrid feeders consist of non-homogeneous line segments. In this paper, a fault location scheme is proposed for distribution grids with both homogenous and hybrid feeders using the edge computing. The standard transient amplitude ratio arrays of the hybrid feeders are first constructed and stored at the edge nodes. Then, an accurate fault location model is trained by the radial basis function neural network and stored in the substation server. To achieve this goal, the transients are first sampled at the measurement nodes and transmitted to the edge nodes nearby. The amplitudes are then extracted to determine the faulty branch. For a hybrid feeder, the actual amplitude ratio array is compared with the standard one to identify the faulty line segment. Finally, the amplitude ratio of the two transient components with different frequencies at one terminal of the faulty section is computed and transmitted to the server. Based on the model of a distribution network built by PSCAD/EMTDC, extensive fault simulations are studied. The results demonstrate the effectiveness of the scheme.

Presently used magnetic target location methods are greatly affected by geomagnetism and involve complex solution processes. To address these problems, herein, a linear location method based on the two-point magnetic gradient full tensor is proposed. Moreover, the principle of single-point magnetic gradient full tensor positioning is analyzed in detail. Based on this principle, a two-point magnetic gradient full tensor location model is constructed. A linear location method is proposed. Measuring the magnetic gradient full tensor at two locations and using a linear method for the calculation of the positioning model, a more precise location of the magnetic target is achieved. The simulation results show that the proposed method significantly reduces the relative error percentages in the three magnet field directions, with the minimum value being 0.0021% in the $X$ -direction. The maximum $X$ -direction error of the proposed method is reduced by 18.66% compared with that of the single-point magnetic gradient full tensor positioning method. In addition, the positioning accuracy of the method is experimentally verified. The $Z$ -direction error value is reduced to 5.48%.

The existing four linear section location methods are all established based on the three-phase fault current direction. When a single-phase grounding fault occurs in a neutral noneffectively grounded system, these methods are no longer applicable. This study proposes a linear optimization method for section location suitable for single-phase ground fault in the neutral noneffectively grounded system. It presents an improved transient zero-sequence current direction identification algorithm based on line voltage to accurately determine a transient zero-sequence current direction under single-phase grounding faults without zero-sequence voltage information. Second, it proposes a suitable linear section location model for single-phase ground faults as per the transient zero-sequence current direction’s distribution characteristics after the single-phase ground fault. Finally, it is verified in different feeders that the proposed method can quickly and accurately locate the fault section after the single-phase ground fault occurs in the neutral-point ineffectively grounded distribution system.

Purpose This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type. Design/methodology/approach In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem. Findings As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms. Practical implications The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability. Originality/value The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.

In this paper, based on an improved swarm optimization algorithm, a new site selection model of a municipal solid waste (MSW) incineration plant is proposed. First, the whale optimization algorithm and particle swarm optimization algorithm are combined according to certain rules to improve the performance of the hybrid algorithm. Through a verification of the single and multi-peak functions, the results show that the algorithm achieves a good performance. The location model of the MSW incineration plant is based on many factors, including the economy, environmental protection, population scale, and operation cost. Finally, based on a sample analysis, a new location model of an MSW incineration plant is used to select the location of an Anshan MSW incineration plant, and a reasonable location is obtained.

O perfil do cidadão tem se modificado em relação aos impactos causados pelo alto consumo e o descarte incorreto gerado pelos seus resíduos; com isso os cidadãos tem continuamente buscado soluções que minimizem os impactos de suas ações. Assim, o presente estudo teve por objetivo identificar a melhor localização para a instalação do elo Centro de Triagem da logística reversa de aparas de papel gerados em escolas do bairro Vila Mariana, levando em consideração as variáveis de decisão. Por meio da metodologia escolhida definiram-se os seguintes objetivos específicos: mapear as escolas no bairro da Vila Mariana, na cidade de São Paulo; identificar as variáveis de decisão que influenciam e devem ser levadas em consideração para determinar as estimativas; avaliar a instalação de um Centro de Triagem para atender a região utilizando modelo de localização, centro de gravidade. Considerando o método escolhido, as escolas da região foram mapeadas possibilitando definir a melhor localização para o Centro de Triagem, bem como avaliar a sua instalação pelos critérios de volume de aparas de papel gerado pelas escolas mapeadas, custo do transporte do resíduo das escolas até o Centro de Triagem e a distância entre as escolas e o Centro de Triagem.

Automated Guided Vehicle (AGV) indoor autonomous cargo handling and commodity transportation are inseparable from AGV autonomous navigation, and positioning and navigation in an unknown environment are the keys of AGV technology. In this paper, the extended Kalman filter algorithm is used to match the sensor observations with the existing features in the map to determine the accurate positioning of the AGV. This paper proposes an improved joint compatibility branch and bound (JCBB) method to divide the data and then randomly extract part of the data in the divided data set, thereby reducing the data association space; then, the JCBB algorithm is used to perform data association and finally merge the associated data. This method can solve the problem of the increased computational complexity of JCBB when the amount of data to be matched is large to achieve the effect of increasing the correlation speed and not reducing the accuracy rate, thereby ensuring the real-time positioning of the AGV.

Prior planning of shelters and evacuation routes is the foundation of effective and safe post flood management. In this study, a hierarchical model for emergency shelter location selection in prepa...

Catastrophic interlocking chemical accidents (CICAs) are the most frequent hazardous events that occur in chemical parks, and they result in severe human casualties and property losses. Emergency rescue facilities play an important role in ensuring the safety of chemical parks, as their locations determine the efficiency and effectiveness of the emergency management process. This paper proposes a multi-objective emergency rescue facilities location (ERFsL) model for emergency management of CICAs. Due to the special characteristics of CICAs, the proposed model considers not only the spatial characteristics of the facilities but also the potential risks associated with CICAs. A quantitative area risk assessment (QARA) approach based on domino effect and grid partition is involved to delineate the potential negative impacts of CICAs on different demand points. Moreover, a matrix encoding scheme is coupled with Pareto-based multi-objective evolutionary algorithms (MOEAs) for model solving. A case study is presented to demonstrate the methodology. A set of reasonable and non-dominated solutions indicate that the matrix encoding scheme and the corresponding evolutionary operators can be successfully coupled with the MOEAs to produce feasible child solutions. We also find that the involvement of risk analysis has a significant effect on the location decision. The findings show that our work has the potential to ensure the safety and long-term development of the parks.

The insufficient charging infrastructure greatly obstructs the development of the electric vehicle(EV) industry. How to efficiently deploy charging stations in a city becomes an urgent problem for the local government. Previous refueling location models are not suitable for fulfilling the EVs' charging demand. This paper proposes a location model for charging stations based on the characteristics of travel behaviors of urban residents. The model consists of two parts: one for short distance commuters which utilize slow charging (SC) facilities, and the other for long distance travelers which utilize fast recharging (FR) facilities. It aims to harness limited resource to meet as much as possible charging demand for both parking vehicles and vehicles on long journeys. A typical city of China is used as an example to demonstrate the implementation and results of the model. We employed sensitive analyses to search for specific factors having strong impact on the number and location of charging stations. It is suggested that travel distance and locating capacity will greatly affect the final result.

This paper considers a multiperiod emergency medical services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. Whereas the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope constraints that allow us to control the degradation of coverage under the more severe scenarios. These models give rise to large mixed-integer programs, which can be tackled directly or by using a conservative approximation scheme. For the former, we implement the branch-and-Benders-cut method, which improves significantly the solution time when compared with using both a recently proposed state-of-the art branch-and-bound algorithm and the CPLEX solver. Finally, a practical study is conducted using historical data from the Northern Ireland Ambulance Service and sheds some light on optimal EMS location configuration for this region and on necessary trade-offs that must be made between emergency demand coverage and expected cost. These insights are confirmed through an out-of-sample performance analysis.

In recent years, with the rapid development of urbanization, the urban public emergency management faces increasing challenges, and the number of fire incidents has increased largely. For mitigating injury risk and reducing property loss, fire station locations need to be optimized to provide efficient fire emergency services. However, the locations of fire facilities in China are mainly determined according to administrative divisions, lacking effective data-driven applications. In addition, existing location models are designed for general purpose, and few have taken into account the unique characteristics of fire services with multiple objectives, i.e., maximum coverage, minimum overlapping coverage and balanced workloads. With the recent development of geospatial big data, lots of fire risk related data (e.g. fire incidents data and travel time data) can be obtained, which provides an unprecedented opportunity for urban fire emergency facilities planning. In this paper, we propose to establish the multi-objective maximal covering location model by accurately estimating fire rescuing demands and travel cost from geospatial big data. A case study was carried out in Nanjing, China, and the result shows that our method is effective in optimizing the locations of fire stations.

Bike-sharing systems can be implemented to complete the coverage of public transport networks which could be insufficient to serve an entire urban area. Some methodologies that maximise coverage or accessibility are suggested in the literature. In this paper, we propose a bike-sharing stations location model that includes not only these issues but also equality aspects. The model aims to minimise inequalities in bicycle-public transport mobility among observed population groups trying to maintain specified levels of accessibility and coverage at the same time. We evaluated the performance of the model on a test network and carried out a sensitivity analysis according to the available budget. The results showed that maximising accessibility or coverage alone, without considering equality, may lead to an unequal distribution of accessibility among the population, producing discrimination between different groups. The outcomes of the application revealed the significance of the model in evaluating equality in the network design phase for achieving not only a satisfactory bike-sharing system and public transport multimodal accessibility of each zone but also a high equality measure among the considered population groups. Budget availability also played an essential role since a minimum budget value is needed to achieve higher levels of equality. The proposed approach could serve transport and public authorities as a decision support system in planning future investment as well as promoting multimodal mobility because it links bike-sharing stations with stops/stations of the public transport lines networks.

Underground mine fires remain a concern for mine operators, posing a health and safety risk to mineworkers. In the last decade, the number of mine fires has decreased significantly; however, dealing with an unknown fire in underground mines can be a challenging task, which could lead to a hazardous condition for miners during an evacuation and rescue operation. A timely detection of a mine fire and monitoring its characteristics, namely size and location, are of great importance in reducing the risk of mine fire injuries. A new improved fire location algorithm has been developed and integrated into an Atmospheric Monitoring System (AMS) program by researchers from the National Institute for Occupational Safety and Health (NIOSH). This paper describes the new fire location model and presents the results of verification fire tests conducted at the Safety Research Coal Mine (SRCM) facility of the Pittsburgh Mining Research Division (PMRD) using the collected AMS data. NIOSH is endeavoring to develop workplace solutions to improve detection of and reduce the risk of hazardous conditions in mines. The results demonstrate successful application of the improved fire location model and provide a useful tool for solving the problem of unknown fire location and reducing the risk of hazardous conditions.

In this note, we analyze location problems where the distance (time) to get to the destination by air is affected by winds. We propose two models: the asymmetric Weber location problem and the roun...

The continuously low efficiencies of flood relief service in responding to flooding disasters in Taiwan highlight a need for research. This service efficiency is negatively influenced by flood relief facility locations and their operational management structure. The Fazih River floodplain has been previously identified as a high-flood-risk area using an analytic hierarchy process. The purpose of this study is to use the location set covering problem and the maximal covering location problem to determine the number of relief facilities required and the maximum covering area of each facility for the case study area of the Fazih River floodplain. The findings showed that covering distances range from 3.0 to 6.6 km and the number of facilities required for improved service efficiency is between 1 and 4.

A three-sample distribution-free test for detecting non-decreasing ordered alternatives is proposed to alleviate some of the problems in the existing tests, including the risk of frequent detection...

Plug-in electric vehicles are widely acknowledged as an effective tool for numerous environmental and economic concerns. In this article, a novel model for the planning of fast-charging stations is established based on a data-driven distributionally robust optimization approach, which aims to minimize the expected planning cost for both transportation network and distribution network. $\phi $ -divergence, a statistical measure, is utilized to establish the serviceability constraints. On the other hand, a modified capacitated flow refueling location model is employed to develop the location constraints. In addition, ac power flow constraints are developed to model the operation of DN with the penetrations of PEVs. Finally, a case study is illustrated to validate the proposed planning model.

Smart City is a new model and new form of urban development based on the Internet of Things, the advanced information technology, intelligent technology and multi-network integration. In most cases, wireless sensor networks need to be monitored in complex environments or in areas that humans cannot reach. Most of the nodes will adopt aircraft throwing and other methods, resulting in uncontrollable node positions. Such research cannot be carried out without knowing the specific source of the information. Therefore, the problem of node location in wireless sensor networks has become one of the key issues that must be solved. In this paper, a wireless sensor network topology based on correlation neighbor graph is constructed. The DV-Hop (Distance Vector-Hop) localization algorithm with hop number correction and average hop weighting is proposed. The signal strength value received by the node is converted into the distance between the nodes by the shadowing model; and the ratio of the distance value to the communication radius between the nodes is used to correct the hop value. In order to further improve the positioning performance of the algorithm, the MDV-Hop (Modified Distance Vector-Hop) algorithm is proposed. The MDV-Hop algorithm mainly improves the error caused by the number of hops between anchor nodes, the average hop distance and the network topology in the DV-Hop algorithm. Aiming at the problems of the maximum likelihood coordinate calculation method, the localization problem of nodes is transformed into the minimum problem of solving nonlinear equations, and the improved Bat algorithm is used to replace the maximum likelihood algorithm to obtain the coordinates of unknown nodes. The simulation shows that the unknown node has achieved better positioning results in the IoT positioning model based on wireless sensor network topology and has higher positioning accuracy.

Emission screening and control for motorized traffic is mandatory in most countries worldwide. However, there may be cases that periodic inspections in permanent establishments may be inefficient a...

Emergency medical service (EMS) response is extremely critical for pre-hospital lifesaving when disaster events occur. However, disasters increase the difficulty of rescue and may significantly increase the total travel time between dispatch and arrival, thereby increasing the pressure on emergency facilities. Hence, facility location decisions play a crucial role in improving the efficiency of rescue and service capacity. In order to avoid the failure of EMS facilities during disasters and meet the multiple requirements of demand points, we propose a multi-coverage optimal location model for EMS facilities based on the results of disaster impact simulation and prediction. To verify this model, we explicitly simulated the impacts of fluvial flooding events using the 1-D–2-D coupled flood inundation model FloodMap. The simulation results suggested that even low-magnitude fluvial flood events resulted in a decrease in the EMS response area. The integration of the model results with a geographical-information-system (GIS) analysis indicated that the optimization of the EMS locations reduced the delay in emergency responses caused by disasters and significantly increased the number of rescued people and the coverage of demand points.

We derive explicit expressions for arbitrary moments and quantiles of the posterior distribution of the location parameter η in the normal location model with Laplace prior, and use the results to approximate the posterior distribution of sums of independent copies of η.

This paper develops a programme model with equilibrium constraints for decision-making regarding a multihub port location in West Africa. To consider the high security risks associated with inland ...

This paper proposes a method for multi-target location of UAV based on monocular vision. First, a multi-sensor-based target location model for UAV is introduced, and the ineffectiveness of the method in multi-target detection is analyzed. Next, a vision-based multi-target location method is proposed, and the multi-target location is achieved by determining the convergence of the location results. Finally, the effectiveness of the algorithm was verified by simulation experiments.

Background: Timeliness of emergency medical services (EMS) is critical for patient survival. Identifying optimal locations for ambulance vehicles could increase the chance of timely service delivery. This study incorporates Geographical Information Systems (GIS) with a mathematical optimization technique to improve the 5-minute coverage of EMS demands. Methods: This study was conducted in the county of Mashhad, the northeast of Iran, including 94 ambulance vehicles distributed across 74 EMS stations. Locations of demands were extracted using analysis of one-year EMS call data. Network analysis was employed to estimate the travel times. A maximal covering location problem (MCLP) model with a capacity threshold for vehicles was implemented using the CPLEX optimizer. To make the proposed model more practical in the context of EMS, we added a constraint to the model formulation to maintain an acceptable service level for all EMS calls. Two scenarios were implemented: (1) a relocation model of existing vehicles among existing stations and (2) an optimal allocation model of EMS vehicles and stations using a list of candidate locations.Results: Using the relocation model, the proportion of calls for service within the 5-minute coverage standard increased from 69% to 75%, ensuring all urban and rural service demands to be reached within 16 and 48 minutes, respectively. Our allocation model revealed that the coverage proportion could rise to 84% of the total call for service by adding ten vehicles and eight new stations.Conclusions: Incorporating GIS techniques with optimization modeling has the potential to improve population health outcomes in real-world decision making regarding the accessibility and equity of health resource allocation including EMS services.

With the development of economy and the deepening of economic system reform, China's logistics industry has entered an era of rapid development. Logistics not only plays a vital role in the economic development of a country, but also is the key to improve the competitiveness of an enterprise, known as the "third profit source" of an enterprise. As an important node in logistics system, distribution center is an important part of logistics system. Based on firefly algorithm, this paper selects 10 function optimization problems and solves them in different dimensions, all of which get good results. The experimental results show that the proposed method has higher accuracy and faster convergence speed in solving function optimization problems

Scientific planning, and the layout of the national level reserve base of emergency materials, will help improve the effectiveness of a country’s overall emergency disaster reduction system. Based on the P-center location theory, this paper analyzes the factors affecting site selection at the national and state levels, determines the reasonable number of emergency material reserve bases at national and state levels, and then develops a national macro-level emergency supply reserve location planning model. In this study, the city of 28 states in a country were selected as alternative reserve cities and emergency demand city matrix, to conduct the research. The model is solved using a variable neighborhood search algorithm (VNS). The calculation results obtain the reasonable number of emergency material reserve bases set at the national level in this studied country, and the optimal solution of the base layout can be obtained if the number of emergency reserve bases set at the national level remains unchanged. The experimental results shows that the selected algorithm is reasonable.