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

Authors:
Amir Abbas Najafi;Seyed Taghi Akhavan Niaki;Moslem Shahsavar
Affiliations:
Department of Industrial Engineering, Qazvin Islamic Azad University, Nokhbegan Avenue, Qazvin, Iran;Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran;Department of Industrial Engineering, Qazvin Islamic Azad University, Nokhbegan Avenue, Qazvin, Iran
Venue:
Computers and Operations Research
Year:
2009

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Abstract

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.