Work

An Evaluation of Production Plans Under Uncertainty in the Automotive Industry Using EDAS and Monte Carlo Simulation

Year: 2026

Type: article

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.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.17868

Author: Ulises Mercado Valenzuela

Institutions: Facultad de Estudios Superiores Aragón, Universidad Nacional Autónoma de México

Language: English

Topic: Multi-Criteria Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431