Location Selection Model Research Articles

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

Research on the Location Selection Problem of Electric Bicycle Battery Exchange Cabinets Based on an Improved Immune Algorithm

The rise of new energy technologies has accelerated progress towards sustainable development, and many companies are beginning to invest in renewable resource-related facilities. Electric bicycles have always been an important mode of green transportation; however, they also have problems such as slow charging, difficult charging, and that burning and short circuiting may occur during charging. Electric bicycle battery exchange cabinets effectively solve these problems by exchanging low batteries with full batteries instead of charging. However, current battery exchange cabinets face the problems of insufficient construction and unreasonable site selection. Therefore, this paper proposes a location selection model for electric bicycle battery exchange cabinets based on point demand theory, aiming to maximize rider satisfaction and the service capacity of exchange cabinets. The immune algorithm is introduced to solve the location model; however, the traditional immune algorithm has some problems such as poor stability and slow convergence. In this paper, the mutation process of the traditional immune algorithm is improved by introducing multi-point mutation, guided mutation, and local search. Finally, based on the data of electric bicycle riders in Shanghai, we verify that the location model based on point demand theory performs well on two objective functions of rider satisfaction and battery exchange cabinet service capability. We also expand the application of point demand theory to location models. Then, by conducting experiments with different parameter groups, through sensitivity analysis and convergence analysis, we verified that the improved immune algorithm performs better than the traditional immune algorithm in its accuracy, search accuracy, stability, and convergence.

Industrial location and urban macroform prediction model based on cellular automata and multi-criteria decision-making methods (ILAUFM)

The relationships between industrialization and urban regions, as well as technological advancements, innovations, and new work patterns and organizations, significantly impact the structuring of cities. Industrial sectors can become key drivers in shaping the form and macrostructure of a city over time. By examining and evaluating the existing system of structures and relations in the settlements in order to “estimate the land use differentiations” that may arise in the future and “to determine the spatial location of industrial facilities or sites,” a model has been developed. This model aims to predict potential “land use differentiations” and determine the “spatial location selection of industrial facilities or sites” in the future based on the analysis and evaluation of existing structures and relational systems in settlements. The model consists of the integration of the LUCAM, which is based on a cellular automata operating system, along with the “GIS-Based Integrated Industrial Area Location Selection Model,” which evaluates all data under the main headings of natural structure characteristics, socio-economic structure characteristics, and built environment characteristics using weighted overlay analysis. In this article, the general structure of the model developed within the scope of the research and İnegöl sampling are explained. The outputs of this model can be used for alternative separation for logistics centers, residential areas, secondary centers, reinforcement areas, and industrial sites in different settlements. In addition, in the research, a detailed model was designed by integrating decision-makers (central administrations, local administrations, registered users, NGOs) into the urban planning process. The proposed model can be used as a tool in designing transportation and other urban systems (distribution order of equipment areas, green area system, grading of centers, etc.).

Study on Location Selection of Urban Two-Level Joint Express Delivery Stations Considering Fair Cost Allocation among Enterprises

The rapid growth of e-commerce has heightened the importance for express delivery companies to ensure timely deliveries. Consequently, it is essential to explore ways to deliver more packages to customers while simultaneously reducing costs through the adoption of a joint distribution mode. This study presents a two-level delivery location selection model within the joint distribution mode, considering factors such as delivery station capacity and the number of transport vehicles, with the objective of minimizing the total cost associated with selecting delivery station locations. The proposed model is addressed using a combination of the k-means algorithm and the improved discrete firefly algorithm. In addition, to facilitate equitable cost allocation among enterprises, the Shapley value method is introduced in this study. A case study based on real data from an urban distribution network in the city of Hebei Province, China, is adopted to perform the experiments. The results of this study indicate that the improved algorithm not only improves solution accuracy but also reduces solution time when compared to both the particle swarm optimization and artificial bee colony methods. Furthermore, the application of the Shapley value method demonstrates the efficacy of a rational allocation of costs.

Developing a Multi-Criteria Decision-Making model for nuclear power plant location selection using Fuzzy Analytic Hierarchy Process and Fuzzy VIKOR methods focused on socio-economic factors

In response to its position as the fourth most populous country globally, Indonesia is exploring constructing nuclear power plants (NPPs) as a sustainable energy solution. A pivotal step in this initiative is selecting an appropriate NPP site. This study employs two Multi-Criteria Decision-Making (MCDM) methods, the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy VIKOR, to identify the most suitable location for an NPP, focusing on socio-economic factors. The Fuzzy-AHP method is utilized to prioritize ten sub-criteria: transmission network, operating costs, economic impact, security, transportation network, legal considerations, the impact of tourism, land ownership, historical sites, and public acceptance. Following this, the Fuzzy VIKOR method leverages these prioritized criteria to evaluate two potential sites: East Kalimantan and West Kalimantan. The analysis reveals that security, transmission, and transportation networks emerge as the top priorities. The application of the Fuzzy VIKOR algorithm identifies West Kalimantan as the optimal site for NPP construction, evidenced by its lower VIKOR index of 0.3599, indicating a higher overall preference based on the evaluated criteria. The study demonstrates that the integration of Fuzzy-AHP and Fuzzy VIKOR methods prioritizes critical socio-economic factors and quantitatively assesses potential sites, offering a systematic and objective approach to support decision-making in NPP site selection.

An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center

In disaster relief efforts, establishing logistical channels with the affected population is crucial. This necessitates the construction of temporary logistics distribution centers. However, rapidly selecting suitable locations for these temporary logistics centers presents a considerable challenge. An inability to swiftly provide viable relief strategies can have significant negative implications for both the government’s reputation and the overall effectiveness of the relief efforts. In terms of problem modeling, the majority of researchers base their models on economic variables, with few considering the issue of panic among the disaster-affected populace. However, it is critical to address not only the widespread panic but also to focus on proactive rescue operations for those who are excessively panic-stricken or less mobile due to the disaster. In this article, we create an improved genetic algorithm with a density-guiding operator based on the location selection model of emergency logistics centers to address these issues. During variation iteration, the algorithm introduces population density as a guide and makes the algorithm’s parameters adaptive and dynamic, which can successfully choose a superior option. Finally, numerical tests are run on the Jiading District of Shanghai data set to confirm the algorithm’s efficacy. The final improvement of the adaptive algorithm is 13.21%.

A new bi-stage interactive possibilistic programming model for perishable logistics distribution systems under uncertainty

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution.

An integrated interval type-2 fuzzy BWM-MARCOS model for location selection of e-scooter sharing stations: The case of a university campus

Scooter sharing has gained tremendous popularity among university campuses as in other places with many visitors. In practice, shared dockless scooters are more widely used than dock-based scooters. However, dockless scooters can cause inefficient mobility in case of the needs such as recharging and finding the nearest scooter among scattered ones in various places. Since the scooter-sharing literature still has significant knowledge gaps regarding identifying an optimal deployment of scooter-sharing stations, this paper aims to determine the necessary criteria to be considered while deploying scooter-sharing stations and identifying the best locations based on a university campus case using an integrated interval type-2 fuzzy BWM-MARCOS model. This study focuses on a university case that can be exemplary for other universities and similar locations, including many people in their various parts. Universities are bounded areas, and their visitors are mostly the same people for a long time. Therefore, first location selection and corrective actions in time may give more traceable and precise results. This research identifies the main criteria and presents a systematic road map for location selection of shared e-scooter stations, applying ITF2-BWM to determine the importance weights of the location selection criteria and employing IT2F-MARCOS to select the most optimal shared e-scooter stations. Hence, the current paper differs from past studies concerning the studied case and the proposed integrated model. A comparative study with IT2F-TOPSIS is employed to strengthen the accuracy of the results produced by the proposed model. Also, a sensibility analysis of the results was performed to test how changes in criteria weights would affect the final scores of alternative locations.

Research on Location Selection Model of Base Station based on Improved Genetic Algorithm

With the increase in the number of users accessing the 5G network in the future, how to choose the location of 5G base stations to ensure effective network coverage of the service area, so as to provide reliable communication and transmission services, is a key issue to be considered. By establishing an improved genetic algorithm model, the site planning problem is solved. According to the circular coverage area of the base station, the coverage requirement and the minimum cost optimization goal are completed. First, denoising according to business volume, and finally get 35,915 weak coverage points. The sum of the cost of setting up macro base stations and micro base stations is the optimization goal, and the coverage rate of the weak coverage point is more than 90%, is set as the constraint condition. At the same time, the lethal factor is set in the selection process, and the individuals who do not meet the threshold conditions of the original base station are deleted. The improved operator is adopted in the crossover link and mutation link, which speeds up the running speed of the algorithm and ensures the population diversity and the accuracy of the results. Finally, 689 macro base stations and 269 micro base stations are established, with a coverage rate of 93.51% and an optimal cost solution of 7159. By visualizing the results on the coordinate map, it can be clearly seen that most of the weak coverage points have been covered, which shows that the improved genetic algorithm shows fast convergence speed and good optimal value, and verifies the effectiveness of the model.

Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province

The urban expansion process involves multiple stakeholders whose interactions and decision-making behaviors have a complex impact on urban land conversion. In this study, we established an urban expansion simulation model that couples two sub-models: the residential location selection model and the land acquisition bargaining model. Those sub-models include four types of agents: resident agent (RA), real estate developer agent (DA), government agent (GA), and farmer agent (FA). The residential location selection model is composed of three agents, RA, DA, and GA, and is first used to select residential locations, while an artificial neural network (ANN) is used to define the behavior rules of RA and RA selects pixels as candidate locations according to the joint decision probability. Then the land acquisition bargaining model is used, which is composed of GA and FA. If the land acquisition is successful, a pixel is converted into urban land, which is occupied by the corresponding RA; otherwise, the RA selects the next pixel and enters the bargaining process again, and so on, until the RA successfully selects a residential location. Each iteration represents the selection process of an agent. We used this model to simulate urban expansion within the Wuhan Urban Development Zone (WHUDZ) of central China from 2009 to 2019. The overall accuracy and Kappa coefficient of the simulation results were 92.78% and 55.24%, respectively, which were higher than the results using logistic regression cellular automata. Moreover, we obtained the relative contributions of various influencing factors in the ANN on the residential location selection, revealing the influence of the land acquisition process on land expansion. In addition, the coupled model predicted that the WHUDZ’s urban land area will reach 1415.82 km2 in 2029, mainly through extensional expansion, and the southeast and northwest will be expansion hot spots.

Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System

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.

Spatial-based multicriteria decision-making model for coconut sugar agro-industry location selection: A case study at Indragiri Hilir District, Riau Province, Indonesia

Coconut has become a major agricultural commodity because of its significant economic multiplier effects on people in the Indragiri Hilir District, Riau Province, Indonesia. One of the superior coconut products is coconut sugar, which may serve as a healthier substitute for conventional sugar because it has a lower glycemic index. Developing coconut sugar in the agro-industry certainly requires spatial mapping that best fits the criteria of sustainable agro-industry. Therefore, a spatial-based multicriteria decision-making model is required to select the best locations for product development. The integration between geographic information systems (GIS) and the analytical hierarchy process (AHP) was applied to acquire the target. It is relatively easy to handle various criteria with spatial and non-spatial data and balance qualitative and quantitative data. The results showed that the criteria contributing to selecting the location for the coconut sugar agro-industry included economic, social, environmental, technology, and infrastructure. Tembilahan Hulu and Kuala Indragiri became the most potential locations relevant to the analysis results. This work provides significant input and data for local governments in constructing policies related to the development of the coconut sugar agro-industry.

Location Selection Model for Small Wholesale Shops in Samutprakarn Province, Thailand

The objectives of this research were to study the factors affecting the importance of location selection criteria and present a model for the location selection of a small wholesale store in Samutprakarn province. The research applied the Modified Analytic Hierarchy Process methodology: screening the criteria of location selection from the literature review by interviewing five small wholesale business owners and using a set of questionnaires to collect data from 60 small wholesale merchants, followed by an analysis. The research found that the location zone is the most important factor influencing the location selection criteria. For the stores located in the industrial area, the priority is given to the parking space and the width of the road, while those located in the residential area place the first priority on the proximity or being located along the main and secondary roads. However, the importance level of the location selection criteria ranked in the second and third positions was similar for industrial and residential locations, namely proximity to fresh markets and proximity to distribution centers, respectively. The location selection model will help the small wholesale business owners in Samutprakarn province and similar areas consider the potential location for their business establishments. Furthermore, the commercial building owners can also use this model to assess the rental potential of their commercial buildings. Academia can also use the acquired factors and models for further studies.

Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management

In Vietnam, rapid population and economic growth are responsible for the recent increase in solid waste. Energy production from waste is now becoming an effective solution around the world, especially in Vietnam, to solve environmental challenges while contributing to the country’s sustainable energy production. Waste-to-energy production has become a solution to the municipal solid waste problem, which is projected to increase by 10–16%. In this study, the author proposed a fuzzy MCDM model to assess and select a solid-waste-to-energy plant location in Vietnam. In the first stage, the fuzzy analytic hierarchy process (FAHP) technique is utilized to analyze the relative weight of the primary and secondary evaluation elements, and a combined compromise solution (CoCoSo) model is used to rank the candidates in the final stage. This is the first solid-waste-to-energy plant location evaluation and selection model used in a renewable energy project in Vietnam based on expert interviews and a literature review. This study’s contribution can be a significant guide in analyzing and selecting appropriate locations for solid-waste-to-energy projects, as well as for decision makers and investors in other renewable energy projects in Vietnam and throughout the world.

Associated Fuzzy Probabilities in MADM with Interacting Attributes: Application in Multi-Objective Facility Location Selection Problem

For decreasing service centers’ selection risks in emergency facility location selection, it is crucial to have selected candidate service centers within deeply detailed facility location selection model. To achieve this, a new approach developed in this article involves two stages. In the first stage, the fuzzy multi-attribute group decision making (MAGDM) model for evaluation of the selection of candidate service centers is created. For the aggregation of experts’ assessments of candidate service centers (with respect to attributes) aggregation operators’ approach is used. Experts’ assessments are presented in fuzzy terms with semantic form of triangular fuzzy numbers. For the deeply detailed facility location selection modeling and for the intellectual activity of experts in their evaluations, pairwise interactions between attributes of MAGDM model are considered in the construction of the second-order additive triangular fuzzy valued fuzzy measure (TFVFM). The associated triangular fuzzy probability averaging (As-TFPA) aggregation operators’ family is constructed with respect to TFVFM. Analytical properties of the As-TFPA operators are studied. The new operators are certain extensions of the well-known Choquet integral operator. The extensions, in contrast to the Choquet aggregation, consider all possible pair-wise interactions of the attributes by introducing associated fuzzy probabilities of a TFVFM. At the end of the first stage, a candidate service center’s selection index is defined as As-TFPA operator’s aggregation value on experts’ assessments with respect to attributes. At the second stage, fuzzy multi-objective facility location set covering problem (MOFLSCP) is created for facility location selection optimal planning with new criteria: (1) maximization of candidate service centers selection index and classical two criteria, (2) minimization of the total cost needed to open service centers and (3) minimization of number of agents needed to operate the opened service centers. For the constructed two-stage methodology a simulation example of emergency service facility location planning for a city is considered. The example gives the Pareto fronts obtained by As-TFPA operators, the Choquet integral-TFCA operator and well-known TOPSIS approach, for optimal selecting candidate sites for the servicing of demand points. The comparative analysis identifies that the differences in the Pareto solutions, obtained by using As-TFPA operators and TFCA operator or TOPSIS aggregation, are also caused by the fact that TFCA operator or TOPSIS approach considers the pair interaction indexes for only one consonant structure of attributes. While new As-TFPA aggregations provide all pairwise interactions for all consonant structures.

A novel MCDM model for warehouse location selection in supply chain management

The present investigation reveals a novel method for the evaluation of warehouse location for leagile supply chain entailing Fuzzy Multi Criteria Analysis (FMCA). An attempt has been made to apply the concept of decision theory for selecting the warehouse under contradictory criteria. Aggregate Modified Weighted Value (MWV) of normalized score of alternative is determined to evaluate Benefit Cost Ratio (BCR) which is considered as the warehouse selection index. The proposed algorithm is illustrated with a suitable numerical example to adjudge its desirable importance in capability and practicability. It also ensured that the achieved result clearly matches with those of previous research works. Finally, sensitivity analysis has been carried out that justify and supports the application of proposed algorithm in finding the most favorable outcome to the selection problem of warehouse location.

Search well and be wise: A machine learning approach to search for a profitable location

A good location is critical for the sales performance of a hotel or a restaurant. This study proposes a machine learning-based model for location selection that focuses on the estimation of sales potential of a prospective site and helps to overcome the lack of historical data for the prospective site and the subjective criteria used in conventional models. The proposed model involves three major steps. First, we use an attribute selection algorithm to identify the key factors that contribute to the profitability of a specific location. Second, we evaluate the similarity between the candidate site and the existing stores by using an improved grey comprehensive evaluation method. Finally, we use a kernel regression model to predict the sales potential of the candidate site. A case study of a well-known international restaurant chain is used to illustrate the application of the proposed data-driven model. The results indicate that our proposed model helps to accurately select the most profitable locations.

Fuzzy Multi-Criteria Decision Making for Solar Power Plant Location Selection

Vietnam is one of Southeast Asian countries with a rapid GDP growth rate, ranging from 6.5% to 7% annually, leading to an average increase in energy demand of 11% per year. This demand creates many new opportunities in the energy industry, especially renewable energy, to ensure sustainable development in the future for the country with applications of solar energy growing at the present, and other opportunities to expand in the future. In Vietnam, thanks to favorable weather, climate, terrain characteristics and many preferential support policies, there are many great opportunities in the field of solar energy exploitation and application. Location selection is an important problem in all renewable energy projects. Therefore, the author proposed a fuzzy Multi-criteria Decision-Making Model (MCDM) model for solar power plant location selection in this study, and as a result, location 5 is the optimal solution. The contribution of this study is to propose a MCDM for solar power plant location selection in Vietnam under fuzzy environmental conditions.

A multi-criteria location selection model based on fuzzy ANP and <i>Z</i>-number VIKOR methods: a case study

Initial investment for construction of a power plant is an important issue which technologies and methods are moving forward to minimise the total costs. Therefore, a power plant should be established in a region which will increase the reliability and efficiency with minimum total cost. In this study, location of a solar power plant is optimised with considering a set of criteria including environmental, economic, social and strategic aspects in which energy resource usage will be maximised along with the least cost and the economic growth of country. For this purpose, fuzzy ANP and Z-number VIKOR approaches are used to rank the 20 candidate cities in Iran. The obtained results indicate the efficiency and reliability of this study.

A multi-criteria location selection model based on fuzzy ANP and <i>Z</i>-number VIKOR methods: a case study

A multi-criteria location selection model based on fuzzy ANP and <i>Z</i>-number VIKOR methods: a case study