A hybrid approach integrating goal programming, multiple criteria decision making, and dynamic decision-making for production planning
DOI:
https://doi.org/10.35335/emod.v16i3.67Keywords:
Production planning, Goal Programming, Multiple Criteria Decision Making (MCDM), Dynamic Decision-Making, Hybrid approachAbstract
This study suggests combining Goal Programming, Multiple Criteria Decision Making (MCDM), and Dynamic Decision-Making to solve production planning difficulties. Production planning entails balancing conflicting goals and dynamic circumstances when allocating resources, scheduling production, and managing inventory. The hybrid approach provides decision-makers with a comprehensive and adaptive framework that balances conflicting objectives, analyzes options using numerous criteria, and accounts for the dynamic production environment. Goal Programming helps solve the production planning challenge. MCDM methods like AHP or TOPSIS analyze and rank various production plans based on multiple factors. Dynamic Decision-Making methods like stochastic programming or simulation optimization accommodate for demand, supply, and other uncertainties in the production environment. A numerical example shows how the hybrid approach develops an optimal production plan by minimizing deviations from desired targets. Decision-makers can evaluate objective priorities and their effects on the solution by altering objective weights in sensitivity analysis. The hybrid approach can handle conflicting objectives, evaluate options using numerous criteria, and adapt to a dynamic production environment, according to studies. The suggested approach provides decision-makers with a comprehensive framework for efficient and successful production planning, adding to current information. Applying the hybrid method to real-world case studies, addressing supply chain dynamics and sustainability, and using AI and machine learning to improve decision-making are future research objectives. Production planning using Goal Programming, MCDM, and Dynamic Decision-Making seems promising. It helps manufacturers optimize resource allocation, customer happiness, and operational efficiency
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