Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem

Abstract

Featured Application This study compares the existing heuristic rules using small/large networks, different resource types, relaxed/limited resource supply, and serial/parallel resource allocation procedures. Resource-constrained project scheduling (RCPS) aims to minimize project duration under limited resource availabilities. The heuristic methods that are often used to solve the RCPS problem make use of different priority rules. The comparative merits of different priority rules have not been discussed in the literature in sufficient detail. This study is a response to this research gap. It compares 17 heuristic priority rules and seeks the best performing heuristic priority rule. This is the first study ever that compares heuristic priority rules by considering combinations of variations in (1) resource allocation procedures, (2) number of activities, (3) number of resource constraints, and (4) resource supply levels. The objective is to understand the relative merits of heuristic rules used in solving the RCPS problem. The findings indicate that the "minimum late finish time" rule generates the shortest predicted project duration when used in parallel resource allocation, whereas the "minimum late start time", "minimum late finish time", and the "highest rank of positional weight 2" rules perform best in serial resource allocation. It was also found that parallel resource allocation is slightly superior to serial resource allocation in most instances.