The Heuristic Procedure for Resource Constrained Proposal Project Portfolio Problem

2021 ◽  
Vol 22 (1) ◽  
pp. 135-159
Author(s):  
Yoon Jae Cho ◽  
In Soub Paek ◽  
Jong Chool Kim ◽  
Tae Ho Ahn
Keyword(s):  
Project Portfolio ◽  
Heuristic Procedure ◽  
Resource Constrained ◽  
Portfolio Problem

Multi-mode resource constrained multi-project scheduling and resource portfolio problem

2015 ◽  
Vol 240 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Umut Beşikci ◽  
Ümit Bilge ◽  
Gündüz Ulusoy
Keyword(s):  
Project Scheduling ◽  
Resource Constrained ◽  
Portfolio Problem ◽  
Multi Mode

TOPSIS-Grey Method Applied to Project Portfolio Problem

2016 ◽  
pp. 767-774 ◽  
Author(s):  
Fausto Balderas ◽  
Eduardo Fernandez ◽  
Claudia Gomez ◽  
Laura Cruz-Reyes ◽  
Nelson Rangel V
Keyword(s):  
Project Portfolio ◽  
Portfolio Problem

scholarly journals A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yongyi Shou ◽  
Wenwen Xiang ◽  
Ying Li ◽  
Weijian Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Portfolio Selection ◽  
Level Structure ◽  
Project Portfolio ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Project Portfolio Selection ◽  
Multiproject Scheduling ◽  
Upper Level ◽  
Self Learning

A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.

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