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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.

For most companies, successful business operation is based on the location at the right place, in the right region, on the right plot. New investments in land, buildings and material handling equipment are carried out for the longer term. As the global business environment in which a company needs to decide on location is becoming more uncertain every day, it is of huge importance to make a thoughtful and ‘future-proof’ decision on the location for a new distribution site. Allocation can also be defined as a process that results in finding a position for a warehouse, company, production, people, things, and activities in a specific area. A newly built distribution site is impossible to be moved in a short period of time, so the decision on where to build a distribution site, warehouse, or manufacturing plant is of vital and strategic importance.

Distribution lines are integral parts of the modern power system, which can affect the security and stability of power supply directly. An effective power system protection scheme should be able to detect all occurring faults as soon as possible. There are two tasks in fault diagnosis. One is the fault classification, where high accuracy rates have already achieved. Thus, this paper focuses on the other task, i.e. fault location. Enlightened by Fourier transform, this paper proposes an online data-driven method, which transforms signals from time domain to image domain through signal-to-image (SIG) algorithm and then process the transformed images with framework based on convolutional neural network (CNN). On the one hand, we can extract more crucial characteristic and information from image domain. On the other hand, the CNN-based structure is much smaller than others. It needs less memory space and would be easier to be transplanted to hardware platform. Moreover, the proposed algorithm does not require synchronous devices. The numerical comparison shows that the proposed SIG-CNN fault location model achieves robust and accurate results compared with other data-driven algorithms.

The scientific location of earthquake emergency supply warehouses is conducive to the effective distribution of emergency relief resources and improved rescue efficiency in earthquake hazard. Comprehensively considering the regional population as well as coverage quality at the demand points, this paper aims to divide the coverage thresholds of earthquake emergency rescue and logistic supplies according to their time-series features, and to build a location model for supply warehouses according to the variety and amount of stored supplies considering their time-series features, in hope of optimizing the set covering issue of earthquake relief supply warehouses. The solution is approached with two methods: the target deviation rate minimization model and NSGA-II algorithm. The results obtained by solving the target deviation rate minimization model can balance every target. The branch and bound algorithm can find the global optimal solution at a certain calculation scale with high calculation efficiency, but its efficiency decreases significantly when the operation scale increases. The NSGA-II algorithm is more suitable for large-scale solution calculations with high calculation efficiency, and it can output a set of non-inferior solutions for decision makers to select from according to different preference. Taking Aba Prefecture in Sichuan Province as illustration, the feasibility of the model is validated; meanwhile, the effectiveness and benefits of the two approaches in solving the problem of multi-objective set covering of the warehouses are compared and analyzed.

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.

The rapid growth of the intercity travel demand has resulted in enormous pressure on the passenger transportation network in a megaregion area. Optimally locating hubs and allocating demands to hubs influence the effectiveness of a passenger transportation network. This study develops a hierarchical passenger hub location model considering the service availability of hierarchical hubs. A mixed integer linear programming formulation was developed to minimize the total cost of hub operation and transportation for multiple travel demands and determine the proportion of passengers that access hubs at each level. This model was implemented for the Wuhan metropolitan area in four different scenarios to illustrate the applicability of the model. Then, a sensitivity analysis was performed to assess the impact of changing key parameters on the model results. The results are compared to those of traditional models, and the findings demonstrate the importance of considering hub choice behavior in demand allocation.

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.