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In this study, we propose a random-key and cuckoo search (CS) based approach to solve the CLLP.
Due to the NP-hard nature of the model, we propose a genetic algorithm based approach to solve the problem.
Secondly, we employ an ordinal optimization (OO) based approach to solve the maximizing reliability problem and a bisection method to solve the minimizing budget problem.
The activities documented in "Gesamtverkehrsplan für Österreich 2013" show a shift towards a more scientific based approach to solve the transport related challenges – but it is too early to assess the outcome of this process at present.
We developed the notion of working patterns that can fulfill the requirements with generic transmission energy consumption model and variable communication ranges and sensing ranges, and adopted a column generation based approach to solve the problem where a column corresponds to a feasible working pattern, with initial working patterns being obtained through either a random selection algorithm.
Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 min. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan.
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The purpose of this paper is to explore bi-level genetic algorithm (GA) based approach to solving the optimal toll locations and toll levels simultaneously in a multiclass network.
Machine learning (ML) is based on the way the human brain approaches pattern recognition tasks, providing an artificial intelligence-based approach to solve classification problems.
A DE-based approach to solve the OPF problem was developed in [47].
An improved WP-based approach to solve the problems of speech enhancement has been presented in this paper.
It is a significant and efficient market-based approach to solve the allocation problem with more requisitions.
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