부산대학교 도서관

Project resource leveling, which is usually performed in the project planning phase, aims to minimize fluctuations in resource usage such that the project success probability is enhanced. The existing studies on resource leveling mainly assume a fixed project structure, i.e., the activities and precedence relationships are given in advance. However, in real-world projects, the project structure is usually flexible, i.e., not all activities are implemented, and the precedence relationships exist only between the activities that are implemented. Aiming at providing support for project managers to make effective resource leveling decisions when facing flexible project structures, we study the resource leveling problem with flexible structures (RLP-PS). We present a nonlinear integer programming model for the RLP-PS and a linearization method to transform the nonlinear model into a linear model. To efficiently solve the RLP-PS, we devise two problem-specific algorithms: a two-stage heuristic algorithm and a customized genetic algorithm. Based on the PSPLIB benchmark dataset, extensive computational experiments are performed to analyze the performances of our algorithms. The experimental results reveal the effectiveness and competitiveness of our algorithms.