A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

• Published in: Computers & operations research Vol. 36; no. 11; pp. 2994 - 3001
• Main Authors: Najafi, Amir Abbas, Niaki, Seyed Taghi Akhavan, Shahsavar, Moslem
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2009; Elsevier; Pergamon Press Inc
• Subjects: Applied sciences; Cash flow; Design of experiments; Discounted cash flows; Exact sciences and technology; Experimental design; Generalized precedence relations; Genetic algorithms; Mathematics; Net present value; Operational research and scientific management; Operational research. Management science; Performance evaluation; Probability and statistics; Project management; Project scheduling; Response surface methodology; Scheduling, sequencing; Sciences and techniques of general use; Statistics; Studies; Generalized precedence relations; Project scheduling; Discounted cash flows; Response surface methodology; Net present value; Genetic algorithms; Availability; Project management; Cash flow; Scheduling; Cash discount; Modeling; Treasury assets; Genetic algorithm; Experimental design; Response surface; Investment
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2009.01.016

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.