Cooperative multi-actor multi-criteria optimization framework for process integration

Paradigm shifts towards sustainable production systems, as intended by industrial symbiosis and circular economy solutions, require multi-criteria decision-making and consensus among resource-sharing actors, yet a structured methodology for balancing diverse company preferences in such integrated process systems remains absent. This paper introduces the Process Integration Multi-Actor Multi-Criteria Optimization (PI-MAMCO) framework, which builds upon heuristic principles for the solution of multi-actor multi-criteria situations, while incorporating holistic optimization and cooperative game theory techniques, specifically tailored for addressing PI challenges. For the first time, multiple criteria benefits are allocated simultaneously, and the consequences on stability are discussed. The method is demonstrated in a palm oil complex, integrating a mill, a combined heat and power plant, and a refinery to efficiently manage effluents and generate utilities for internal use or sale. Goal programming yields stable coalitional benefits of 5.7 million USD and 41.0 kt CO 2 e per year, allowing fair benefit allocation among participants without any party incurring a loss in any criterion. This demonstrates how PI-MAMCO enables stakeholders in the process industry to propose binding inter-company collaboration contracts by harmonizing the perspectives of involved actors, identifying agreements impractical to achieve otherwise. However, unstable regions for certain preference articulations are identified, and examples for unstable allocations using the often employed Shapley value and the Maali’s method are given. • Proposes PI-MAMCO method for Multi-Actor Multi-Criteria Process Integration problems. • Identifies stable and unstable regions in Multi-Criteria allocations. • Demonstrates stability with economic and environmental benefits in Palm Oil Complex. • Shows situations where popular allocation methods may lead to unstable solutions.