scholarly journals Revised Weighted Fuzzy C-Means and Fortified Weiszfeld Hybrid Method for Uncapacitated Multi-Facility Location Problems

2019 ◽  
Vol 10 (4) ◽  
pp. 53-71 ◽  
Author(s):  
Isik Guzelgoz ◽  
Sakir Esnaf ◽  
Tarik Kucukdeniz
Keyword(s):  
Facility Location ◽  
Hybrid Method ◽  
Location Problems ◽  
Facility Location Problems ◽  
Weiszfeld Algorithm ◽  
Fuzzy C Means ◽  
Pioneer Study ◽  
Method Show

In this article, a hybrid method is proposed to solve the uncapacitated planar multi-facility location problems. The new hybrid method consists of a combination of the Revised Weighted Fuzzy C-Means (RWFCM) algorithm proposed by Esnaf and Küçükdeniz (2013) and the Fortified Weiszfeld algorithm developed by Drezner (2015). The cluster centers and the cluster assignments of the RWFCM are fed into the Fortified Weiszfeld Algorithm separately for each cluster and facility-customer allocations are determined. The proposed approach is benchmarked on sample datasets from the facility location literature. Results of the proposed hybrid method show that the newly proposed sequentially-run method achieves better results when compared against the benchmark methods. This paper is a pioneer study of the hybrid use of Revised Weighted Fuzzy C-Means and Fortified Weiszfeld algorithms.

Heuristic Optimization-Based Clustering Solution for Large Facility Location Problems

2016 ◽  
pp. 171-191
Author(s):  
Tarık Küçükdeniz ◽  
Şakir Esnaf
Keyword(s):  
Particle Swarm Optimization ◽  
Facility Location ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Location Problems ◽  
Heuristic Optimization ◽  
Swarm Optimization ◽  
Facility Location Problems ◽  
Fuzzy C Means ◽  
Standard Particle Swarm Optimization

Facility location-allocation problems are one of the most important decision making areas in the supply chain management. Determining the location of the facilities and the assignment of customers to these facilities affect the cap of achievable profitability for most of the companies' supply chains. Geographical clustering of the customers, while considering their demands, has been proved to be an effective method for the facility location problem. Heuristic optimization algorithms employ an objective function that is provided by user, therefore when the total transportation cost is selected as the objective function, their performance on facility location problems is considered to be promising. The disadvantage of population based heuristic optimization algorithms on clustering analysis is their requirement of the increased number of dimensions to represent the complete solution in a single member of the population. Thus in two-dimensional geographical clustering, number of dimensions required for each population member is double of the number of required facility. In this study, a new neighborhood structure for the standard particle swarm optimization algorithm is presented for uncapacitated planar multiple facility location problem. This new approach obsoletes the need for higher number of dimensions in particles. Proposed method is benchmarked against k-means, fuzzy c-means, fuzzy c-means & center of gravity hybrid method, revised weighted fuzzy c-means and the standard particle swarm optimization algorithms on several large data sets from the literature. The results indicate that the proposed approach achieves lower total transportation cost within less computational time in facility location problems compared with the standard particle swarm optimization algorithm.

Integration of AHP-QFD for selecting facility location

2015 ◽  
Vol 22 (3) ◽  
pp. 411-425 ◽  
Author(s):  
Rajesh Chadawada ◽  
Ahmad Sarfaraz ◽  
Kouroush Jenab ◽  
Hamid Pourmohammadi
Keyword(s):  
Facility Location ◽  
Solution Process ◽  
Pairwise Comparison ◽  
Integrated Approach ◽  
Point Of View ◽  
Location Problems ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Facility Location Problems ◽  
Managerial Experience

Purpose – The purpose of this paper is to describe and implements an analytic hierarchy process (AHP)-QFD model for selecting the best location from an organization point of view which picks the site with the best opportunity requirements. Integration of AHP-QFD process gives us a new approach to assist organizations through observing various factors and selecting the best location among different alternatives. This approach uses AHP method to match the preferences required by decision makers and these preferences are applied to the characteristics of QFD. The model fundamental requirement are perfect potential locales and the areas are contrasted and both quantitative and qualitative elements to permit directors to join managerial experience and judgment in the answer process. The AHP-QFD model is also applied on a case study to illustrate the solution process. Design/methodology/approach – The integration of AHP and QFD is used to analyze available options and select the best alternative. This can be done by ranking each criterion through a pairwise comparison. Given collected data, the QFD approach is used to find the capability of each criterion. Findings – Integration of AHP-QFD is used to select the best alternative in facility location. This integrated approach can be best used in dealing with facility location problems. Originality/value – The developed AHP-QFD model in facility location problems, facilitates the inclusion of market criteria and decision maker opinion into the traditional cost function, which has been mainly distance base in the literature.

On Constrained Facility Location Problems

2008 ◽  
Vol 23 (5) ◽  
pp. 740-748 ◽  
Author(s):  
Wei-Lin Li ◽  
Peng Zhang ◽  
Da-Ming Zhu
Keyword(s):  
Facility Location ◽  
Location Problems ◽  
Facility Location Problems
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