SAGOMECON: An Adaptive ε-Constraint-Based Optimization Method for Multi-Criteria Decision-Making in Collaborative Industrial Networks

Abstract

In collaborative industrial networks, decision-making processes often involve conflicting objectives such as minimizing operational costs and risks, while simultaneously maximizing efficiency and inter-organizational collaboration. Existing multi-objective optimization methods, including the ε-constraint and its variants, face significant challenges in handling dynamic constraints and achieving computational efficiency in real-world scenarios. To address these limitations, this study introduces SAGOMECON (Simplified Adaptive Optimization with Modified ε-Constraint), an enhanced optimization approach designed to support adaptive and efficient multi-criteria decision-making in dynamic environments. SAGOMECON extends the conventional SAUGMECON framework by incorporating real-time constraint updates, adaptive slack handling, and iterative refinement mechanisms, enabling it to maintain solution feasibility under shifting priorities and evolving operational conditions. The proposed method was evaluated using simulated datasets representing partner selection scenarios in collaborative networked organizations (CNOs). Comparative analysis against the traditional ε-constraint and SAUGMECON methods demonstrates that SAGOMECON consistently delivers Pareto-optimal solutions with reduced computational time and superior adaptability to dynamic changes. The findings suggest that SAGOMECON offers a practical and scalable solution for decision-makers in collaborative industrial settings, particularly where trade-offs between competing objectives must be navigated under uncertainty. This contribution is significant for industries seeking intelligent optimization strategies that align with agile and data-driven decision-making frameworks.