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.
Subgame Perfect Equilibrium (SGPE) is a refined version of Nash equilibrium used in games of sequential nature. Computational complexity of classical approaches to compute SGPE grows exponentially with the increase in height of the game tree. In this paper, we present a quantum algorithm based on discrete-time quantum walk to compute Subgame Perfect Equilibrium (SGPE) in a finite two-player sequential game. A fullwidth game tree of average branching factor b and height h has ()O n b oracle queries to backtrack to the solution. The resultant speed-up is () Ob times better than the best known classical approach, Zermelo's algorithm.
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