In this paper, we consider the multiple traveling salesman problem (MTSP) with the minmax objective, which includes more than one salesman to serve a set of cities while minimizing the maximum distance traveled by any salesman. For this problem, we have proposed a novel memetic algorithm, which integrates with a sequential variable neighborhood descent that is a powerful local search procedure to exhaustively search the areas near the high-quality solutions. However, there are some inefficient neighborhoods in the existing sequential variable neighborhood descent for the minmax MTSP, which could restrict the search performance. Therefore, we have redefined a new neighborhood sequence where only the neighborhoods that move cities from one tour to another unidirectionally are considered. Computational experiments on a wide range of benchmark problems within an acceptable time limit show that compared with six existing algorithms, the proposed algorithm is better than the other algorithms in terms of three aspects, including the precision, the robustness and the convergence speed. Meanwhile, we have also investigated the total distance traveled by all the salesmen when optimizing the minmax objective, and the results show that in comparison with the six existing algorithms, the proposed algorithm has a better or at least competitive capacity to maintain the total distance as short as possible. Furthermore, two kinds of statistical tests are utilized to examine the significance of the presented results, indicating the superiority of the proposed algorithm over the other algorithms on the minmax objective.
The rapid development of internet-based ride-hailing services has contributed to transportation in cities and, at the same time, has significantly impacted existing travel modes in cities. A question has emerged as to whether and to what extent ride-hailing services replace private car use. Although the private car is convenient, comfortable, and flexible, it has low utilization rate and high maintenance and parking costs. Better understanding of the relationship between ride-hailing services and the use of private cars has been brought to the forefront for auto dealers and urban transportation policymakers. However, controversies remain regarding how ride-hailing services will impact the use of private cars in cities. Given this setting, our study applied a difference-in-differences method to analyze the impact of ride-hailing services on the use of private cars with balanced panel data from 109 prefecture-level cities in China from 2010 to 2016. Moreover, we employed some methods to verify the robustness of the preliminary results. The empirical results show that ride-hailing services had a negative impact on the use of private cars in urban areas. Over time, the negative impact initially strengthened and then weakened. Further studies showed that ride-hailing services had a more significant negative impact on private car use in eastern cities than in western cities. The results showed that the influence of ride-hailing services on private car use in urban areas is heterogeneous across time and cities.
Based on previous research on open innovation and appropriability strategies, using knowledge production functions and evolutionary game methods, this paper describes the process of dynamic cooperation between open innovation platforms and their participants. This paper specifically analyzes the influence of open innovation platform’s knowledge appropriability/knowledge sharing strategies, as well as participants’ exit/nonexit strategy, on the cooperative relationship. Through simulation analysis, this paper draws the following conclusions: first, the knowledge appropriability strategy of the open innovation platform and the participant’s nonexit strategy is an important strategic point of the cooperation between open innovation platforms and participants; second, the amount of knowledge production affects the strategic choices of open innovation platforms, while the knowledge increment affects the strategic choices of participants; third, the appreciation coefficient of complementary assets determines the direction of evolution of the cooperation process.
Currently, opinions on whether and how ride-hailing services have impacted the use of traditional taxis are divided, and no consensus has been reached. This paper used panel data from 44 cities in China from 2010 to 2016 to estimate the impact of ride-hailing services on the use of traditional taxis by applying the multiperiod difference-in-difference (multiperiod DID) method and conducting a series of robustness tests. The results show that ride-hailing services have a large, negative impact on the use of traditional taxis, and this negative impact is more severe in eastern cities. These results suggest that in China it is necessary for traditional taxi operators to make changes and innovations to become more sustainable in response to pressure from ride-hailing services. Additionally, it is suggested that city governments in China pay greater attention to the impact of emerging ride-hailing services on traditional taxi services, embrace emerging modes of mobility, and create appropriate policies to coordinate the development of both new and traditional services.
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