On Nonredundant Cost-Constrained Team Formation

On Nonredundant Cost-Constrained Team Formation

Yu Zhou (School of Computer Science and Technology and School of Software, Xidian University, Xi'an, China), Jianbin Huang (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China & School of Software, Xidian University, Xi'an, China), Heli Sun (Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China) and Xiaolin Jia (Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/IJDWM.2017070102
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Abstract

Due to the wide application of the task assignment on the internet, team formation problem has become an important research issue. A recently proposed problem ClusterHire aims to find a team of experts to accomplish multiple projects which can harvest a maximum profit under a limited budget. However, there exist redundancies in the team yielded by existing algorithms. This paper first studies the properties of the problem, and give two pruning strategies based on them. Secondly, a redundancy-eliminating strategy and a team-augmenting strategy are proposed. In addition, a new algorithm for generating a profit-maximizing team is also proposed. It is based on the redundancy-eliminating and team-augmenting strategies. The experimental evaluations show that our proposed strategies and algorithms are effective.
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1. Introduction

The team formation and the task assignment on the internet are related to each other. They can be applied to many areas, e.g. online labor market, social activity organization. Recently, a variant of team formation problem, called ClusterHire (Golshan & Terzi, 2014), proposed to form a cost-constrained profit-maximizing team. Given a set of experts, a set of skills and a set of projects, each expert possessed some skills, and each project required some skills. Each expert was associated with a compensation demand, and each project was associated with an expected profit. The goal was to find a team of experts whose cost did not exceed the given budget, and maximize the total profit of projects. In the real world, the online labor market like www.guru.com) now offer team-hiring services to their enterprise customers. Some companies could outsource the projects in his hand, which they would not devote themselves to, to some teams with fixed payment if they accept this service. Note in particular that these projects are relatively small, and not their core business. Thereby, they hire some teams to complete these projects with fixed payment. For the hired team consisting of freelancers with complementary skillsets, everyone gets payment according to his/her amount of work. In consideration that the project is relatively small, it is not necessary to hire the experts with redundant skillset. In addition, they also do not wish to hire a redundant employee to join them, because the payment obtained by them will decrease accordingly. On side of freelancers, some freelancers with complementary skillsets in online labor markets wish to unite up to pursue a larger profit. They cooperate remotely on some projects, and respectively gain payment according to their respective amounts of work. Obviously, redundant freelancers will decrease the payment of other freelancers more or less. Under these scenarios, the redundant experts are invaluable to the team, and therefore eliminating them has no influence on the projects. (II) Article (Golshan & Terzi, 2014) only provided three heuristic algorithms, but did not study the properties of ClusterHire. That is to say, the authors did not consider whether there were effective pruning strategies based on the properties of ClusterHire.

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