Work

A Confidence-Interval Circular Intuitionistic Fuzzy Zero Point Model for Optimizing Spare Parts Transfer in Smart Manufacturing Environments

Year: 2025

Type: conference-paper

Abstract: In Industry 4.0 systems, timely delivery of critical components to maintenance points is essential for continuous operation.This paper introduces a novel Confidence-Interval Circular Intuitionistic Fuzzy Zero Point Method (CIC-IFZPM) to optimize the transfer of spare parts in a smart factory setting.The method addresses uncertainty in transfer cost, delivery time, and priority assessment through circular intuitionistic fuzzy sets (C-IFS), which reflect both membership and hesitancy with geometric interpretation.A customized version of the index matrix algorithm integrates transportation constraints, expert confidence intervals, and machine availability limitations.The model is validated through a simulated industrial scenario, where production cells request components dynamically, and a central warehouse must allocate them optimally.Compared to classical fuzzy optimization approaches, the proposed method ensures more robust decision-making under incomplete or imprecise data, offering better performance in real-time control environments.The framework is applicable to predictive maintenance logistics, autonomous scheduling, and industrial resilience planning.

Source: Annals of Computer Science and Information Systems

DOI: 10.15439/2025f4185

Authors: Velichka Traneva, Stoyan Tranev, Mihai Petrov, Venelin Todorov

Institutions: Prof. Assen Zlatarov University, Institute of Mathematics and Informatics, Institute of Information and Communication Technologies

Language: English

Topic: Multi-Criteria Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4415420843