An integration of a Multi-Objective Optimization Design framework with Design Science Research Process for engineering problems

This work investigates how multi-objective optimization techniques can effectively solve complex engineering problems. We propose integrating the Multi-objective Optimization Design (MOOD) framework with the Design Science Research Method (DSRM) to support iterative problem-solving and knowledge acquisition. Unlike traditional approaches, this integration enables continuous refinement of models, cost functions, optimization algorithms, visualization strategies, and decision-making processes. A case focused on controller tuning for refrigeration systems, proposed by the Spanish Committee of Automation, is presented. Results highlight the flexibility of the framework and its potential to produce feasible, high-quality solutions through iterative learning. Future work will assess the real-world performance of the proposed solutions and further refine the framework for broader engineering applications.