Optimizing Production Planning Under Uncertainty: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms for Improved Decision-Making

Abstract

This research addresses the challenge of uncertainty in production planning by proposing a novel approach that combines fuzzy goal programming with stochastic optimization techniques. The integration of these two methodologies provides decision-makers with a comprehensive framework to make improved and robust decisions in the face of uncertainty. Fuzzy goal programming allows decision-makers to express imprecise objectives and constraints, accommodating the inherent vagueness and trade-offs in production planning. Stochastic optimization techniques consider multiple scenarios and their associated probabilities, enabling the optimization of production plans that are robust and near-optimal across different uncertain situations. The proposed approach offers flexibility in decision-making, as decision-makers can express their preferences and goals in a subjective manner while considering uncertainty. The research contributes to the field by providing a systematic framework to manage uncertainty in production planning and improve overall performance in manufacturing organizations. The practical implications of the research are significant, as decision-makers can make informed decisions regarding resource allocation, customer demand fulfillment, and cost optimization. The research findings highlight the effectiveness of the proposed approach and its potential for application in various industry contexts. However, limitations in computational complexity and the need for further refinements are acknowledged. Future research can focus on refining the approach, addressing specific industry challenges, and extending its applicability in real-world production planning problems.