A Hybrid Genetic Algorithms and Sequential Simulated Annealing for a Constrained Personal Reassignment Problem to Preferred Posts

Abstract

This paper discusses the implementation of a new hybrid algorithm to efficacy solve a constrained personnel reassignment to preferred posts by generating a high quality and optimal solution. This optimization of posts can be occupied by a qualified employee during a job rotation or redeployment (reassignment) staff operation. This operation is organized by a decision-maker to adapt each post in priority to qualified employees for improving the productivity of enterprise. Generally, this problem is a NP hard problem that has been modeled into a combinatorial optimization problem. Next we have developed a hybrid graph Flow and genetic algorithm called HFGA, to solve it [1]. However, we have noticed that the stagnation phenomenon of this HFGA algorithm is usually persisted producing an increase in the number of iterations and consequently also a significant consummation of CPU time. In order to remedy this problem and improving in parallel the optimal solution and convergence rate, we propose as our first contribution a hybrid genetic HFGA and Adaptive Genetic Immigration AIG operator [2] called HFGA-AIG. In our second contribution, we propose another hybridization of HFGA-AIG with a Simulated Annealing SA sequentially called Hybrid Genetic Simulated Annealing Algorithms HFGA-SA-AIG. Our aim is to obtain an improved solution quality of this NP Hard problem. This proposed algorithm can be adapted to a personnel assignment optimization model and allows us also to ensure an excellent allocation of resources for maximizing the productivity in a multisite entreprise. The obtained numerical results indicate that the HFGA-SA-AIG algorithm outperforms both HFGA-AIG and HFGA. In terms of solution quality and remedying of the stagnation phenomenon.

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