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Received:May 11, 2024 Revised:June 28, 2024
Received:May 11, 2024 Revised:June 28, 2024
中文摘要: 针对当前众包平台面临的订单类型多样性(外卖订单与快递订单)和配送骑手的同质化(单一外卖型与单一快递型)问题, 且现有众包配送机制较少兼顾商家和顾客满意度, 在派单模式下考虑骑手的异质性, 通过引入全能型骑手, 将骑手划分为单一外卖型、单一快递型和全能型3类, 根据各类骑手可服务的订单类型差异, 构建基于模糊时间窗的商家和顾客对于骑手到达取送货点时间的满意度成本函数, 将商家和顾客的满意度转化为时间惩罚函数, 建立了以时间惩罚成本、路径行驶成本、人员运营成本最小化为目标的模型, 针对模型的特点以及传统算法存在的问题, 设计将遗传算法与大规模领域搜索算法相结合的混合算法, 随后通过具体算例, 采用模拟退火算法、遗传算法和混合算法分别进行求解, 通过不同算法的优化结果对比分析, 验证模型和改进算法的可行性和有效性. 实验结果表明: 在众包配送过程中合理考虑骑手异质性以及商家和顾客的满意度不仅能够有效提升商家和顾客满意度, 也能够降低众包平台配送成本和提高配送效率, 对于众包平台制定配送策略具有一定的参考意义.
Abstract:In crowdsourcing platforms, orders have different types (takeaway and express orders), while delivery riders are typically responsible for only one type of order (either takeaway or express delivery). Additionally, the existing delivery mechanism rarely meets the satisfaction of merchants and customers. Therefore, considering the heterogeneity of riders in a dispatch mode, this study introduces the concept of all-round riders, dividing riders into three categories: takeaway riders express riders, and all-round riders. According to the differences in the types of orders that riders can serve, a cost function based on a fuzzy time window is constructed to represent the satisfaction of merchants and customers with the time when riders arrive at pick-up and delivery points. The satisfaction is then transformed into a time penalty function. A model is constructed to minimize time penalty costs, route driving costs and personnel operation costs. Considering the characteristics of the model and the limitations of traditional algorithms, this study designs a hybrid algorithm combining genetic algorithms and search algorithms in large domains. Then, the simulated annealing algorithm, genetic algorithms, and hybrid algorithm are used to solve the problem respectively through concrete examples. The analysis of the optimization results of different algorithms validates the feasibility and effectiveness of the proposed model and the improved algorithm. Experimental results show that considering the heterogeneity of riders and the satisfaction of merchants and customers during crowdsourcing delivery not only effectively improves their satisfaction but also reduces delivery costs and improves delivery efficiency for crowdsourcing platforms. This strategy offers a reference for crowdsourcing platforms in formulating delivery strategies.
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基金项目:国家自然科学基金(71601118)
引用文本:
王会静,袁鹏程.考虑骑手异质性的众包配送策略优化.计算机系统应用,2024,33(12):210-221
WANG Hui-Jing,YUAN Peng-Cheng.Optimization of Crowdsourcing Delivery Strategy Considering Rider Heterogeneity.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):210-221
王会静,袁鹏程.考虑骑手异质性的众包配送策略优化.计算机系统应用,2024,33(12):210-221
WANG Hui-Jing,YUAN Peng-Cheng.Optimization of Crowdsourcing Delivery Strategy Considering Rider Heterogeneity.COMPUTER SYSTEMS APPLICATIONS,2024,33(12):210-221