This study utilizes a time-precedence network technique to construct two models of multi-mode resource constrained project scheduling problem with discounted cash flows (MRCPSPDCF), individually including the progre...This study utilizes a time-precedence network technique to construct two models of multi-mode resource constrained project scheduling problem with discounted cash flows (MRCPSPDCF), individually including the progress payment (PP) and the payment at an equal time interval (ETI). The objective of each model is to maximize the net present value (NPV) for all cash flows in the project, subject to the related operational constraints. The models are characterized as NP-hard. A heuristic algorithm, coupled with two upper bound solutions, is proposed to efficiently solve the models and evaluate the heuristic algorithm performance which was not performed in past studies. The results show that the performance of proposed models and heuristic algorithm is good.展开更多
基金教育部博士点基金资助项目(20120073110029)国家自然科学基金资助项目(71371123)+2 种基金国家自然科学基金重点项目(71131005)上海交通大学文理交叉重点项目(11JCZ02)Europe-China High Value Engineering Network(EC-HVEN:295130)
基金教育部博士点基金资助项目(2012007311OO29)国家自然科学基金重点项目(71632008)+3 种基金国家自然科学基金资助项目(71371123)国家自然科学基金重点资助项目(71131005)上海交通大学文理交叉重点资助项目(11JCZ02)Europe-China High Value Engineering Network(Ec HVEN:295130)
文摘This study utilizes a time-precedence network technique to construct two models of multi-mode resource constrained project scheduling problem with discounted cash flows (MRCPSPDCF), individually including the progress payment (PP) and the payment at an equal time interval (ETI). The objective of each model is to maximize the net present value (NPV) for all cash flows in the project, subject to the related operational constraints. The models are characterized as NP-hard. A heuristic algorithm, coupled with two upper bound solutions, is proposed to efficiently solve the models and evaluate the heuristic algorithm performance which was not performed in past studies. The results show that the performance of proposed models and heuristic algorithm is good.