Fuzzy decision-making for cost-effective production planning in manufacturing systems

Abstract

This research addresses the critical issue of production planning through innovative methodologies. By introducing a fresh perspective to the classical Economic Production Quantity (EPQ) model, the study incorporates hexagonal fuzzy numbers to accommodate a single-stage system with rework. In today's manufacturing landscape, the presence of imperfect items significantly impacts industry operations. The proposed model offers a promising solution by effectively reducing system costs. Central to the study is the enhancement of single-machine maintenance and lifespan, ensuring optimal production costs across both regular production periods and revamp periods. Through meticulous analysis, the research aims to minimize the anticipated annual total cost by evaluating reworkable item costs, disposal costs, and penalty lost sale costs within the realms of fuzzy and Neutrosophic arenas. Furthermore, the consideration of service level constraints is integral, with the research demonstrating the convexity of the proposed model under such constraints. To illustrate the efficacy of the approach, a numerical example based on a uniform distribution is presented. Crucially, the study compares results between traditional crisp cases and uncertain fuzzy environments to validate optimal policies. Additionally, the research proposes novel algorithms to define the total cost function of the production process. By leveraging triangular fuzzy numbers, hexagonal fuzzy numbers, and hexagonal interval-valued Neutrosophic numbers, unexpected cost functions are effectively addressed. An illustrative example further elucidates the application of these algorithms. To ensure the robustness and reliability of the proposed approach, simulation analysis is employed to validate its accuracy. Through these comprehensive methodologies and findings, the research contributes significantly to advancing production planning strategies and optimization techniques in modern manufacturing environments.