Sustainable clustering of customers using capacitive artificial neural networks: A case study in Pegah Distribution Company

Saeed Yousefi, Hadi Shabanpour, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


To survive, organizations should inevitably work based on sustainability principles in an ever-increasing changes of markets. Appropriate flexibility and responsiveness are particularly important when considering sustainability issue and market changes in clustering problem. One of the uses of clustering can be allocation of resources and equipment for providing the highest level of customer service which has been a matter of concern for decision makers in distributive companies. Capacitive clustering is a common method for solving allocation and distribution problems. However, traditional clustering models ignore sustainability criteria in defining clusters' capacity. The objective of this study, therefore, is to propose a novel method for optimizing resource allocation for customers given the sustainability criteria. Capacitive clustering is a technique that has a widespread application in data mining. This approach has been used for equipment distribution, sales targeting, market segmentation, etc. One prevalent clustering method is growing neural gas network (GNGN) technique. GNGN is a neural network with uncontrolled learning. In this paper, for the first time, we utilize GNGN to cluster customers given sustainability criteria. Here, the clusters' centers are determined and allocated with regard to capacity constraints of the clusters. The obtained results in general can be regarded as an optimized sustainable distribution system in which the number of trucks, distribution routes as well as fuel consumptions and environmental pollutions are minimized. We can also refer to reductions in urban traffic, maintenance costs, staff costs, and decreases in the fatigue of drivers and distributers due to the proximity of supermarkets. An illustrative case study is done to indicate the applicability and remarkable contributions of the suggested clustering approach.

Original languageEnglish
Pages (from-to)51-60
Number of pages10
JournalRAIRO - Operations Research
Issue number1
Publication statusPublished - Jan 1 2021


  • Capacitive artificial neural network
  • Customers' clustering
  • Distribution companies
  • Neural gas network (NGN)
  • Sustainable clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science Applications
  • Management Science and Operations Research

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