Heuristic method for automakers' technological strategy making towards fuel economy regulations based on genetic algorithm: A China's case under corporate average fuel consumption regulation
Heuristic method for automakers' technological strategy making towards fuel economy regulations based on genetic algorithm: A China's case under corporate average fuel consumption regulation
复制标题
基于遗传算法的汽车企业燃油经济性监管技术策略制定的启发式方法——以企业平均油耗监管下的中国案例
DOI：
10.1016/j.apenergy.2017.07.076
复制
发表时间：
2017-10-15
期刊：
影响因子：
11.2
通讯作者：
Hao, Han
中科院分区：
文献类型：
--
作者：
Wang, Sinan;Zhao, Fuquan;Hao, Han
汽车燃油经济性标准已在全球范围内实施。然而，汽车制造商很难获得符合这些强化标准的最佳节油技术组合，同时将总体成本降至最低。本文提出了一种基于遗传算法的启发式技术战略规划方法。特别介绍了中国企业平均燃油经济性标准的案例研究。此外，还综合考虑了技术成本、降低油耗效果和技术物理重量等因素构建了数学模型。问题复杂性被分析并证明是NP-hard的。此外，对详细的遗传算法和目前大多数汽车制造商在确定中国技术策略时使用的贪婪算法进行了性能比较分析。结果表明遗传算法优于普通方法，因为它提供了更经济合理的策略。此外，贪心算法下的增量成本比遗传算法下高出16.4%。由于中国基于重量的标准的反作用，与现有策略相比，减重技术的优先级应较低。为了到 2020 年满足这些标准，汽车制造商应该采用更多传统的发动机和变速箱技术，而不是混合动力电动汽车技术。建议汽车制造商开发启发式算法，更合理地做出战略决策。
The vehicle fuel economy standards have been implemented worldwide. However, it is quite difficult for the automakers to secure an optimal portfolio of fuel-efficient technologies which complies with these strengthened standards and minimizes the overall cost at the same time. In this paper, a genetic-algorithm-based heuristic method is proposed for technological strategy planning. In particular, a case study of the Corporate Average Fuel Economy standards in China is presented. Moreover, the mathematical model is constructed with the considerations of the technology cost, effect of reducing fuel consumption and technology physical weight. Problem complexity is analyzed and proven NP-hard. Moreover, a comparison analysis of performance is carried out between the elaborated genetic algorithm and the greedy algorithm that is currently used by most automakers to determine the technological strategies in China. The results imply that genetic algorithm outperforms the common method because it provides more economical and reasonable strategies. In addition, the incremental cost under the greedy algorithm is 16.4% higher than that under genetic algorithm. Due to the counteractive effect under the weight-based standards in China, the mass reduction technologies should be given lower priorities compared with current strategies. To satisfy the standards by 2020, automakers should implement more conventional engine and transmission technologies instead of the hybrid electric vehicle technologies. It is recommended that automakers should develop heuristic algorithms to make strategic decisions more reasonably.