Optimizing Production Planning in Uncertain Environments: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms - Jose Sanchez; Gotzee Marion (International Enterprise Integration Association, 2020)
This research focuses on the optimization of production planning in uncertain environments by employing a fuzzy goal programming approach with adaptive metaheuristic algorithms. Uncertainty poses significant challenges in production planning, requiring robust methodologies to handle conflicting objectives and unpredictable factors. In this study, we propose a novel approach that integrates fuzzy logic and metaheuristic algorithms to address these challenges effectively. The fuzzy goal programming framework enables the modeling of imprecise or vague goals and constraints, providing a more accurate representation of uncertainties. The adaptive metaheuristic algorithms offer efficient optimization capabilities by exploring the solution space and adapting to changing circumstances. A mathematical formulation is developed, considering multiple objectives such as minimizing production costs, maximizing production output, minimizing inventory levels, and minimizing deviations from customer demand. The formulation is solved using appropriate metaheuristic algorithms, such as genetic algorithms. A numerical example and a case study are presented to demonstrate the practical application and effectiveness of the proposed approach. The results show that the approach successfully optimizes production planning, achieving the desired levels of satisfaction for each objective in uncertain environments. This research contributes to the field by providing decision-makers with a comprehensive and robust methodology to improve production planning strategies, enhance operational efficiency, and meet customer demands effectively in the face of uncertainty.