A parametric assessment approach to solving facility‐location problems with fuzzy demands

Lin, Pei-Chun; Watada, Junzo; Wu, Berlin

doi:10.1002/tee.21997

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Table 2 shows the demand for all the 50 customers (C1 to C50).Transportation Cost 200/unit/unit distance and Price charged to client j is 150000 per Unit. We have taken the data sets of (Lin, 2014). Thus the maximum profit 8,42,35,350 and the minimum distance 73.64 units, corresponds to the number of clusters = 3.…”

Section: Numerical Examplesmentioning

confidence: 99%

Clustering based hybrid approach for facility location problem

Sharma¹,

Jalal²

2017

10.5267/j.msl

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The main objective of facility location problem is the utilization of the facility by maximum number of possible customers so that the profit is maximized. For instance, in some services like wireless sensor networks, Wi-Fi, repeaters, etc., where the service area is limited, some specific equipment is installed in such a way that it could be used by maximum number of users. Here, the number of users for a particular facility is optimized with the help of clustering technique. The study develops a model for facility allocation problem. For the solution algorithm, a hybrid approach which is based on clustering and mixed integer linear programming (MILP) is proposed. The proposed method consists of two parts where in the first part, the K-means clustering technique is used and in the second part, for each cluster an MILP technique is implemented so that the facility which yields the maximum profit is obtained. Numerical examples for clustering and without clustering are presented. Analysis shows that due to clustering the average distance between facility and customer is significantly reduced.

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Section: Numerical Examplesmentioning

confidence: 99%