Abstract
In logistics and transportation, it is usually necessary to take into account the distance, route, time, transportation cost, and human resources. In this paper, the coordinates of 31 cities are used as data storage, and the three parts of an ordinary distance, travel time, and high-speed cost between the two places are added with target weights to obtain the minimum comprehensive cost. In this paper, the shortest path at the core of the TSP is transformed into the minimum comprehensive cost, and each cost in transportation is weighted in proportion to the target. On the premise of finding the shortest path, to solve the logistics transportation planning problem in the business travel problem, it will simply find the minimum path Single objective function optimization is to add objective weights to achieve the objective function with the smallest comprehensive cost and optimize the algorithm. While comprehensively considering cost and time, it avoids the inoperability caused by the linear distance between two points, reduces logistics transportation costs, improves logistics timeliness, and improves customer satisfaction.
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We gratefully acknowledge anonymous reviewers who read drafts and made many helpful suggestions.
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This work is supported by Higher Education Department of the Ministry of Education Industry-university Cooperative Education Project.
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Chen, J., Chen, F., Guo, C., Sun, Y. (2021). Research on Business Travel Problem Based on Simulated Annealing and Tabu Search Algorithms. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_60
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DOI: https://doi.org/10.1007/978-3-030-78609-0_60
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