An Evaluation of Production Plans Under Uncertainty in the Automotive Industry Using EDAS and Monte Carlo Simulation
Received: 30 January 2026 | Revised: 21 February 2026 | Accepted: 8 March 2026 | Online: 17 March 2026
Corresponding author: Ulises Mercado Valenzuela
Abstract
This paper proposes a methodological framework that integrates Multi-Criteria Decision-Making (MCDM) methods and stochastic simulation to evaluate production planning alternatives under uncertainty. The Evaluation based on Distance from Average Solution (EDAS) method is integrated with a Monte Carlo simulation model to assess the feasibility and dynamic behavior of four alternative production plans in an automotive company. The analysis considers fifteen criteria covering economic, operational, logistical, risk, and environmental aspects to provide a comprehensive view of the problem. The results indicate that the combined plan with buffers and flexible capacity ( ) achieves the best score in the EDAS method and is also more robust under variable demand scenarios. Furthermore, a sensitivity analysis is performed on the weights of the criteria and demand volatility, verifying that the integrated model offers better recommendations than those obtained by deterministic models or isolated MCDM methods. This work supports the development of advanced methodologies for decision-making in manufacturing, demonstrating that the integration of these approaches is of great value in the analysis of this type of problem.
Keywords:
automotive manufacturing, Evaluation based on Distance from Average Solution (EDAS), Multi-Criteria Decision-Making (MCDM), production planning, Monte Carlo simulation, uncertaintyDownloads
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