| 考虑成本约束的在线旅行社后向整合策略研究 |
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| 引用本文： | 叶锦龙,鲁 芳,王 芹.考虑成本约束的在线旅行社后向整合策略研究[J].湖南工业大学学报,2023,37(2):90-96. |
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| 作者姓名： | 叶锦龙 鲁 芳 王 芹 |
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| 作者单位： | 中南林业科技大学 物流与交通学院 |
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| 基金项目： | 湖南省自然科学基金资助项目（2018JJ3131） |
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| 摘 要： | 为提高旅游服务的竞争力，在线旅行社通过后向整合旅游服务集成商的方式提高旅游服务质量，但自建和并购两种后向整合策略存在成本差异，如何在多个旅游目的地实现旅游资源整合程度的最大化是在线旅行社亟需解决的问题。通过构建考虑成本约束的在线旅行社后向整合模型，设计了基于爬山算法的改进遗传算法求解模型，数值仿真分析了不同成本约束下在线旅行社在每个旅游目的地的整合策略。研究结果表明：在线旅行社获得的边际收益随着投入整合成本的增大而减小，因此在线旅行社应确定成本投入，避免资金浪费；改进的遗传算法比传统遗传算法有更好的寻优性能。
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| 关 键 词： | 在线旅行社 成本约束 纵向整合策略 改进遗传算法 |
| 收稿时间： | 2022/3/11 0:00:00 |
A Cost-Wise Research on the Backward Integration Strategy of Online Travel Agency |
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| YE Jinlong,LU Fang,WANG Qin.A Cost-Wise Research on the Backward Integration Strategy of Online Travel Agency[J].Journal of Hnnnan University of Technology,2023,37(2):90-96. |
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| Authors: | YE Jinlong LU Fang WANG Qin |
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| Abstract: | In view of an improvement of the tourism service competitiveness, the backward integration of tourism service integrators has been adopted by online travel agencies for improving tourism service quality. However, due to the cost disparity between the two backward integration strategies of self construction and mergers and acquisitions (M&A), the way for a maximization of the integration of tourism resources in multiple tourism destinations becomes an urgent issue for online travel agencies. By constructing a backward integration model of online travel agencies with cost constraints taken into consideration, an improved genetic algorithm solution model is designed on the basis of the hill climbing algorithm, followed by an analysis of the integration strategies of online travel agencies in each tourism destination under different cost constraints by a numerical simulation. The results show that the marginal revenue of online travel agencies decreases with the increase of input integration cost, which makes it necessary for online travel agencies to determine the cost input so as to avoid the capital waste; the improved genetic algorithm is characterized with a better optimization performance than the traditional genetic algorithm. |
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