Facility Location Selection Problem Research Articles

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.

A selectively calibrated derivation technique and generalized fuzzy TOPSIS for semiconductor supply chain localization assessment

There is a localization wave in the semiconductor industry for pushing wafer foundries to transfer their production capacity to where chip designers are located. The main reasons for the localization of semiconductor supply chains include the US–China trade war, geopolitics, COVID-19, the Ukraine–Russia war, and rising environmental awareness. However, this is a challenging task since some of the issues considered in traditional facility location selection problems are no longer decisive factors. To overcome this challenge, this study proposes the selectively calibrated derivation technique (SCDT)-generalized fuzzy technique for order preference by similarity to ideal solution (GFTOPSIS) approach to help wafer foundries select suitable facility locations for customer-required capacity localization. The proposed SCDT-GFTOPSIS approach has been applied to a real case to validate its effectiveness. According to the experimental results, SCDT effectively improved the accuracy of approximating the fuzzy priorities of criteria by reducing the mean absolute deviation (MAD) by 81%. In addition, the most suitable location for the facility was in Singapore, where the support of the local government was higher, while requiring less total investment and subject to less political and war risk. However, the final selection was subject to further negotiations.

Facility Location and Capacity Level Selection Problem and its Application to a Supply Chain Design Case in the Period of Pandemic

Bu çalışmada kademeli kapasitelere sahip tesisler için tesis yeri ve kapasite seviyesi seçimi problemi ele alınmıştır. Doğru kapasitede doğru lokasyonda tesis seçimi ile tesislerin sabit işletme giderleri ile bu tesislerde üretilen mal ve hizmetlerin dağıtım merkezlerine taşıma maliyetlerinden oluşan toplam maliyetin en aza indirilmesi amaçlanmıştır. Bu problemin çözümü için tam sayılı doğrusal programlama modeli önerilmiştir. Bu çalışmada fark yaratan durum, bir tek aşamalı tedarik zincirinde potansiyel lokasyonlardaki tesisler arasından seçim kararının yanı sıra, kademeli kapasitelerin tesisler arasında transferi ve ya sisteme dahil olacak yeni kapasitelerin dağıtımı problemini ele almak için yeni bir tam sayılı doğrusal programlama modeli önerilmesidir. Böylece atıl kapasiteler daha iyi değerlendirilebilecek ve taşıma maliyetleri düşürülebilecektir. Gerek iş hayatında gerekse insanların bireysel yaşamında derin izler bırakan COVID-19 sürecinin tedarik zincirleri üzerindeki etkisi tüm ezberleri bozmuştur. Pandemi döneminde Pazar ihtiyaçlarının neredeyse tamamen değişmesi ile birlikte, çoğu tedarik zinciri ağı mevcut yapısıyla ihtiyacı karşılamakta zorluk çekmektedir. İşte tam bu noktada tedarik zincirini yeniden tasarlamak isteyen bir dezenfektan üreticisi ile ilgili vaka ele alınmış ve tanıtılan model bu vakaya uygulanmıştır. İncelenen vakanın ve uyarlanan modelin, benzer durumdaki vakaların çözümüne katkı sağlaması hedeflenmiştir.

Analyzing the wastewater treatment facility location/network design problem via system dynamics: Antalya, Turkey case

Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of wastewater treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015–2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.

Facility Location Selection with Integrated Fuzzy AHP and Fuzzy VIKOR Methods

Kar amacı güden her işletmenin varoluş sebebi maksimum faydayla faaliyet göstermektir. Maksimum fayda sağlama yolunda stratejik kararlar önemlidir. Bu kararlar işletmeleri uzun dönemde etkiler ve hata yapılırsa büyük maliyetler oluşabilir. Stratejik kararlardan biri tesis yeri seçimidir. Tesis yeri yatırımı karar vericilerin hedefleri doğrultusunda karlı sonuçlanmalıdır. Çalışmada Ark Pres Emniyet Kemerleri A.Ş. için tesis yeri seçimi problemi ele alınmıştır. İşletmedeki karar vericilerin kriterlerin önem düzeylerinin tek bir kararda olmaması problemi karmaşıklaştırmıştır. Çözümde Çok Kriterli Karar Verme tekniklerinden Bulanık Analitik Hiyerarşi Prosesi (BAHP) ve Bulanık Çok Kriterli Optimizasyon ve Uzlaşık Çözüm (Bulanık VIKOR) yöntemleri uygulanmıştır. Bulanık AHP ile karar vericilerin değerlendirmeleri doğrultusunda kriterler ağırlıklandırılmış, Bulanık VIKOR ile bu ağırlıklar kullanılarak alternatifler sıralanmış ve seçim yapılmıştır. Çalışmada ana kriter ağırlıklarından en önemlisi maliyet, alt kriter ağırlıklarından en önemlisi işçilik maliyetleri olarak belirlenmiştir. Uygulama sonucunda işletmenin tesis yeri beş alternatif arasından en uygun olan Kocaeli - Arslanbey Organize Sanayi Bölgesi seçilmiştir.

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.

Associated Probabilities in Interactive MADM under Discrimination q-Rung Picture Linguistic Environment

In some multi-attribute decision-making (MADM) models studying attributes’ interactive phenomena is very important for the minimizing decision risks. Usually, the Choquet integral type aggregations are considered in such problems. However, the Choquet integral aggregations do not consider all attributes’ interactions; therefore, in many cases, when these interactions are revealed in less degree, they do not perceive these interactions and their utility in MADM problems is less useful. For the decision of this problem, we create the Choquet integral-based new aggregation operators’ family which considers all pair interactions between attributes. The problem under the discrimination q-rung picture linguistic and q-rung orthopair fuzzy environments is considered. Construction of a 2-order additive fuzzy measure (TOAFM) involves pair interaction indices and importance values of attributes of a MADM model. Based on the attributes’ pair interactions for the identification of associated probabilities of a 2-order additive fuzzy measure, the Shapley entropy maximum principle is used. The associated probabilities q-rung picture linguistic weighted averaging (APs-q-RPLWA) and the associated probabilities q-rung picture linguistic weighted geometric (APs-q-RPLWG) aggregation operators are constructed with respect to TOAFM. For an uncertainty pole of experts’ evaluations on attributes regarding the possible alternatives, the associated probabilities of a fuzzy measure are used. The second pole of experts’ evaluations as arguments of the aggregation operators by discrimination q-rung picture linguistic values is presented. Discrimination q-rung picture linguistic evaluations specify the attribute’s dominant, neutral and non-dominant impacts on the selection of concrete alternative from all alternatives. Constructed operators consider the all relatedness between attributes in any consonant attribute structure. Main properties on the rightness of extensions are showed: APs-q-RPLWA and APs-q-RPLWG operators match with q-rung picture linguistic Choquet integral averaging and geometric operators for the lower and upper capacities of order two. The conjugation among the constructed operators is also considered. Connections between the new operators and the compositions of dual triangular norms (Tp,Spq) and (Tmin,Smax) are also constructed. Constructed operators are used in evaluation of a selection reliability index (SRI) of candidate service centers in the facility location selection problem, when small degree interactions are observed between attributes. In example MADM, the difference in optimal solutions is observed between the Choquet integral aggregation operators and their new extensions. The difference, however, is due to the need to use indices of all interactions between attributes.

Facility Location Selection Using Clustering Based Genetic Algorithm

Facility location selection is one of the most important decisions of companies and industries. At the same time, since a business process begins with the selection of a facility location, it is the first step to consider. Everything starts with the facility location selection. If a location far to suppliers, manufacturers or the market is selected, this will lead to increasing costs in the long run for both the company and other items in the supply network. The distant location also affects the mutual contracts in detail. Besides, the facility location has effects on labor costs and other related costs. Almost all of the costs in the company is closely related with the facility location. Based on this mentioned importance, the facility location selection problem is considered in this study, and the clustering based genetic algorithm method is proposed for the solution of facility location selection problem. In the introductory part of the study, facility location selection problem and the related literature is introduced. After, methods used in the solution are presented as K-means clustering algorithm, genetic algorithm and the proposed algorithm respectively. Detailed numerical results of the study is given in the facility location selection section by using Ruspini75 data set from Operations Research Library. All obtained results are interpreted in the results and discussion section and the study is concluded with the suggestions for future works.

Hierarchical Group Compromise Ranking Methodology Based on Euclidean–Hausdorff Distance Measure Under Uncertainty: An Application to Facility Location Selection Problem

Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classical compromise ranking. This research extends a new hierarchical group compromise ranking methodology under a hesitant fuzzy (HF)environment to handle uncertainty, in which for the margin of error, the DMs could assign the opinions in several membership degrees for an element. The hesitant fuzzy set (HFS)is taken into account for the process of the proposed hierarchical group compromise ranking methodology, namely HFHG-CR, and for avoiding the data loss, the DMs’ opinions with risk preferences are considered for each step separately. Also, the Euclidean–Hausdorff distance measure is utilized in a new proposed index for calculating the average group score, worst group score and compromise measure regarding each DM. A new ranking index is presented for final compromise solution for the evaluation. Proposed HFHG-CR methodology is applied to a practical example for a facility location selection problem, i.e. cross-dock location problem, to show the validation and application.

Sustainable shelter-site selection under uncertainty: A rough QUALIFLEX method

In recent years, nature disasters have been observed with larger scale and stronger lethality than before. As one of the most crucial links of humanitarian relief operation, shelter site selection has been paid notable attention by the researchers. Nevertheless, subjectivity and vagueness of realistic environment is neglected consequently in most of the previous literature which aims at solving facility location selection problems. Besides, prevailing MCDM methods utilized in related work have poor mechanism dealing with inconsistent data, meanwhile, they are commonly lack of flexibility. In view of the above mentioned facts, an innovative integrated method is developed in this paper. The proposed approach combines the strength of the qualitative flexible multiple criteria (QUALIFLEX) method in manipulating multiple evaluation criteria with heterogeneity and the advantages of interval rough number in blurry information environment. Moreover, social and environmental aspects which are of great significance whereas rarely mentioned are taken into consideration when shelter sites are evaluated. The competence and feasibility of the proposed method is illustrated in a case study in the settings of Wenchuan County, where a serious earthquake struck on 12th May 2008. To verify the validity of the proposed method, a comparative analysis with other related methodologies is provided.

Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem

Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem

A GIS-based interval type-2 fuzzy set for public bread factory site selection

PurposeThe purpose of this paper is to present a new public bread factory location selection for Istanbul Metropolitan Municipality (IMM).Design/methodology/approachA two-stage methodology is proposed to determine the location for the public bread factory facility. This framework is based on both geographic information systems (GIS) and multi-criteria decision-making (MCDM) techniques. The first stage of the methodology aims to decrease the number of possible alternative locations to simplify the selection activity by applying GIS; the second stage utilises interval type-2 fuzzy MCDM approach to exactly determine the public bread factory site location.FindingsIn this study, the authors present weighted normalised-based interval type-2 hesitant fuzzy and interval type-2 hesitant fuzzy sets (IT2HFSs)-based compressed proportional assessment (COPRAS) methods to overcome facility location selection problem for a fourth public bread factory in Istanbul.Practical implicationsThe results show that the proposed approach is practical and can be employed by the bakery industry.Originality/valueIn this study, the authors present a two-stage methodology for public bread factory site selection. In the first stage, the number of alternatives is reduced by the GIS. In the second stage, an interval type-2 fuzzy set is implemented for the evaluation of public bakery factory site alternatives. A new integrated approach based on COPRAS method and weighted normalised with IT2HFSs is proposed.

A New Efficient Machine Learning Algorithm to Solve Facility Location Selection Problem of Geoinformatics

A New Efficient Machine Learning Algorithm to Solve Facility Location Selection Problem of Geoinformatics

Capacity constrained maximizing bichromatic reverse nearest neighbor search

When planning a new development (facility/service site), location decisions are always the major issue. In this paper we introduce a novel query capacity constraint MaxBRNN, which can solve the facility location selection problem efficiently.The MaxBRNN (maximizing BRNN) query is based on bichromatic reverse nearest neighbor (BRNN) query which uses the number of reverse nearest customers to model the influence of a facility location. The MaxBRNN query has been appreciated extensively in spatial database studies because of its great potential in real life applications, such as, markets decision, sensor network clustering and the design of GSM (global system for mobile communication). The existing researches mostly suppose that the service facility's capacity is unlimited. However, in real cases, facilities are inevitably constrained by designed capacities. For example, if the government wants to select a new place to set up an emergency center to share the existing centers’ patients, they need to know the current emergency centers’ capacity so that they can estimate the new center's scale. Thus, the capacity constrained MaxBRNN query is significantly important in planning a new development. As far as we know, the capacity constrained MaxBRNN query has not been studied yet, so, we formulate this problem, propose a basic solution and develop some efficient algorithms for the query.Our major contributions are as follows: (1) we propose a novel query capacity constraint MaxBRNN which can solve the facility location selection problem effectively and efficiently; (2) we develop a basic algorithm CCMB and two improved algorithms which can find out the optimal region in terms of building a new facility, maximize its impact and deal with the complicated reassignment when adding new facilities into the dataset; (3) we prove the algorithms’ effectiveness and efficiency by extensive experiments using both real and synthetic data sets.

Multi-Criteria and Multi-Stage Facility Location Selection under Interval Type-2 Fuzzy Environment: A Case Study for a Cement Factory

AbstractThe study proposes a comprehensive and systematic approach for multi-criteria and multi-stage facility location selection problem. To handle with high and more uncertainty in the evaluation and selection processes, the problem is solved by using multi-criteria decision making technique with interval Type-2 fuzzy sets. The study contributes the facility location selection literature by introducing the application of fuzzy TOPSIS method with interval Type-2 fuzzy sets. Finally, the suggested approach is applied to a real life region and site selection problem of a cement factory.

A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill

Facility location selection problem is one of the challenging and famous kinds of MCDM problems including both quantitative and qualitative criteria. For each Multiple Criteria Decision Making (MCDM) problem, when the ratings of alternatives with respect to the criteria and/or the values of criteria’s weights are presented by Interval Valued Fuzzy Numbers (IVFNs), the conventional fuzzy MCDM methods (Type-1 fuzzy MCDM methods) tend to be less effective. Therefore, the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be applied for solving such fuzzy MCDM problems. In this paper, we propose an IVF-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method based on uncertainty risk reduction in decision making process. By using such method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving two numerical examples that the former of which is a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The second numerical example is presented with an aim of comparing our method with the two other IVF-MCDM methods. As a result, we found out the proposed method is reliable and practical for the facility location selection problems and other MCDM problems. Moreover, the proposed method has a considerable accuracy and is flexible and easy to use.

A new flexible and reliable IVF-TOPSIS method based on uncertainty risk reduction in decision making process

One of the challenging and famous types of MCDM (Multiple Criteria Decision Making) problems that includes both quantitative and qualitative criteria is Facility location selection problem. For the common fuzzy MCDM problems (Type-1 fuzzy MCDM problems), the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the common fuzzy numbers. However, in the majority of cases, determining the exact membership degree for each element of the fuzzy sets which are considered for the ratings of alternatives with respect to the criteria or/and the values of criteria weights as a number in interval [0,1], is difficult. In this situation, the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the IVFNs (Interval Valued Fuzzy Numbers) and thereby the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be used. In this paper, the authors propose an IVF-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on uncertainty risk reduction in decision making process. By using this method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The proposed method is also compared with another IVF-TOPSIS method. As a result, the authors concluded that in addition to benefits such as simplicity and ease of use that exist in the previous IVF-TOPSIS methods, the proposed method has a significant reliability and flexibility and is practical for facility location selection problems and other IVF-MCDM problems.

An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem

Companies pursuing extension of their activities and new companies in establishment phase are using various concepts and techniques to consider location decision, because location greatly affects both fixed and variable costs and on the overall profit of the company. This paper suggests a new use of quality function deployment (QFD) for facility location selection problem instead of applying it to traditional product quality promotion. Fuzzy sets concept is also incorporated to deal with imprecise nature of the linguistic judgments of decision makers. First, fuzzy QFD as a stand-alone approach is presented to address international facility location selection decision. To consider resource limitations and operational constraints, fuzzy goal programming is combined with fuzzy quality function deployment to present a developed approach to deal with global facility location-allocation decision. A demonstration of the applicability of proposed methodologies in a real-world problem is presented.

Facility location selection using fuzzy axiomatic design principles under group decision-making environment

Choosing the most suitable site for carrying out operations of any manufacturing or service organisation is a typical facility location selection problem. Selecting a location for a new organisation or expansion of an existing one is a crucial decision for its eventual success or failure. This paper presents a fuzzy multi-criteria decision-making (MCDM) method based on axiomatic design principles for solving facility location selection problems under group decision-making environment. The group of decision makers is assumed to be homogeneous where the degrees of importance (reliability) of all decision makers are taken to be equal to each other. The decision of selecting an appropriate facility location for a distribution centre, manufacturing set-up, commercial complex or any public organisation always involves consideration of many socio-economic criteria which can be more effectively expressed in qualitative terms. Trapezoidal fuzzy numbers are used here for representing those qualitative criteria into quantitative values. The location alternatives are ranked based on the ascending values of their total information content. Two illustrative examples are solved applying the adopted method to validate its universal applicability and acceptability as an almost accurate and comprehensive MCDM tool. A sensitivity analysis study is also performed.