Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise
Computers and Operations Research
Prioritization and operations NPD mix in a network with strategic partners under uncertainty
Expert Systems with Applications: An International Journal
A group recommendation system with consideration of interactions among group members
Expert Systems with Applications: An International Journal
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Bilateral Collaboration and the Emergence of Innovation Networks
Management Science
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Grouping and partner selection in cooperative wireless networks
IEEE Journal on Selected Areas in Communications
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
An integrated method for collaborative R&D project selection: Supporting innovative research teams
Expert Systems with Applications: An International Journal
Classification by clustering decision tree-like classifier based on adjusted clusters
Expert Systems with Applications: An International Journal
Multi-objective team formation optimization for new product development
Computers and Industrial Engineering
Hi-index | 12.06 |
The member selection is an important decision problem in the formation of R&D teams. Selecting suitable members will facilitate the success of R&D projects. In the existing methods for partner selection, the individual information to measure the individual performance of members is mostly used, while the collaborative information to measure the collaborative performance between members is seldom considered. Therefore, this paper proposes a method for member selection of R&D teams, in which both the individual information of members and the collaborative information between members are considered. In order to select desired members, a bi-objective 0-1 programming model is built using the individual and collaborative information. To solve the model, a multi-objective genetic algorithm is developed since the model is NP-hard. A practical example followed by simulation experiment is used to illustrate the applicability of the proposed method. Additionally, the experimental results show that the proposed method can support satisfactory and high quality member selection.