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The location and selection of logistics nodes that facilitate the China Railway Express and rail-sea intermodal transportation has received increasing attention in China under the Belt and Road Initiative. The objective is to solve problems caused by the increasing number of origin cities opening international trains, such as disorderly competition, insufficient cargoes and low overall coordination. This study screens 22 cities as candidate Chinese international container multimodal hubs (CICMHs) in consideration of the actual situation of China's trade transportation. Thirteen indicators are screened using the information contribution rate-information substitutability method. Then, a comprehensive evaluation model is proposed to evaluate the candidate CICMHs and rank them. The model is based on the extended grey relational analysis-technique for order preference similar to ideal solution in combination with prospect theory. Chongqing, Guangzhou, Shanghai, Wuhan, Chengdu, Xi'an, Nanjing, Tianjin, Zhengzhou and Dalian are selected as the CICMHs. Moreover, a sensitivity analysis of the index weight fluctuations and decision-makers' preference and a comparative analysis of different decision-making methods are performed. The robustness and stability of the proposed model are demonstrated. This study can support the location and selection of CICMHs and expand the methods and applications in the decision-making field. [ABSTRACT FROM AUTHOR]

Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose the most appropriate agritourism location in Vietnam for long-term investment. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on eco-nomic, social, and environmental factors. In the first stage, the fuzzy analytic hierarchy process (FAHP) is used to estimate the relative criteria rating through the evaluation process. In the second stage, the fuzzy technique for order preference using similarities to ideal solution (FTOPSIS) is applied to rank the potential alternative locations. Finally, the best alternative to tourist site investment is Can Tho (A8), which maximizes resources and enhances the local benefits. Future research can also be used to support similar site-selection processes in other regions or could be applied to other types of tourism. [ABSTRACT FROM AUTHOR]

Consumption of frozen food has increased significantly in many countries experiencing total closure due to epidemics. In order to meet this increase in demand, companies apply different strategies in both production and logistics processes. In particular, warehouse locations need to be closer to consumers so as not to disrupt the increasing demand and the cold chain. Considering the sustainable position in the frozen food industry, it is essential to focus on both commercial and economic impacts. However, especially the environmental and sustainable criteria have always been neglected in terms of planning the country and the city. Therefor e, this study has three main research questions; (1) identify key criteria for sustainability-focused frozen food facility location, (2) understand the relationship between these criteria, and (3) develop a hierarchical sustainable location roadmap for pra ctitioners. Initially, a literature review was conducted to find the answers to these questions and reveal suitable criteria. Secondly, PROMETHEE was used to determine the relative importance of the data-driven performance of the main and sub-criteria. Therefore, this study gives the reader a comprehensive insight into how sustainable data-driven performance criteria are applied in frozen food storage facility locations. Finally, the proposed model used real -life data to select a developing country's sustainable frozen food storage location. [ABSTRACT FROM AUTHOR]

The location of logistics nodes that facilitate the China Railway Express (CRexpress) and rail-sea intermodal transportation has received increasing attention in China under the Belt and Road initiative. This paper selects 38 cities as candidate Chinese international container intermodal hubs (CICIHs) through qualitative screening by considering the current operations of the CRexpress/rail-sea intermodal transportation and government planning strategies. Subsequently, five connectivity indexes are proposed to reflect the node performance considering the multiple modes and stages of transportation in a logistics chain. In view of the shortcomings of the grey relational analysis-technique for order preference by similarity to ideal solution (GRA-TOPSIS) method, a hybrid multi-criteria decision making (MCDM) model is proposed to comprehensively evaluate the hub location. The model is based on the grey area relational analysis-technique for order preference by similarity to ideal solution (GARA-TOPSIS) in combination with analytic hierarchy process, entropy method and game theory. Tianjin, Harbin, Zhengzhou, Wuhan, Chongqing, Xi’an, Urumqi, Guangzhou, Shanghai and Nanning are selected as the final CICIHs, considering the geographical location, economic development and national policy pertaining to the Belt and Road initiative. A sensitivity analysis of the index weight and preference coefficient and a comparative analysis of different MCDM methods are performed. The robustness and stability of the proposed model are verified. This study can support the location selection of CICIHs and expand the methods and applications in the decision-making field.

Logistics Centers (LCs) are among the most important facilities for rapid, cost effective, environmental friendly and secure logistics services to the urban logistics problems. Finding proper locations to LCs has an important effect on their success. In this study, LCs’ location selection problem for Istanbul Metropolitan area is investigated. Istanbul is a leading city of Turkey in many categories including various industries, import/export amounts, transportation infrastructure and population density. However, industrialization and urbanization have introduced numerous problems to Istanbul. New projects are continuously being announced for the problems of metropolis, however, they may also bring new problems if they are not punctiliously planned and applied. Hence, in this study, it is aimed to propose a systematic Multi-Criteria Decision-Making (MCDM) approach for the LCs’ location selection problem of Istanbul. In this approach, an integrated DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) and Intuitionistic Fuzzy (IF) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is utilized. DEMATEL is used to analyse the decision criteria and IF-TOPSIS is used to evaluate candidate locations for LCs. Considering the vagueness inherent to this decision-making problem, IF sets are used for the evaluation process. Results are compared with the results of IF-VIKOR (VIše Kriterijumska Optimizacija kompromisno Rešenje – multi-criteria optimization and compromise solution) technique. First publihed online 19 March 2020

Selecting the new branch location of a company is a critical decision for the profitability and sustainability of the company. Site selection problems require the evaluation of alternatives under multiple and conflicting criteria. This study aims to determine the most suitable location for a new branch of a supplier company in the maritime industry and compare the results obtained from different Multi-Criteria Decision-Making (MCDM) methods using AHP, ARAS, and fuzzy TOPSIS. In the study, eight alternatives were evaluated under the criteria of cost, transportation, natural factors, qualified workforce, supplier connections, presence of competitors and port potential. The findings revealed that the most important criterion is "cost" and the most suitable location for the new branch of the company is Izmit. Having compared the results obtained from the MCDM methods, it was seen that the best alternative was the same with all three methods. Still, different results were obtained in the rankings of the remaining alternatives. This once again showed that MCDM methods should be used for decision support purposes. [ABSTRACT FROM AUTHOR]

In our daily life, we could be confronted with numerous multiple attribute group decision making (MAGDM) problems. For such problems we designed a model which employs probabilistic linguistic MABAC (multi-attributive border approximation area comparison) based on the cumulative prospect theory (CPT-PL-MABAC) method to solve the MAGDM. The CPT-PL-MABAC method can take experts' psychological behaviour and preferences into consideration. Furthermore, we utilize the combined weight consisting of subjective weight and objective weight. The objective weight is acquired by the entropy method. Additionally, the concrete calculating steps of CPT-PL-MABAC method are proposed to solve the MAGDM for selecting the optimal location of express distribution centre. Also, a numerical example for location selection of express distribution centre is given as the justification of the usefulness of the designed method. Finally, we compare the designed model with the other three existing models, and summarize the advantages and shortcomings. [ABSTRACT FROM AUTHOR]

Finding an optimal location for a future facility amidst existing sites is a challenging task—and potentially has numerous applications in resource planning. Formally, given a set of candidate sites S and existing sites E, the Optimal Site Selection (OSS) problem aims to find the optimal location s ∈ S that serves maximum customers. In this paper, we study the OSS problem for predicting school location from a set of available sites in the Urban areas. We leverage deep neural networks to solve this problem in a resource and time-efficient way—using a dataset of 47,926 schools across 36 districts of the Punjab province. The dataset includes numerous features such as no. of teachers, no. of classrooms, and no. of sections associated with each school site. We also incorporate a novel feature called site influence that gives the expected number of potential students for a particular school. Our proposed model performs exceedingly well with the addition of this feature. We approach this problem by defining class labels based on a given school's capacity—and further train a deep neural network using the aforementioned features to achieve accuracy values up to 82%. Overall, this study facilitates optimal school site selection in the Urban areas, thereby, adding to communal prosperity and growth. [ABSTRACT FROM AUTHOR]

Medical tourism had notable growth in the past few years, and it is understood that one of the most prevalent types of medical tourism is dental tourism. This study aims to select the most suitable city for investment in dental tourism. Five dimensions and nineteen criteria associated with dental tourism have been specified, and the DEMATEL-based ANP is used to determine the interrelation of criteria and compute their weights. Then, the VIKOR is used to evaluate the suitability of different cities for investment in dental tourism and determine the potential areas of the selected cities that need enhancement. A case study is conducted to present the applicability of the suggested model. The results indicate that the dental facilities and service is the most critical dimension, and reputation of hospital/facility and doctors, dental clinics with certification/accreditation scheme are the most important criteria for analysing the suitability of potential locations. [ABSTRACT FROM AUTHOR]

The park effect occurs for various reasons in field applications of wave energy conversion. Not all possible causal factors are equally responsible for causing the park effect. The varying significances of the causes were approximated in an objective manner using the analytical hierarchical process multi-criteria decision-making technique and were assessed with an index function where the reasons were used as indicators and the function was the aggregated weighted contribution of each individual reason. A real-time monitoring system was also developed to monitor peak effects in a wave-to-power system in a continuous manner, such that loss due to the park effect could be prevented in real time, ensuring minimum loss of production capacity. Such a monitoring system would use the same aggregated weighted function to monitor the effects, and a network of sensors fixed at strategic points would be used to retrieve the magnitudes of the causal factors. An automatic framework to estimate the park effect was developed using a polynomial neural network architecture, and it was tested for three different locations. An experimental validation of the index values for these three locations was performed. The results indicated that offshore locations of wave-to-power systems were the most vulnerable to the park effect. It was also found that, both offshore and near shore, the distance between converters has the highest significance, whereas for onshore converters, the position of the WEC in relation to the incoming wave was found to be the most important indicator among the six investigated in the present study. Significant uncertainties appeared due to the lack of field-tested results for use in calculating the park effect on devices. However, the use of a physical model reduced these uncertainties, and a reliable framework was developed. Further real-time testing of the indicators will help develop practical implementations of the index for the formulation of policy. [ABSTRACT FROM AUTHOR]

Today, the required energy mostly comes from fossil fuels. Due to the limitation of fossil fuel reserves in the world and emissions of pollutants, today's industries have been challenged to replace renewable energy source.Among these renewable energies, solar and wind are important. In this research, firstly, the factors affecting the location of the solar and distributed generation have been investigated and mapping of criteria in the GIS has been prepared.Then, considering the importance of integrating the information, the ANP technique is chosen for weighting the layers and implemented with Super Decision software.Finally, the model of supply chain of the distribution network is proposed with the aim of maximizing the supplier's profit and minimizing the emissions.Zanjan province is considered as the case study for which the model is solved. According to the results, areas of Khodabandeh, Ijrud, and Mahnashan are suitable for the construction of wind power plants and areas of Khodabande and Ijrud are suitable for the construction of solar power plant. Finally, Khodabandeh, Zanjan, and Mahnashan are suitable for construction of the small gas scale plant.

The location selection for nuclear power plants (NPP) is a strategic decision, which has significant impact operation of the plant and sustainable development of the region. Further, the ranking of the alternative locations and selection of the most suitable and efficient locations for NPPs is an important multi-criteria decision-making problem. In this paper, the non-sequential Monte Carlo probabilistic method and the Latin hypercube sampling probabilistic method are used to evaluate and select the optimal locations for NPP. These locations are identified by the power plant's onsite loads and the average of the lowest number of relay protection after the NPP's trip, based on electricity considerations. The results obtained from the proposed method indicate that in selecting the optimal location for an NPP after a power plant trip with the purpose of internal onsite loads of the power plant and the average of the lowest number of relay protection power system, on the IEEE RTS 24-bus system network given. This paper provides an effective and systematic study of the decision-making process for evaluating and selecting optimal locations for an NPP.

ABSTRACTElectric vehicles arouse interest since they not only contribute economies of countries in the context of dependency to oil but also support to more livable and sustainable urban areas. The location selection of electric vehicle charging stations is one of the most vital topics in order to enhance the use of electric vehicles. In this sense, the aim of this paper is to propose an approach that integrates Geographic Information System (GIS) techniques and Multi-Criteria Decision Making (MCDM) methods for finding suitable locations of the electric vehicle charging stations. In this regard, the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) methods are used to calculate the weights of criteria. While the two different weights for each criterion are obtained by means of AHP in terms of environmental impact and accessibility, another weight for each criterion is obtained as a means of applying the FAHP. The intersection of three different suitability indexes is determined so as to achieve a holistic, credible result. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the alternative locations. The results show that the proposed approach offers a notable solution to be selected suitable charging station locations. Moreover, policymakers and administrators could benefit from these results in order to make efficient decisions for forward planning and strategies.

In this study, by using Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) approach which begins to become popular in recent times for solution of Multi criteria Decision Making (MCDM) problems, warehouse location selection for an electricity distribution company is performed. KEMIRA-M logically distinguishes criteria into two groups and it computes criteria importance weights by including interactions between both groups. KEMIRA-M considers both decision makers’ preferences related to the priorities of criteria and quantitative or qualitative values of these criteria in decision making process. Decision makers can change importance weights of criteria based on median priority component representing expected rankings of criteria importance weights and they can see the effect of this variability on rankings of alternatives. In warehouse location selection problem investigated in this study, it is aimed to choose the favorable location considering different criteria and these criteria were grouped firm related and environmental criteria to evaluate 20 alternative warehouse locations. In this context, as firm related criteria; Operation Center-Meeting Point (OC-MP) transportation cost per month, main warehouses’ transportation costs per month, number of connected OC-MP, consumption amounts of OC-MP were considered. Population, distance to the closest main road, average distance to main supplier, mobility, investment amounts in 2018, average delivery time and land cost were taken into account as environmental criteria group.

Bu çalışmada, Çok Kriterli Karar Verme (ÇKKV) problemlerinin çözümü için son zamanlarda popüler hale gelen Modifiye Edilmiş Kemeny Medyan Gösterge Sıralaması Uygunluk Yaklaşımı (Modified KEmeny Median Indicator Ranks Accordance, KEMIRA-M) kullanılarak; bir elektrik dağıtım şirketi için depo yeri seçimi gerçekleştirilmiştir. KEMIRA-M, kriterleri mantıksal olarak iki gruba ayırmakta ve her iki grup arasındaki etkileşimleri de kapsayarak kriter önem ağırlıklarını hesaplamaktadır. KEMIRA-M, karar vericilerin hem kriter öncelikleriyle ilgili tercihlerini hem de kriterlerin nicel veya nitel değerlerini dikkate almaktadır. Karar vericiler, kriterlerin önem ağırlıklarını medyan bileşen öncelikleri temelinde değiştirebilirler ve bu değişkenliğin etkilerini alternatiflerin sıralaması üzerinde görebilirler. Bu çalışmada incelenen depo yeri seçim probleminde farklı kriterler göz önünde bulundurularak en uygun yerin seçilmesi amaçlanmıştır. Bu kriterler 20 depo yeri alternatifinin değerlendirilmesi için çevresel ve firma ile ilişkili kriterler olmak üzere iki gruba ayrılmıştır. Bu kapsamda, firma ilişkili kriterler olarak; Operasyon Merkezi-Buluşma Noktası Depolarının (OC-MP) aylık ulaşım maliyetleri, 2018 yatırım tutarları, aylık ana depolar arası ulaşım maliyetleri, bağlı OC-MP sayısı ve OC-MP tüketim tutarları dikkate alınmıştır. Çevresel kriterler olarak; nüfus, en yakın ana yola uzaklık, ana tedarikçiye ortalama uzaklık, hareket esnekliği, ortalama teslimat süresi ve arazi maliyeti göz önünde bulundurulmuştur.

Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (two-dimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

The location of joint distribution center (JDC) is of great importance in urban freight system. This work aims to minimize the total cost, including the fixed cost, the transportation cost, and the penalty cost for the missed deliveries, considering the joint distribution willingness (JDW) of restaurant and the coverage radius of JDC. The integer programming model is formulated to select the optimal location of JDC and opens no more than k-JDCs. To solve the problem, an improved K-means (I-K-means) algorithm combining local search with penalty is proposed. The delivery problems obtained by freight survey from 114 restaurants in Beijing, China are taken as a case study. The formulation in this paper can reduce the cost of deliveries in restaurant industry, decrease the number of freight vehicles on road, and promote the joint distribution mode in urban freight system.

The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions. Given a set of spatial objects and a coverage radius, the MCR problem aims to identify a location from the road network, so that we can reach as many spatial objects as possible within the given coverage radius from the location. This problem is fundamental to supporting many real-world applications. Given a road network and a set of sensors, this problem can be used to find the best location for a sensor maintenance station. This problem can also be applied in medical research, such as in a protein–protein interaction network, where the nodes represent proteins, the edges represent their interactions, and the weight of an edge represents confidence. We can use the MCR problem to find the set of interacting proteins with a confidence budget. We propose an efficient exact solution to solve the problem, where we reduce the MCR problem to an equivalent problem named the most overlapped interval and design an edge-level upper bound estimation method to reduce the search space. Furthermore, we propose two approximate solutions that sacrifice a little accuracy for much better efficiency. Our experimental study on real-road network datasets demonstrates the effectiveness and superiority of the proposed approaches.

The fact that the production and consumption amounts are different from each other gives a dynamic feature to the concept of storage. In addition, an effective storage activity hinges upon the selection of the right warehouse location that takes into account capital and labor investments; minimizes the transportation cost and time. The aim of this study is to investigate the preliminary researches in the selection of the warehouse location selection and to present information on which success factors and methods are preferred. Within this scope, many academic studies have been examined by filtering various databases. According to the results of the study, the most used methods in warehouse location selection are multi-criteria decision making methods such as AHP, ANP and TOPSIS; and the most commonly used success factor is determined as the cost-based factors.

Dünya üzerinde üretim ve tüketim miktarlarının birbirlerinden farklı olması depolama kavramına dinamik bir özellik kazandırmaktadır. Ayrıca etkin bir depolama faaliyeti; sermaye ve işçilik yatırımlarını dikkate alacak, taşıma maliyet ve zamanını minimize edecek doğru depo yerinin seçilmesinden geçmektedir. Bu doğrultuda çalışmanın amacı, depo yeri seçiminde yapılmış olan öncül araştırmaların incelenmesi ve depo yer seçiminde hangi başarı faktörleri ve yöntemlerin tercih edildiğine dair bir bilgi sunmaktır. Bu amaç kapsamında çok sayıda akademik çalışma çeşitli veri tabanları süzülerek incelenmiştir. Çalışmanın sonuçlarına göre depo yer seçiminde en fazla kullanılan yöntemler AHP, ANP ve TOPSIS’in başını çektiği Çok Kriterli Karar Verme yöntemleri, en çok kullanılan başarı faktörü ise Maliyet içerikli faktörler olarak belirlenmiştir.

There is an increase in the number of people who have changed their country for compulsory reasons due to the wars experienced worldwide. This raises the problem of identifying the regions where these people, called refugees, will live in the countries. In this regard, we determine in this paper where the camps established for refugees living in Istanbul should be located. The presence of many quantitative and qualitative factors which should be handled in determining the best location has enabled this problem to have multi-criteria decision-making structure. In addition, the advantage of fuzzy logic is used to convert the evaluations taken from experts into available numbers and to include them in decision-making process. For this purpose, a novel model with the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy WASPAS methods is proposed for the first time in the literature, to select the best location for the refugee camp. In addition, a comparative analysis is applied to determine the validity of the results obtained and the sensitivity analysis is applied to check the robustness of the model. As a result, the most suitable location for a refugee camp in Istanbul is identified reasonably with the proposed model. [ABSTRACT FROM AUTHOR]

The current era of industrial economics necessitates warehouse and logistic distribution centers (DCs) to contribute productively toward an organization's success. Playing such a critical productive role implies that logistics activities must be practiced effectively and efficiently. However, the indistinguishability between effectiveness and efficiency leads to a somewhat shallow interpretation, and consequently, a diluted evaluation. Hence, this paper aims to develop a productivity evaluation model for nine DCs belonging to an international automotive vehicles and spare parts company. The developed model was set up based on two multi-criteria decision making (MCDM) approaches: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness, while the DEA was utilized in order to measure the efficiency of the investigated DCs. The resulting hybrid model collectively creates what can conceptually and practically be considered a productivity evaluation model. The results also provide six different strategies through which distribution center locations can be evaluated in order to implement potential future initiatives. [ABSTRACT FROM AUTHOR]

Energy is an important resource for the future of human beings. It is an indicator of development and sophistication. With the increase in the population and the development of technology, the need for energy has also increased. Because of limited fossil resources in energy production and in order to keep environmental pollution at minimum, renewable and environmentally compatible resources should be preferred and developed. Solar energy is one of the energy sources with high potential among renewable energy sources. Location selection is very important issue for solar energy as negative effect of a wrong location selection will be high because of high investment costs and agricultural land invasion. The aim of this study is to determine the most suitable location for the solar power plant within the boundaries of Karaman Başyayla, Konya Taşkent ve Niğde Bor. 8 criteria that affect the right location selection have been determined and criterion weights were found with Entropy. By using TOPSIS, MOOSRA and ARAS methods, it has been tried to determine the most suitable location for the solar power plant planned to be built in the regions above. Konya Taşkent alternative ranked first in all methods, while Karaman Başyayla alternative ranked second and Niğde Bor alternative ranked third. [ABSTRACT FROM AUTHOR]

Optically Stimulated Luminescence (OSL) dating directly gives the last time of the sediment exposure to light and zeroing of the luminescence signal inside the sediment. OSL has become a major tool for dating sediments over the past 40 years. This method has found a special place in Quaternary sciences in the world today and has vast applications in Iran. Its use to determine the time of occurrence of long" lasting earthquakes, fault slip rate, earthquake return period, past climatic periods including drought and wet periods, as well as determining the time of construction of ancient facilities and the growth of Iranian civilization has expanded over time. Knowing the basics and having a strategy for site selection, choosing a location, and suitable sediment sampling method for luminescence dating have a significant effect on the age accuracy. Sampling from an inappropriate site, sampling of unsuitable materials or suitable but insufficient materials can thwart the laboratory's attempt to determine age. Every measurement begins with sampling, and sampling always contributes to he uncertainty of he measurement. Age uncertainty is the most important parameter that describes the quality of measurements. Sediment sample collection methods for luminescence dating can be divided into 3 categories: Sannpling with tube, as a block or in a light bag. The most common method of sanple collection for luminescence dating is to hammer a tube in the vertical surface of a sedimentary layer. If the layer is thin and the tube is too large for the layer and cannot sanple the thin target or either the grain size of he target unit is large to prevent hammering, or it is cement scale and sampling using a tube is inpossible, there are two options: to manually collect sediment or to extract a bock. The higher the accuracy of dating, the more accurate the interpretations of age. The two main factors to obtain the exact age of the accident are accurate sampling from the right place. Sampling strategies vary depending on the puipose, environment and location of he sampling. But when sampling, it is necessary to consider the relevant principles. These include the adequate knowledge of study hypotheses, source and history of sediments, presence of quartz and potassium feldspar in sediments, the role of light, the role of moisture, ensure of no biological disturbance in soil and sediment and the presence of radioactive equilibrium around the sanple. It is especially important to consider the adequacy of resetting of the luminescence signal, the ability to characterize the radioactive environment surrounding the sanple (dose rate), and the lack of evidence for post-depositional mixing (bioturbation in soils and sediment). [ABSTRACT FROM AUTHOR]

In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with (1 - 1 e) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. We further present a pair of extensions to the problem, namely k-Additional and k-Eliminative Influential Facility Placement problems. We also present corresponding approximate solutions towards both extensions and theoretically show that results of both algorithms are guaranteed. Experiments on real datasets verify the efficiency and effectiveness for all these algorithms comparing with baselines. [ABSTRACT FROM AUTHOR]

For many commercial establishments, the choice of location plays a crucial role in successful development and payback. Rapidly growing urban development brings a lot of uncertainty for deciding on the appropriate location of a new establishment, as one should consider a number of complex non-linear factors. In general, existing approaches rely on the manually obtained data from surveys and reports or indirect location-based data, which do not represent the real spending patterns of customers. By using data from debit card transactions, we bring a new perspective to this problem, since this information directly reflects the facts and amounts of purchases for this area. In this study, we test different graph neural network (GNN) architectures to find the best model for customer flow prediction. First, we gather data and features, based on which we will make a prediction. After that we build a general architecture for a model, that would allow us to test it with different types of GNN, different number of layers and neighbour order. After running tests with various parameters best performance was achieved with GCN-layer. The evaluation of the trained model shows that the R2 coefficient is 0.835. We also provide additional analysis of prediction quality for each financial category separately. [ABSTRACT FROM AUTHOR]

Significant increments of energy demand and the need to decarbonize the existing energy systems motivate policymakers to utilize renewable energy resources. Accordingly, this paper suggests a hybrid electricity generation system that operates with wind and municipal solid waste biomass to generate monotonous currents and protect the environment by using a portion of the urban wastes. To select a hybrid power plant location, Z-number data envelopment analysis is employed considering economic, social, environmental, and strategic factors, in addition to reliability of fuzzy data. Furthermore, a routing problem is solved by the particle swarm optimization algorithm to calculate the optimal cost of urban waste gathering. As a case study, the model is applied to thirty-one cities in Iran; Shahrbabak, Meymeh, and Birjand are three cities selected as optimal hybrid power plant locations. Finally, the sensitivity analysis results indicate the importance of globally adopting land cost and distance from power distribution network factors. [ABSTRACT FROM AUTHOR]

The location selection is a strategic decision that significantly influences revenue, level of competition, and success of companies and countries. This study aims to propose a hybrid approach for the location selection, to evaluate the potential location for the automotive manufacturing plant of Turkey, and to reveal a comprehensive analysis of weighting and multiple criteria decision-making (MCDM) methods. The proposed approach integrates different objective and subjective weighting, MCDM, and Copeland methods. Turkey has recently introduced its first automobile prototypes and has announced that the manufacturing plant will be located in Bursa. This decision is thoroughly examined via four objective weighting methods—entropy, criteria importance through inter-criteria correlation, standard deviation, and mean weight and a subjective method—analytic hierarchy process. Besides, the alternatives are evaluated based on six MCDM methods—technique for order preference by similarity to ideal solution, preference ranking organization method for enrichment evaluations, vise kriterijumska optimizacija i kompromisno resenje, organization, rangement et synthese de donnes relationnelles, elimination and choice translating reality, and the weighted sum method. The outcomes of the weighting methods and MCDM methods, the impact of the attribute weights provided by each method on rankings, the outcome of each method pair, and selection of the best location (Bursa) are thoroughly evaluated considering a real-world case with a potential outcome that makes evaluations more realistic and tangible unlike most of the other studies in the literature. In this regard, Spearman's rank correlation coefficients are considered. Also, sensitivity analysis is conducted to reveal the robustness of the methods and the impact of each weight on outcomes. Some considerable results, including the most robust method and optimal method pairs for the case, are presented. [ABSTRACT FROM AUTHOR]

Location selection of emergency facilities is a vital issue in the disaster management, which can be considered as a multi-criteria decision making problem with uncertainty. In this regard, the evidential reasoning (ER) approach can be used for such a multiple criteria analysis with uncertain information. However, the original ER approach neither took into account the bounded rationality of experts, nor examined the information at different time nodes, which may not conform to the actual situation of location selection. To solve this problem, we propose a time-series-based ER approach under the bounded rationality of experts to make full use of all evidence. Firstly, we propose a prospect-theory-based modification function to adjust the evidence given by experts and reduce the corresponding subjectivity. Considering the information at different time, we then propose a time-series-based ER approach to generate the comprehensive evidence of each alternative over the whole period. Besides, a VIKOR-based ranking method is proposed to improve the way to rank alternatives in the original ER approach. Finally, the proposed method is applied in the location selection of the emergency material reserve and the corresponding comparative analyses are given to show the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, natural disasters happening over the world pose severe challenges to emergency evacuation systems, especially for highly-populated urban community areas. Underground spaces not systematically utilized yet provide a new development direction for the construction of emergency shelters. Emergency response for mitigating negative impacts of disasters calls for well-designed shelter system and effective management of pedestrian evacuation. In this paper, we study the location selection of underground emergency shelters in urban community areas, along with pedestrian evacuation planning once disaster happens. The aim is to effectively use the existing underground space and sidewalk network to satisfy pedestrian evacuation demand as much as possible. We formulate the problem as a network flow model, and develop a minimum-cost-maximum-flow solution approach. A real-world case study based on the East Nanjing Road community area in central Shanghai is conducted. The results show that sidewalks are the bottleneck of the pedestrian evacuation network due to their limited capacity. Reserving vehicle lanes, implementing one-way evacuation, and opening local small-scale underground shelters, are shown to be effective measures for improving the pedestrian evacuation performance. Overall, the proposed methodology and managerial insights based on the case study in Shanghai show a great potential of employing underground space as shelters for pedestrian evacuation system design in urban community areas. [ABSTRACT FROM AUTHOR]

Issue: Climate change, and its impacts on ecological, agricultural and other societal systems, is most often studied by relying on temperature data derived from countrywide weather‐station networks. Yet, these data do not capture microclimates, those arising from soil, vegetation and topography, at spatial scales relevant to the majority of organisms on Earth. We argue that a unified strategy is missing to design regional or countrywide networks to measure microclimates and thus provide ecologically relevant and sound climate data, for instance for modelling biodiversity and ecosystem functions. Evidence: Here, we develop an integrative framework to design effective microclimate networks for potential implementation at the country level. With the dawn of novel low‐cost sensor technologies and modelling techniques it is time for designing standardized microclimate networks. We make an important step forward in that regard by providing hands‐on training to generate an optimal sensor distribution to capture as much microclimate diversity as possible at the regional or country scale. Conclusions: By implementing our framework to design countrywide or regional microclimate networks, strategically positioned to capture a representative picture of microclimates available within the focal country or region, governments could lay the foundation for the development of a next generation of modelling and synthesis of landscapes, to serve a range of societal needs now and into the future as climate change accelerates. [ABSTRACT FROM AUTHOR]

The Baltic region relies heavily on imported fossil fuels and strongly emphasizes renewable energy development. However, certain obstacles, such as site selection, hinder solar energy development in this region. Therefore, major energy stakeholders must identify optimal solar photovoltaic construction locations. The present study endeavored to conduct a comprehensive and meticulous review of pertinent literature published from 2013 to 2023 to identify critical factors that influence site selection and look into various methodologies employed in this domain. Later, a two-stage approach was used for the first time to identify the most suitable locations among 39 potential cities in the Baltic region for constructing solar PV farms. In the first stage, Data envelopment analysis (DEA) models were used to filter out the areas with the highest potential by measuring their efficiency indices using temperature, wind speed, humidity, precipitation, and air pressure as inputs and sunshine hours, elevation, and irradiation as outputs. Subsequently, six selected evaluation criteria were used to prioritize the locations with solar energy potential. In the second stage, CRiteria Importance Through Intercriteria Correlation (CRITIC)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to determine the criteria's objective weights and rank potential sites for the research case study. The results indicate that 25 locations in the Baltic region are suitable for building solar farms, with specific photovoltaic power output being the most significant criterion that impacts solar site selection. Furthermore, by excluding Gross domestic product (GDP) and labor force from the analysis, the results can align with the Global Solar Atlas's reports. [ABSTRACT FROM AUTHOR]

• Dry ports provide significant economic, environmental and social benefits. • Effective dry port network design is critical for effective maritime logistics. • Presents an integrative framework considering reverse logistics in dryport operations. • Developed a Hesitant Fuzzy Step-wise Weight Assessment Ratio Analysis and Complex Proportional Assessment (HF-SWARA-COPRAS) Dry ports are crucial to marine logistics, providing significant economic, environmental, and social benefits. Effective network design is key to their success, and decisions on location and capacity must consider both objective data and expert opinions to avoid costly long-term consequences. This research presents an integrative framework that combines a fuzzy multi-criteria decision-making method with fuzzy stochastic chance-constrained optimization, forms an integrated multi-objective model, and solves it using the epsilon-constraint approach. The demands for imports and exports are presented in a scenario-based manner, and in each scenario, demands are expressed as trapezoidal fuzzy numbers. To guarantee the level of confidence in satisfying demands, a fuzzy chance constraint method has been used and the stochastic objective function has been transformed into a deterministic counterpart using a novel robust optimization technique. The paper is also among the first to consider reverse logistics from inland to seaports through dry ports to highlight their role in marine export. Since the output of the model is highly dependent on the parameter settings, the mechanism of the parameters' impact on the profit maximization objective function is investigated through a full factorial experiment. The results of the mathematical model proposed in this research, which has been tested on an Iranian case study suggest that high-capacity dry ports should be established in the cities of Sirjan and Tehran, while smaller dry ports should be built in the cities of Isfahan, and Ahvaz. Also, the model suggests that the seaports of Chabahar and Anzali should be connected to Iran's national railway network so that the transportation of goods from them to dry ports on the hinterlands in Sirjan and Tehran can be done at a lower cost. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) have been encouraged to penetrate deeper in the vehicle market for the green transportation system. One of the key issues to promote EV industry is to deploy Battery Swapping Stations (BSSs) that can satisfy the electricity demand of EV users. Since large scale data of vehicles such as GPS locations and electricity requests can be collected, the data-driven approach can be a cost-effective and useful method to select the locations of BSSs. In this paper, we propose a data-driven framework to solve the BSS location selection problem based on a large scale of GPS data of taxies in metropolitan area. The proposed solution consists of three main steps: Hidden Markov Model (HMM) based map matching and trajectory extraction, electricity consumption rate model based battery swapping demand estimation and clustering strategy based BSS location determination. Compared to the state-of-art deployment baseline, our proposed scheme is more easily to implement in reality and the mean distance error between the location that battery swapping demand is generated and the nearest BSS is reduced by 52.5% and 62.7%, which will definitely reduce the range anxiety of EV users and help improve the will of using EVs.

In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems.

Location theory is studying the impact of location on any economic activity, trying to understand where operations should be settled and what should be the reason for such a decision. The aim of this paper is to discuss and prioritize the key location determinants for successful selection of an optimal location for a business unit in the ICT industry. It emphasizes the importance of this concept in ICT sector today when the digitalization and big internet revolution occurs. The initial determinants were formed based on the literature review and evaluated through two rounds of the Delphi study among experts from ICT companies in Serbia. The second goal of the study was to reach a consensus about the relative importance of the agreed determinants. According to the obtained results using the AHP decisionmaking model, the priority in selecting a location for a business unit in the ICT industry is human resource availability, second is the political and economic environment, and the third is the competition. The paper finds that political and economic strategies start to play an important role in ICT market, as governments are getting more interested in this industry. The study confirmed that due to a shortage of labour supply and fierce competition in the ICT sector, the concept of location selection is an important success factor for sustainable growth and development of ICT organizations

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Rapidly changing lifestyles, rising expectations, and increased private vehicle ownership have caused increases in the number of urban trips. All these changes that have appeared in time have made the parking requirement more important every day. Parking surveys have been required to determine what actions to be taken leadingly during the location selection and building processes of the parking areas to be built. The failure on planning parking areas correctly reveals environmental and economic problems as well as social problems. In this study, parking surveys were carried out primarily in order to determine location, types and capacities of existing car parks. The capacity and locations of the new parking areas have been determined considering five-year traffic increases. The points where parking needs were most needed were determined counting the vehicles parked at 44 separate on-road parking areas in the city center of Erzincan, where parking fees are charged by the municipality. In determined parking areas, statistical analyses were performed with spatial analysis outputs, artificial neural networks and time series analysis methods counting the vehicles parked regularly during daylight hours for one year. Solutions were offered to reduce parking load in traffic identifying new correctly located car parking areas in Erzincan city center. In the light of the analysis results, the location and capacities of the new parking areas suggested to be built identifying 6 different points in the city center were determined. In conclusion, an additional 1420-vehicle parking capacity in suggested new parking spaces was proposed as a solution-oriented approach for the parking problem in the city center. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose the most appropriate agritourism location in Vietnam for long-term investment. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on eco-nomic, social, and environmental factors. In the first stage, the fuzzy analytic hierarchy process (FAHP) is used to estimate the relative criteria rating through the evaluation process. In the second stage, the fuzzy technique for order preference using similarities to ideal solution (FTOPSIS) is applied to rank the potential alternative locations. Finally, the best alternative to tourist site investment is Can Tho (A8), which maximizes resources and enhances the local benefits. Future research can also be used to support similar site-selection processes in other regions or could be applied to other types of tourism.

• Included demolition operation in the two-stage disaster waste management system. • Developed MIP models which capture the key features of disaster waste clean-up. • Proposed three approaches to minimise total clean-up cost and time. • Provided thorough analysis of a case study for 2009 Black Saturday bushfires. • Optimised results can help decision-maker developing the waste clean-up plan. Disaster waste clean-up after large disasters is one of the core activities at the recovery stage of disaster management, which aims to restoring the normal functioning of the disaster affected area. In this paper we considered a waste clean-up system consists of (i) demolition operation, (ii) collection of waste from customer nodes to temporary disaster waste management sites (TDWMSs), (iii) processing at TDWMSs, and (iv) transportation of the waste to final disposal sites in the recovery of disasters. A multi-objective mixed integer programming model is developed to minimise the total clean-up cost and time. Three different approaches are developed to solve the problem, which are tested with artificial instances and a real case study. Results of artificial instances indicate that the models developed can be used to obtain close to optimal solutions within an acceptable computing time. Results of the case study can facilitate the decision-makers to develop the waste clean-up with minimised total cost and clean-up time by selecting the right location of TDWMSs and setting up the proper waste clean-up schedule. [ABSTRACT FROM AUTHOR]