Multi-Criteria Decision Support for Fairness-Aware Coordination in Distributed Resource-Constrained Multi-Project Scheduling

Abstract

In engineering R&D organizations, resource contention among autonomous projects creates complex scheduling challenges in distributed multi-project environments. Traditional coordination mechanisms often prioritize global efficiency while overlooking inter-project fairness, which can lead to stakeholder resistance and execution delays. This study proposes a transparent decision support approach for the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). We develop a two-stage scheduling mechanism that integrates Multi-Criteria Decision-Making (MCDM) into the global coordination process. First, an enhanced Chaotic Genetic Algorithm (CGA) with elitism generates local schedules. Second, global resource conflicts are resolved using MCDM methods. Inter-project fairness is implemented by limiting each project’s relative objective deterioration and by evaluating the dispersion of the resulting project-level burdens. Validation through an Unmanned Aerial Vehicle R&D case study and extensive experiments shows that the TOPSIS-based mechanism achieves a significantly lower standard deviation than the auction-based mechanism under high resource contention. The approach supports transparent and fairness-aware conflict resolution by making trade-offs among project-level outcomes explicit to managers.