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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

  • M. O. Okwu
  • , L. K. Tartibu
  • , E. O. Ojo
  • , S. Adume
  • , J. O. Gidiagba
  • , J. Fadeyi
  • University of Johannesburg
  • Federal University of Petroleum, Effurun
  • University of Stirling
  • Memorial University of Newfoundland
  • University of Buffalo

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

Original languageEnglish
Pages (from-to)1266-1279
Number of pages14
JournalProcedia Computer Science
Volume217
DOIs
Publication statusPublished - 2022
Event4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022 - Linz, Austria
Duration: 2 Nov 20224 Nov 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Adaptive Neuro-Fuzzy Inference System
  • Dual Source
  • Multi-Destinations
  • Multi-Echelon

ASJC Scopus subject areas

  • General Computer Science

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