Applying hybrid genetic algorithm to multi-mode resource constrained multi-project scheduling problems

Abstract

Multi-mode resource-constrained multi-project scheduling problems (MMRCMPSP) are cases with a precedence relationship among activities, capacity constraints of different execution modes for activities, and multiple resources for multiple projects. In this study, hybrid genetic algorithm (HGA) and heuristic approach are developed to solve MMRCMPSP problems with the aim of minimizing makespan. The proposed HGA contains eight combinations of four typical priority rules (earliest due date, shortest process time, minimum slack, and maximum total work content) and two heuristic methods (serial and parallel) for MMRCMPSP. A total of 48 instances of related MMRCMPSP are considered from available resources and used as test beds for performance evaluation. Results demonstrate that the proposed HGA with parallel method and minimum slack priority rule outperforms a simple genetic algorithm and three activity-mode priority rule combinations from the recent literature. In addition, the superiority of HGA becomes increasingly significant when problem complexity increases.