Efficient Priority Rules for Resource Allocation of Stochastic Decentralized Multi-Project Scheduling Problem

Multiple projects are increasingly managed in a decentralized environment, resulting in an inapplicable centralized resource-constrained multi-project scheduling problem (CRCMPSP). A decentralized resource-constrained multi-project scheduling problem (DRCMPSP) is proposed to ensure the timely delivery of products and services within budget. DRCMPSP often faces difficulty in effectively addressing resource conflict. Moreover, activity durations are hard to determine in advance due to various unpredictable events. Therefore, this paper investigates the performance of priority rules (PRs) for global resource allocation of stochastic DRCMPSP. A two-stage heuristic based on PR is then developed to resolve global resource contention under a stochastic environment. 25 PRs from the literature were selected and incorporated into our approach to explore their efficiency on global resource allocation in different global objectives, that is, the expected average project delay (EAPD) and expected total makespan (ETMS). Computational experiments based on the Multi-Project Scheduling Problem LIBrary (MPSPLIB) dataset show that more efficient PRs always favor activities that try to obtain a low delay for each project for EAPD, whereas for ETMS, more efficient PRs tend to favor the longest projects. Furthermore, the hybrid PRs based on more efficient PRs are designed to allocate global resources, and experimental results show that the average percentage of 3 hybrid PRs better than that of their components is 1.14% for EAPD and 0.81% for ETMS, respectively.