Accurate sales forecasting plays an increasingly important role in automobile companies due to fierce market competition. In this article, an econometric model is proposed to analyze the dynamic connections among Chinese automobile sales, typical domestic brand automobile (Chery) sales, and economic variables. Four tests are required before modeling, which include unit root, weak exogeneity, cointegration, and Granger-causality test. The selected economic variables consist of consumer confidence index, steel production, consumer price index, and gasoline price. Monthly is used to empirical analysis and the result shows that there is long-term cointegration relationship between Chinese automobile sales and the endogenous variables. A vector error correction model in econometric based on cointegration is applied to quantify long-term impact of endogenous variables on Chinese automobile sales. Compared with other classical timeseries methods, root mean square error (0.1243) and mean absolute percentage error (10.2015) by vector error correction model for 12-period forecasting are minimal. And the forecasting result is better when the impact of Chery sales is considered. That means that the fluctuation trends of Chinese automobile sales and typical domestic brand automobile sales are closely linked.

In this article, a novel Hybrid method based on particle swarm optimization and ant colony optimization (HPA) is proposed to forecast automobile sales of China. Proposed hybrid model integrating particle swarm optimization and ant colony optimization for estimation of Chinese automobile sales (HPAE) is the first model using aforementioned metaheuristic techniques. HPA is developed for automobile sales forecasting using highway mileage, gross domestic product, automobile ownership, and consumer price index. HPAE is developed in two forms including both Linear (HPAEL) and Quadratic (HPAEQ). Then, in order to show the accuracy of the algorithm, a comparison is made with ant colony optimization and particle swarm optimization. The results show that mean absolute errors of the HPAE model are the best among them, and better-fit solutions are provided by quadratic form (HPAEQ) due to fluctuations of the influential indicators in China.

The mode of automotive maintenance chain is gaining more and more attention in China because of its advantages such as the lower cost, higher speed, higher availability, and strong adaptability. Since the automobile chain maintenance parts delivery problem is a very complex multi-depot vehicle routing problem with time windows, a virtual center depot is assumed and adopted to transfer multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot, which is similar to a vehicle routing problem with time windows. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy is presented for the multi-depot vehicle routing problem with the virtual central depot. At last, the computational results for benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem with time windows. Furthermore, the results of an automobile chain maintenance parts delivery problem also indicate that the improved ant colony algorithm is feasible for solving this kind of delivery problem.

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