Location Selection Problem Research Articles

Renewable Energy from Solid Waste: A Spherical Fuzzy Multi-Criteria Decision-Making Model Addressing Solid Waste and Energy Challenges

With rapid urbanization and industrialization, Vietnam is facing many challenges in solid waste management and increasing energy demand. In this context, the development of renewable energy from solid waste not only solves the problem of environmental pollution but also makes an important contribution to energy security and sustainable economic development. Solid waste to energy is a system of solid waste reatment by thermal methods, in which the heat generated from this treatment process is recovered and utilized to produce energy. Site selection is one of the biggest challenges for renewable energy projects. In addition to technical factors, this decision must also consider environmental impacts, including protecting ecosystems, minimizing noise, and limiting impacts on public health. To solve this problem, multi-criteria decision making (MCDM) methods combined with fuzzy numbers are often used. These methods allow planners to evaluate and balance competing factors, thereby determining the most optimal location for the project. In this study, the authors proposed a Spherical Fuzzy Multi-Criteria Decision-making Model (SFMCDM) for site selection in solid waste-to-energy projects. In the first stage, all criteria affecting the decision-making process are defined based on literature review, experts and triple bottom line model (social, environmental, and economic), and analytic hierarchy process (AHP), and fuzzy theory is applied for calculating the weights in the second stage. The weighted aggregated sum product assessment (WASPAS) method is utilized for ranking four potential locations in the final stage. The contribution of the proposed process is its structured, systematic, and innovative approach to solving the location selection problem for renewable energy projects. Choosing the right location not only ensures the success of the project but also contributes to the sustainable development of renewable energy.

Integrated Best-Worst Method and Gray Relational Analysis for Hospital Location Selection: The Case of Burdur Province

The hospital location (site) selection problem involves determining the most suitable location for a healthcare facility. As a multi-criteria decision-making (MCDM) problem, it requires the evaluation of several factors, including strategic, economic, and social considerations, due to the direct impact hospital locations have on public health. To make an optimal decision, MCDM methods are typically employed to accurately assess the relevant criteria. These methods assist decision-makers in evaluating various factors and determining the best location for the hospital. This study focuses on selecting a site for a private hospital planned for Burdur Province, where no private hospital currently exists. In this context, three alternative locations were evaluated based on expert opinions from academicians, health managers, and urban planners. In the first phase of the study, the Best-Worst Method (BWM) was used to calculate the criteria weights, allowing for the subjective evaluation of multiple factors. Following this, the Gray Relational Analysis (GRA) method was applied to determine the most appropriate location by assessing the degree of similarity or dissimilarity between the alternatives. This combination of methods enabled the ranking of the alternatives, ultimately identifying the best site for the hospital.

Interval-Valued Intuitionistic Fuzzy Yager Power Operators and Possibility Degree-Based Group Decision-Making Model

As an extended form of intuitionistic fuzzy set, the theory of interval-valued intuitionistic fuzzy set (IVIFS) can describe fuzziness more flexibly. This study aims to develop a group decision-making model based on the distance measure, Yager power aggregation operators and the possibility measure in the context of IVIFSs. For this purpose, new distance measure is proposed to quantify the dissimilarity between two IVIFSs. In addition, comparison with existing distance measures is performed to illustrate the efficiency of introduced measure. Combining the Yager’s triangular norms with the proposed distance-based power operators, a series of interval-valued intuitionistic fuzzy (IVIF) Yager power aggregation operators are introduced with their desirable properties. Moreover, a possibility measure is developed for pairwise comparisons of IVIFSs, which overcomes the shortcomings of existing IVIF-score function, IVIF-accuracy function, and IVIF-possibility measures. The developed possibility measure is further utilized to compute the weights of criteria. To prove the practicality and effectiveness of introduced model, it is applied to a case study of manufacturing plant location selection problem with IVIF information. Finally, sensitivity and comparative analyses are carried out to test the stability and robustness of the proposed method under the setting of IVIFSs.

Application of Z-number based fuzzy MCDM in solar power plant location selection problem in Spatial planning

In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

A COMBINATION OF AHP AND GRA FOR FURNITURE MANUFACTURING FACILITY LOCATION SELECTION IN A HYBRID FUZZY ENVIRONMENT

This paper aims to solve the location selection problem for a newly established furniture facility. The choice of location is directly related to the costs businesses should bear to continue their operations throughout their life cycle. Therefore, the location of the establishment is very critical for enterprises. For this problem, we propose a combined method with fuzzy analytic hierarchy process (F AHP) and fuzzy grey relation analysis (F-GRA). F-GRA is used to rank the alternatives, while the priority weights of the criteria are estimated via F-AHP. The use of multi-criteria decision-making methods is not only for cost optimization but also optimization of many parameters (technical, social, economic, demographic, etc.) simultaneously to ensure the sustainability of enterprises. In this problem, the main factors considered are closeness, environment, infrastructure, and economy, and the candidate locations are selected as Antalya, Duzce, Gaziantep, Kayseri, Kocaeli, cities in Turkey. The application steps of the combined method are defined, and the result of the case problem is represented.

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.

Literature analysis of the location selection studies related to the waste facilities within MCDM approaches.

The increase in waste and related environmental problems is one of the major problems compromising health and environmental quality in urban and rural areas. There are a number of policies that can be implemented to reduce waste, but since it cannot be completely eliminated, recycling and disposal facilities for waste will always be required. Researchers and professionals are currently grappling with the issue of where to locate waste facilities. In the light of all this information, a literature review is presented so that researchers can easily access and systematically review previous studies on the waste facility location selection problem. At this point, in order to reduce the reviewed studies to a reasonable level and to conduct a more organized research, this literature research has conducted within the framework of multi-criteria decision-making (MCDM) approaches, which is one of the most applied methods in location selection problems. The subsequent strengths, weaknesses, opportunities, and threats (SWOT) analysis delves into the strengths, weaknesses, opportunities, and threats in the field, offering a concise guide for future research in waste facility location selection problem. The SWOT analysis highlights the strengths of global environmental awareness and versatile MCDM approaches, while addressing weaknesses in emerging technology integration and potential biases. Opportunities for interdisciplinary collaboration and integration of sustainability metrics provide strategic pathways, but threats such as regulatory changes and limited funding underscore challenges. This analysis serves as a concise guide for future research in waste facility location selection.

Location selection for a lumber drying facility via a hybrid Pythagorean fuzzy decision-making approach

The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.

Ranking Performance of MARCOS Method for Location Selection Problem in the Presence of Conflicting Criteria

The selection of the best location for a facility is generally a complex process especially when considering multiple criteria in the selection process. The costs associated with purchasing the land and related constructions makes the location selection problem a long-term investment decision. Hence, the problem should be analysed carefully with a reliable approach to avoid the consequences that follows awkward decisions. Many multiple criteria decision-making (MCDM) methods are developed recently which made the selection of a suitable MCDM method has become a confusing decision. In this study, measurement alternatives and ranking according to compromise solution (MARCOS) method is used to solve a location selection problem with conflicting criteria. The ranking produced by MARCOS method is analysed by a comparison with other common MCDM methods and a sensitivity analysis. The aim of the comparison to show that the method agreed the choice of the best alternative while the sensitivity analysis is employed to check the robustness and efficiency of this method. The sensitivity analysis includes the sensitivity of weight test and rank reversal (RR) test. The comparison of ranking with different methods showed that MARCOS method has strong ranking correlation with other methods. Moreover, the sensitivity analysis showed the reliability and robustness of MARCOS method as it produced high consistency of ranking through the tests. Thus, this study validates the applicability of MARCOS method to handle the presence of conflicting criteria in location selection problems.

A multi-objective optimization model for the location of cold chain logistics distribution center based on trust domain optimization algorithm

Cold chain transportation refers to a logistics method that transports fresh, perishable, and perishable items from the place of production, processing or warehouse to the place of consumption under certain temperature conditions. As consumers have higher and higher requirements for food safety and quality, the cold chain logistics industry has also developed rapidly. However, the high cost of cold chain logistics, the difficulty of technology, the difficulty of ensuring service quality and other problems have also arisen, and how to optimize cold chain transportation has become a hot issue in the cold chain logistics industry. Cold chain transportation optimization refers to the premise of ensuring the quality and safety of goods, through reasonable transportation paths, transportation methods, temperature control and other measures, to minimize the cost of cold chain transportation, improve efficiency and service quality. In order to achieve the optimization of cold chain transportation, it is necessary to establish a reliable cold chain logistics distribution center to ensure the safety and quality of goods during transportation. In order to solve the problem of location selection of cold chain logistics distribution center, this paper first defines the service reliability and calculation method of cold chain distribution center. Then, the location model of the cold chain logistics distribution center and the trust domain optimization algorithm model were established. The model aims to minimize the total cost, while considering multiple factors such as transportation distance, temperature control, facility construction, etc., to ensure the reliability and operational efficiency of the cold chain logistics distribution center. Finally, this paper introduces an example for calculation to prove the feasibility and universality of the model in the location problem of cold chain logistics distribution center. Through the calculation of examples, it can be seen that the model can effectively reduce the cost of cold chain transportation and improve the service quality and efficiency of cold chain logistics distribution center. In summary, this paper proposes a reliable location model and trust domain optimization algorithm model for cold chain logistics distribution centers to solve the challenges faced by the cold chain logistics industry. This model can provide guidance for cold chain logistics companies to gain a greater advantage in a highly competitive market.

Single-Valued Neutrosophic Sets Based Score Function and WASPAS Method for Plant Location Selection Problem

In the context of single-valued neutrosophic sets (SVNSs), where the weights of the criteria are unknown, this work seeks to provide an integrated technique based on Entropy and Weighted Aggregate Sum Product Assessment (WASPAS) methodologies. A numerical case study (the plant site selection issue) is used to establish precise values for the criteria. We initially "fuzzify" the decision matrix before transforming it into an SVNS- decision matrix. The SVNS entropy weight technique is used to assign weights to each criterion. The scoring function is used by a tweaked version of the WASPAS algorithm to choose the optimal solution. The subject under study has also been applied to other MCDM methods, such as the more prevalent VIKOR and MOORA procedures. The SVNS-VIKOR technique has also been applied to the issue at hand. Using the same illustrative scenario, this study evaluates the strengths and weaknesses of several methods for verifying the accuracy of previously published data. The usefulness and efficiency of the used strategy are highlighted by the comparison study. When applied to real-world situations, the suggested method shines because it yields reliable findings even though the underlying data is plagued with issues such as uncertainty, imprecision, indeterminacy, and inconsistency.

A hybrid robust SBM-DEA, multiple regression, and MCDM-GIS model for airport site selection: Case study of Sistan and Baluchestan Province, Iran

This study focuses on the strategic decision-making process for selecting airport locations in developing countries, where investment constraint is a significant concern. Recognizing that airport location decisions are influenced by a multitude of factors, often in the absence of comprehensive data and amidst uncertain forecasts, there is a need for a methodology capable of adeptly handling various factors under uncertainty. The goal is to provide solutions that remain feasible and near-optimal, even with some variations in input parameters, ensuring robustness. To this end, this research introduces a decision tool incorporating Slack-Based Measurement in Robust Data Envelopment Analysis (SBM-RDEA). The Slack-based method is beneficial in cases with multiple inputs and outputs where their relationships are not strictly proportional, as in the airport location selection problem. DEA, in conjunction with Multiple Regression analysis, leverages available data on the attributes of operational regional airports to compute the significance of these criteria in shaping the success factors of these facilities. Furthermore, these weightings can be employed to evaluate the suitability of airport locations currently under development and assess their potential for contributing to regional development. Additionally, to address the uncertainty of input data, a Robust optimization approach is employed to ensure a reliable response for this strategic decision-making process. This weighting framework is utilized twice within the research methodology: first, to assess the potential of different counties in the state for hosting a new regional airport, and second, to identify the optimal location for the airport in an MCDM-GIS analysis. This methodology has been applied to a case study in Sistan and Baluchistan, Iran. The result of the proposed method has been compared with one of the most common existing methodologies. This method identifies Zahedan County as having the highest potential and selects the location (29.48° N, 60.90° E), in the east of Zahedan city, as the optimal site for airport construction. Validation results confirm the efficacy of this solution.

Novel decision-making methods for the sustainable warehouse location selection problem considering the value alteration boundaries and accumulation of alternatives

Novel decision-making methods for the sustainable warehouse location selection problem considering the value alteration boundaries and accumulation of alternatives

Multi-attribute group decision making method for sponge iron factory location selection problem using multi-polar fuzzy EDAS approach

This paper presents new averaging operators, such as mpF Einstein weighted averaging (mpFEWA), mpF Einstein ordered weighted averaging (mpFEOWA), mpF Einstein hybrid weighted averaging (mpFEHWA), mpF Einstein weighted geometric (mpFEWG), and mpF Einstein hybrid weighted geometric (mpFEHWG), as well as new Einstein operations (mpFNs) for handling multi-polar fuzzy numbers. We evaluate these operators for idempotency, boundedness, monotonicity, and commutativity, and we design them to deal with multi-polar fuzzy numbers (mpFNs). Furthermore, the study investigates the use of these operators in MAGDM settings, namely mpFEWA and mpFEWG operators, to expand on this. Additionally, it proposes a procedure for determining the best site for a sponge iron production plant by use of the created MAGDM method. The EDAS method, which stands for “Evaluation based on Distance from Average Solution,” verifies that the solutions are effective. Finally, the suggested model highlights the benefits and possible improvements provided by these creative strategies by comparing the new approach to conventional methods and evaluating its efficiency and practicality.

Location selection for offshore wind power station using interval-valued intuitionistic fuzzy distance measure-RANCOM-WISP method

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment.

Green logistics park location selection with circular intuitionistic fuzzy CODAS method: The case of Istanbul

In this study, it is aimed to integrate CODAS method with circular intuitionistic fuzzy sets as a new solution method for MCDM problems. Containing a radius notation with degrees of central membership and non-membership degrees is the main advantage of circular intuitionistic fuzzy in decision making. On the other side, Combinative Distance-based Assessment (CODAS) method contains many advantages such as basing on two types of distance calculations (Euclidean and Taxicab distances) comparing with other MCDM methods. When the advantages of circular intuitionistic fuzzy sets and CODAS method are considered, proposed circular intuitionistic fuzzy CODAS method (CIFS-CODAS) presents many superiorities compared to other MCDM techniques. By this way, an application for green logistics park location selection will be handled by using CIFS-CODAS to show the validity of the methodology. After, a comparative analysis with intuitionistic fuzzy CODAS (IFS-CODAS), intuitionistic fuzzy TOPSIS (IFS-TOPSIS) and intuitionistic fuzzy EDAS (IFS-EDAS) methods will be performed for green logistics park location selection problem to confirm the robustness of presented method. Green logistics and Green Deal are also emphasized considering environmental factors as a scope of the article. Finally, the results will be evaluated in the context of the logistics sector and green logistics.

Combining subjective and objective weights considerations to solve the emergency location selection problems under spherical fuzzy environments

The development of an emergency will cause difficulties in the selection of emergency locations. The COVID-19 pandemic has caused the establishment of emergency medical facilities to become more important and urgent. However, the selection of emergency locations usually involves unclear assessment information, uncertain information, and unacquirable partial information. Due to incomplete evaluation information, traditional fuzzy set and intuitionistic fuzzy set methods cannot effectively deal with the selection problem of emergency locations. In order to effectively solve the selection problem of emergency locations during an emergency, this paper integrated the spherical fuzzy set and subjective and objective weight considerations to provide correct and reasonable evaluation results for the selection of emergency locations. For the numerical verification, this paper applied the selected location for an emergency temporary hospital during the COVID-19 pandemic location in Istanbul, Turkey as an illustrative example and compared the differences in the calculation results among the proposed flexible emergency location selection method, the spherical weighted arithmetic mean method and the spherical aggregation operator method. The numerical verification assessment results demonstrated that the proposed flexible emergency location selection method not only can deal with unclear, uncertain, and even unacquirable partial information at the same time, but also can consider the subjective and objective weights of assessment data, which could provide effective and correct emergency location selections under spherical fuzzy environments.

Inaccuracies in Expert Judgment: Comparative Analysis of RANCOM and AHP Methods in Housing Location Selection Problem

Inaccuracies in Expert Judgment: Comparative Analysis of RANCOM and AHP Methods in Housing Location Selection Problem

Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique

Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.