Fuzzy based Quantum Genetic Algorithm for Project Team Formation

Fuzzy based Quantum Genetic Algorithm for Project Team Formation

Arish Pitchai (National Institute of Technology Tiruchirappalli, Tiruchirappalli, India), Reddy A. V. (National Institute of Technology Tiruchirappalli, Tiruchirappalli, India) and Nickolas Savarimuthu (National Institute of Technology Tiruchirappalli, Tiruchirappalli, India)
Copyright: © 2016 |Pages: 16
DOI: 10.4018/IJIIT.2016010102
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Abstract

Formation of an effective project team plays an important role in successful completion of the projects in organizations. As the computation involved in this task grows exponentially with the growth in the size of personnel, manual implementation is of no use. Decision support systems (DSS) developed by specialized consultants help large organizations in personnel selection process. Since, the given problem can be modelled as a combinatorial optimization problem, Genetic Algorithmic approach is preferred in building the decision making software. Fuzzy descriptors are being used to facilitate the flexible requirement specifications that indicates required team member skills. The Quantum Walk based Genetic Algorithm (QWGA) is proposed in this paper to identify near optimal teams that optimizes the fuzzy criteria obtained from the initial team requirements. Efficiency of the proposed design is tested on a variety of artificially constructed instances. The results prove that the proposed optimization algorithm is practical and effective.
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Introduction

Personnel selection is a challenging multi-criteria problem in most of the large organizations. Software support for project team formation process plays a vital role in the success of an organization. Effective project management starts from selecting the right person from the pool of employees having the required skills. Plenty of software for automated project management is available in the market for software planning, estimation and control modules. Project managers are required to consider both qualitative as well as quantitative attributes in the team formation process. Effective formation of collaborative teams with conflicting objectives is still an open problem and it attracts extensive research in various fields of business, sports and social studies (Cheatham, & Cleereman, 2006; Tavana, Azizi, Azizi, & Behzadian, 2013).

In small-sized organizations with a reasonable number of teams, manual implementation is possible by a person with sufficient knowledge of the employees’ skills and their present project assignments. In the case of medium or large-sized organizations, project team formation and its management becomes a difficult task, because of the number of simultaneously active projects. Software support for decision making is very crucial in such enterprises, as failure in optimal team formation may lead to decrease in service quality, unfinishable project deadlines and loss of credibility of the organization. Therefore, an effective technique is required to model the qualitative and quantitative requirements of the problem. At the same time, an efficient search tool is needed to identify the combination of employees that makes an optimal team.

The problem of team formation can be seen as a combinatorial optimization problem. Aim of the given problem is to select an optimal team based on the requested attributes of the team members. Characteristics of employees are denoted with different terms like skills, attributes, capabilities, or properties in the literature. Having combinatorial optimization model as the core of a decision support system is strongly suggested to improve the quality of the selected team (Boon, & Sierksma, 2003). Evolutionary approach shows promising results when applied on the optimization model of the given problem (Strnad, & Guid, 2010).

Qualitative capabilities are considered as an important factor for decision making in the previous years. But, inclination towards the consideration of quantifiable data in the process of team formation is increasing nowadays. One of the early approach using the quantifiable attributes of employees made use of data mining technique to extract their expertise (Rodrigues, Oliveira, & De Souza, 2005). Data from employee documents were mined in order to judge their competences. First analytical approach to build multi-functional teams based on the customer requirements was suggested by Zzkarian & Kusiak (1999). They used Analytical Hierarchy Process (AHP) approach to rank the team members and Quality Function Deployment (QFD) planning matrix to organize the attributes considered in the team formation.

Chen, & Lin (2004) proposed a similar approach using Analytical Hierarchy Processing for the formation of a multi-functional team. Multi-functional knowledge, team capability and collegiality of the team members were the three fundamental descriptors taken into account to build a working relationship model. Myers-Briggs type indicator is used to measure the abilities of each individual to cooperatively work with their colleagues. Another analytical approach mathematically formulated the given problem based on the set of labor attribute pools divided into disjoint groups (Fitzpatrick, & Askin, 2005). Their main aim is to restructure the organization from non-cellular to cellular manufacturing trend. Dedication of the workers towards their task is considered as the key element in constructing successful cells. To handle the growing computational complexity, heuristic method based on Kolbe measures was proposed in their approach. Since, the members were selected based on a single measure, this approach is not preferable for dynamic team selection.

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