The Beijing-Tianjin-Hebei region is one of the regions with the most dynamic economies, highest degrees of openness, strongest innovation capacities and largest populations, as well as an important engine driving China’s economic development. Currently, the development of the Beijing-Tianjin-Hebei region faces many difficulties, including an inefficient distribution of regional functions, imbalance in industrial structure, low sophistication, and serious overburdening of resources and the environment. Promoting the overall coordinated development of the region has become an urgent challenge. In this paper, according to the conventions of industrial structure optimization, the three core indicators of regional industrial structure rationalization, sophistication and ecologicalization are constructed. With the help of the grey dynamic correlation model, the three indicators are coupled with the economic growth rate, and the industrial structure optimization index of the Beijing-Tianjin-Hebei region is comprehensively measured. The study shows that rationalization of the industrial structure in the Beijing-Tianjin-Hebei region is increasing but that this trend is not obvious; sophistication of the industrial structure is relatively high and has also been operating at a high level; however, the overall ecological level of the industrial structure in the Beijing-Tianjin-Hebei region is not high and presents the characteristics of high in the middle and low at both ends. In terms of the relationship between industrial structure and economic growth in the Beijing-Tianjin-Hebei region, ecologicalization of the industrial structure has the greatest influence on economic growth, followed by sophistication of the industrial structure, and rationalization of the industrial structure has the weakest influence.
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Truck and trailer routing problem (TTRP) is one of the most frequently encountered problem in city distribution, particularly in populated and intensive downtown. This paper addresses this problem and designs a novel backtracking search algorithm (BSA) based meta-heuristics to solve it. The initial population is created by T-sweep heuristic and then based on the framework of backtracking search algorithm, four types of route improvement strategies are used as building blocks to improve the solutions of BSA in the process of mutation and crossover. The computational experiments and results indicate that the proposed BSA algorithm can provide an effective approach to generate high-quality solutions within the satisfactory computational time.
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