Original Article

A hybrid AHP-PROMETHEE decision method and machining feature interactions: a proposal for a sustainable assessment and improvement methodology

¹ Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia
² Laboratoire Roberval, Département D’Ingénierie Mécanique, Université de Technologie de Compiègne, 60203, France

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 20 August 2025
Accepted: 20 November 2025

Abstract

The manufacturing industry is currently dealing with previously uncommon challenges brought on by shorter product lifecycles, more complex products, and the growing demand for high productivity while maintaining sustainable practices. The complex interactions between machining features are frequently overlooked by traditional machining process planning techniques, which frequently concentrate only on specific cutting parameters. This constraint often leads to ineffective machining processes, increased production expenses, decreased product quality, and increased environmental impact. An innovative process optimization methodology with practical industrial applications is presented in this paper to address these issues. It combines a hybrid multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) with the real-time quantification of feature interactions. The suggested methodology offers a more accurate and realistic depiction of machining dynamics by directly incorporating interaction analysis into process planning. In contrast to traditional methods, the approach is made to integrate seamlessly with CAD/CAM environments, allowing manufacturers to assess and choose the best machining scenarios. The methodology is extremely relevant for contemporary smart manufacturing systems, where connectivity, automation, and data-driven decision-making are crucial, due to its alignment with Industry 4.0 principles. To illustrate the approach’s usefulness and applicability, a case study is provided. The findings demonstrate notable gains in environmental performance, decreased inefficiencies, and process robustness. Overall, the suggested methodology aids in the creation of innovative and intelligent machining processes that satisfy sustainability and industrial objectives.