Multi-skilled resource-constrained multi-project scheduling problem with dexterity improvement of workforce

Abstract

In this study, a mixed-integer mathematical model has been presented for the Multi-Skilled Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where the dexterity of workers can be improved via two ways: (1) Cooperating with more proficient co-workers, and (2) Practicing the possessed skills. The model aims to determine the start and finish times of activities considering the dexterity improvement of workforces. This research presents a Modified discrete variant of the Biogeography-Based Optimization (MBBO) algorithm to solve the model and to minimize the total required duration to complete all projects. The MBBO embraces two novel migration procedures, a new mutation process, and a new habitat selection operator. On several test instances and on a case study, the efficacy of the MBBO has been put to examination in comparison to four other meta-heuristics. The MBBO has overpowered other methodologies in terms of most of the assessment criteria. The obtained results have revealed that the dexterity improvement of workers significantly improves the total required duration to complete all projects. This research and its outputs can be helpful for the researchers who work on project scheduling models. Moreover, this study can inspire future project scheduling formulations, where the dexterity of human resources is affected by the learning phenomenon.