A Fuzzy Topsis Approach For Logistics Center Location Selection

Erkayman, Burak; Gundogar, Emin; Akkaya, Gökay

doi:10.19030/jbcs.v7i3.4263

Cited by 45 publications

(30 citation statements)

References 12 publications

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“…In recent years, many researchers have used the Fuzzy problems. The determination of the best location for a wide r as solar power plants [74], logistics centers [75][76][77], distribu facilities [82][83][84], hospitals [85], landfills [86], stations [87] an Fuzzy TOPSIS is a useful method to select the appropriate loc sites.…”

Section: Fuzzy Topsis Methodsmentioning

confidence: 99%

“…This study used the fuzzy d proposed by [73], where the fuzzy positive and negative solutions were taken as ,0) respectively. years, many researchers have used the Fuzzy TOPSIS method for location selection determination of the best location for a wide range of places has been attempted, such plants [74], logistics centers [75][76][77], distribution centers [78,79], warehouses [80,81], ], hospitals [85], landfills [86], stations [87] and faculties [88]. The research shows that is a useful method to select the appropriate location for different kinds of facilities and the Problem of Location Selection by the Fuzzy TOPSIS Method pping malls have an increasing tendency to spread around the country, the issue of location remains prominent.…”

Section: Problem Of Location Selection By the Fuzzy Topsis Methodsmentioning

confidence: 99%

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A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Erdin

Akbaş

2019

Sustainability

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Multi-criteria decision-making (MCDM) techniques are increasingly being used for the problem of location selection, which directly affects the long-term success of a company. Besides these techniques, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) also represent a useful method for selecting the appropriate location for different kinds of facilities and sites. In this respect, this study aims to compare the results of a MCDM technique, fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and GIS for the location selection of shopping malls in Turkey. According to the results of both fuzzy TOPSIS and GIS, the Marmara region was determined as the best alternative for shopping malls in Turkey.Sustainability 2019, 11, 3837 2 of 22 sense, it can be easily said that location selection represents a typical multi-criteria decision-making (MCDM) problem [3][4][5][9][10][11].As an advanced and a widely used subdiscipline of operations research, MCDM basically represents a decision-making technique that can be used to evaluate a number of alternatives by taking into account multiple and usually conflicting criteria that may be quantitative or qualitative, and aims to provide support to the decision maker for making the choice between alternatives [9,[12][13][14]. In this framework, MCDM can be defined as the process of evaluating alternatives for the purpose of selection or ranking via utilizing a number of qualitative and/or quantitative criteria that have different measurement units [15].MCDM involves a set of techniques such as min-max, max-min, ELECTRE, PROMETHEE, TOPSIS, fuzzy TOPSIS, compromise programming, analytic hierarchy process (AHP), fuzzy AHP, data envelopment analysis, and goal programming, that can be used for comparing and prioritizing multiple alternatives and finally selecting the best-fit choice [16]. Among these techniques, for the solutions to location problems that contain vague and incomplete data and linguistic variables, fuzzy decision-making techniques have been attracting growing attention [12,17].On the other hand, it should be stated that these techniques are not suitable for spatial data [18]. In this context, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) have been applied for solving location problems [18][19][20]. GIS represent computer-based tools that can be used for maintaining, managing, integrating and analyzing spatial data from different sources. GIS allow us to store, edit, manipulate and analyze geographically referenced data to generate interpretive maps and related statistics relevant for decision making [11,14,21]. In this sense, one could say that although MCDM and GIS have developed independently, they actually support one another and their combination generates more useful and reliable information that makes it possible to reach accurate decisions, especially in the case of location selection [11,14,22].Starting from this point of view, thi...

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Section: Fuzzy Topsis Methodsmentioning

confidence: 99%

Section: Problem Of Location Selection By the Fuzzy Topsis Methodsmentioning

confidence: 99%

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Erdin

Akbaş

2019

Sustainability

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“…Based on a definite graph whose vertices and edges are predetermined, Zhu et al established the node location model of 0-1 linear programming to find the best logistics node [12]. Considering the characteristics of urban environment and logistics nodes, Erkayman et al proposed the fuzzy TOPSIS (technique for order preference by similarity to an ideal solution) method of planning the logistics center [13]. From the point of view of transportation, construction, economy, technology, and sustainable development, Turskis and Zavadskas presented the ARAS-F (fuzzy additive ratio assessment) method to select the logistics center [14].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Network Planning of Underground Logistics System on Uncertainty Graph

Zhong

Luo

Bao

et al. 2019

Mathematical Problems in Engineering

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When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

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“…These activities can include distribution, storage, transportation, consolidation, handling, customs clearance, imports, exports, transit processes, infrastructural services, insurance, and banking. These centers are defined for all national and international logistic and related operations (Erkayman et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…A logistics center is primarily an inter-modal terminal, which is the principal component of the intermodal transport chain that constitutes the node where the transhipment of goods from one mode to the other takes place. Logistics centers must be settled near production and commercial centers, highways, railways, airports and, if possible, seaports (Erkayman et al 2011). However, a logistics center is more than a freight village or intermodal terminal, as it provides many other logistics-related services mentioned before.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the logistics center locations using a multi-criteria spatial approach

Önden¹,

Acar

Eldemir

2016

Transport

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The private sector assumes that logistics centers create cost benefits for their operations. On the other hand, the public sector also assumes that logistics sectors maintain harmony with an aim to improve the logistics network structure and efficiency. In Turkey, nineteen logistics centers are on-going to develop a system approach and integrate different transportation modes to increase logistics performance. In this study, we focused on a multi-stage methodology that combines the fuzzy analytic hierarchy process, spatial statistics and analysis approaches to evaluate the suitability degrees of the logistics centers in the study area. To reach the suitability levels, seven decision criteria were considered alongside their priority levels. These criteria were proximities to highway, railway, airports, and seaports; volume of international trade; total population; and handling capabilities of the ports. The reached suitability degrees were tested using a sensitivity analysis. Different scenarios were discussed to understand how the decision environment might illustrate differences in spatial aspect.

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A Fuzzy Topsis Approach For Logistics Center Location Selection

Cited by 45 publications

References 12 publications

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Dynamic Network Planning of Underground Logistics System on Uncertainty Graph

Evaluation of the logistics center locations using a multi-criteria spatial approach

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