A Two-Stage Algorithm Based on 12 Priority Rules for the Stochastic Distributed Resource-Constrained Multi-Project Scheduling Problem With Multi-Skilled Staff

In practical multi-skilled resource-constrained multi-project management, the activity duration is often affected by some factors (e.g., rework, increased workload), leading to uncertainty. Moreover, multiple projects are often managed under a distributed decision-making environment. To deal with uncertain activity durations in distributed multi-project management with multi-skilled staff, this paper studies a stochastic distributed resource-constrained multi-project scheduling problem with multi-skilled staff (MS-SDRCMPSP). In a distributed decision-making environment, a two-stage model with local scheduling and global coordination stages is established to describe MS-SDRCMPSP. A two-stage algorithm with 12 priority rules (TSA-12PRs) is proposed, these 12 priority rules are composed of 4 activity priority rules and 3 resource priority rules. In the local schedule stage, 4 activity priority rules (PRs) are applied to obtain the local schedule plan. In the global coordination phase, we develop 3 resource PRs based on variable neighborhood search (VNS), of which VNS is used to solve the execution order of conflicting projects, and 3 resource PRs are developed to formulate multi-skilled resource assignment strategies. Based on the multi-skilled instances adapted from benchmark instances, we evaluate the performance of the 12 PRs on different instances. The experiment results show that two PRs among 12 PRs perform better than other PRs in all-size instances. Comparing the two-stage algorithm with better two PRs with other approaches in literatures, we find that our method performs better than other approaches, especially in large-size instances. In addition, further experiments show that our method is more conducive to shortening the CPU runtime on distributed problems than centralized methods.

Automated summary

This paper studies a stochastic distributed resource-constrained multi-project scheduling problem with multi-skilled staff. A two-stage algorithm with 12 priority rules is proposed, and the performance of these rules is evaluated on different instances. The experiment results show that two priority rules perform better than others, and our method outperforms other approaches, especially in large-size instances. Furthermore, our method is more conducive to shortening the CPU runtime on distributed problems than centralized methods.