A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

Abstract

Uncertain activity durations and multi‐skilled staff allocation are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized resource‐constrained multiproject scheduling problem with uncertain activity durations and multi‐skilled staff allocation. The robust project scheduling is employed to tackle uncertainty. We name it the robust decentralized resource‐constrained multiproject scheduling problem with multi‐skilled staff (RDMPSP‐MS). To formulate this problem, a two‐layer model containing local scheduling and global coordination decision‐making is established based on the multiagent system. To solve this model, a two‐layer approach (TLA) is developed. In the TLA, a starting time criticality heuristic method is adopted to handle the local scheduling problem, and a global resource allocation heuristic algorithm is designed to allocate multi‐skilled staff. The computational experiments are conducted on the adapted Multi‐Project Scheduling Problem LIBrary (MPSPLIB) data set. The results show that the proposed TLA outperforms seven heuristic algorithms based on priority rules regarding solution quality and efficiency on most problem subsets. It is further verified that the robustness of the baseline schedule deteriorates with the increase in problem size and activity duration variability level. Additionally, the results of this research provide practical guidance for managers who expect to determine reasonable project deadlines in a decentralized multiproject environment.

A Two-Layer Approach for the Decentralized Multi-Project Scheduling Problem Sharing Multi-Skilled Staff

“…Moreover, You et al [30] studied the robust decentralized resource-constrained multi-project scheduling problem with multi-skilled staff (RDRCMPSP-MS). The main relation is that RDRCMPSP-MS is also an extension of the DRCMPSP-MS studied in this paper.…”

“…The main relation is that RDRCMPSP-MS is also an extension of the DRCMPSP-MS studied in this paper. Both literature [30] and [29] further consider the uncertainty of activity durations based on the DRCMPSP-MS. This paper and literature [30] studied the different problems of DRCMPSP-MS in deterministic environments and uncertain environments, respectively.…”

“…Both literature [30] and [29] further consider the uncertainty of activity durations based on the DRCMPSP-MS. This paper and literature [30] studied the different problems of DRCMPSP-MS in deterministic environments and uncertain environments, respectively. The main differences between literature [30] and [29] are that literature [30] employs the robust project scheduling to tackle activity duration uncertainty, generating robust baseline scheduling plans without considering that the durations obey a specific probability distribution.…”

“…This paper and literature [30] studied the different problems of DRCMPSP-MS in deterministic environments and uncertain environments, respectively. The main differences between literature [30] and [29] are that literature [30] employs the robust project scheduling to tackle activity duration uncertainty, generating robust baseline scheduling plans without considering that the durations obey a specific probability distribution. In contrast, literature [29] solves the problem using the stochastic project scheduling, in which activity durations are assumed to obey known distributions and scheduling strategies are obtained instead of baseline schedules.…”

“…In contrast, literature [29] solves the problem using the stochastic project scheduling, in which activity durations are assumed to obey known distributions and scheduling strategies are obtained instead of baseline schedules. The main contribution of literature [30] is using the robust scheduling optimization method combining time buffer addition and robust resource allocation to solve the DRCMPSP-MS under activity duration uncertainty.…”