Title
An efficient genetic algorithm to maximize net present value of project payments under inflation and bonus-penalty policy in resource investment problem
Abstract
In order to develop a more realistic resource-constrained project-scheduling model that is applicable to real-world projects, in this paper, the resource investment problem with discounted cash flows and generalized precedence relations is investigated under inflation factor such that a bonus-penalty structure at the deadline of the project is imposed to force the project not to be finished beyond the deadline. The goal is to find activity schedules and resource requirement levels that maximize the net present value of the project cash flows. The problem is first mathematically modeled. Then, a genetic algorithm (GA) is designed using a new three-stage process that utilizes design of experiments and response surface methodology. The results of the performance analysis of the proposed methodology show an effective solution approach to the problem.
Year
DOI
Venue
2010
10.1016/j.advengsoft.2010.03.002
Advances in Engineering Software
Keywords
Field
DocType
project payment,discounted cash flows,efficient genetic algorithm,bonus-penalty structure,present value,bonus-penalty policy,bonus–penalty structure,inflation,response surface methodology,generalized precedence relation,effective solution approach,activity schedule,genetic algorithm,proposed methodology,resource investment problem,project scheduling,resource requirement level,project cash flow,discounted cash flow,mathematical model,design of experiment,cash flow,net present value
Present value,Terminal value,Mathematical optimization,Schedule (project management),Computer science,Schedule,Net present value,Intrinsic value (finance),Present value of costs,Cash flow
Journal
Volume
Issue
ISSN
41
7-8
Advances in Engineering Software
Citations 
PageRank 
References 
9
0.55
6
Authors
5