Bicriteria transportation problem by hybrid genetic algorithm
Proceedings of the 23rd international conference on on Computers and industrial engineering
Optimal testing-resource allocation with genetic algorithm for modular software systems
Journal of Systems and Software
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Stability analysis of network-based cooperative resource allocation strategies
Automatica (Journal of IFAC)
Expert Systems with Applications: An International Journal
A genetic algorithm for maximum-weighted tree matching problem
Applied Soft Computing
A rough set based approach to patent development with the consideration of resource allocation
Expert Systems with Applications: An International Journal
An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Variable neighborhood search for multi-objective resource allocation problems
Robotics and Computer-Integrated Manufacturing
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.