This study proposes an analytical model and an effective scheme for the periodic broadcast on the control channel in vehicular ad hoc networks (VANETs). An improved Markov model for analysing the performance of the periodic broadcast in VANETs is established. Compared with the traditional two-dimensional Markov chain models, the improvement of our proposed model is achieved by the considerations of the unsaturated traffic conditions with the deterministic message generation at each node, modelled by a discrete-time D/M/1 queue and the control mechanism of freezing the backoff-time counter. In adapting to the change of the vehicle densities, the authors propose to use the dynamic contention window (DCW), instead of the fixed contention window (CW), for the broadcast in the IEEE 802.11p medium access control in VANETs. For a certain vehicle density, a best CW size is chosen to achieve a more effective broadcast. Simulation results show that the proposed DCW-based broadcast performs better than the traditional fixed-CW-size broadcast in terms of the packet collision probability. The results also validate our proposed Markov model and its performance improvement than the scheme without the consideration of freezing the backoff-time counter.

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A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

Wind speed presents a potential seasonal pattern revealed by the self-similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short-term wind speed forecasting accuracy. In this study, a novel method for short-term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.

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