Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms

Abstract

Repetitive project scheduling is a frequently encountered and challenging task in project planning. Researchers have developed numerous methods for the scheduling and planning of repetitive construction projects. However, almost all current repetitive scheduling methods are based on identical production units or they neglect the priorities of activities. This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). In FABC, the fuzzy c-means clustering technique applies several multi-parent crossover operators to utilize population information efficiently and to improve convergence efficiency. The scheduling subsystem considers the following: (1) the logical relationships among activities throughout the project; (2) the assignment of multiple resources; and (3) the priorities of activities in groups to calculate project duration. Two numerical case studies are analyzed to demonstrate the use of the FABC-NRP model and its ability to optimize the scheduling of non-unit repetitive construction projects. Experimental results indicate that the proposed method yields the shortest project duration on average and deviation of optimal solution among benchmark algorithms considered herein and those considered previously. The outcomes will help project managers to prepare better schedules of repetitive projects.