Two Heuristic Algorithms for a Multi-Mode Resource-Constrained Multi-Project Scheduling Problem

Many studies on the resource-constrained project-scheduling problem have been published, but literature further considering multi-mode or multi-project issues often occurring in the real world is rather scarce. In this research, two heuristic algorithms are developed to solve a multi-mode resource-constrained multi-project scheduling problem (MMRCMPSP). The first, a parallel scheduling algorithm (PSA), includes a combination of an activity- and a mode-priority rule; the second is a genetic algorithm (GA). The solutions obtained by the former algorithm with the best activity- and mode-priority rule combination are used as a baseline to compare those obtained by the latter. On four sets of test problems, twenty combinations of rules are compared to determine the best one for deriving the most likely best solution in the proposed PSA. In each set, two resource-availability levels and two due-date levels are considered. Due to certain priority rules’ having probabilistic factors, a two-stage sampling method is introduced to ensure that more reliable computational results are obtained. Finally, the solutions obtained by the proposed PSA having the best activity- and mode- priority rule combination are compared on the test problems with those obtained by the proposed GA. Finally, conclusions are drawn from the computational results.