This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse function...This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.展开更多
To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after...To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after discussing three major processes of order fulfillment, we summarize functional and quality attributes of the OFS. Subsequently, we investigate SOA （Service Oriented Architecture） and present a SOA meta-model to be an integrated framework and to fulfill quality requirements. Moreover,based on the SOA meta-model, we construct a conceptual framework model that aims to conveniently integrate other functions fiom different systems into the order fulfillment system. This model offers enterprises a new approach to implementing order fulfillment.展开更多
新零售带动传统企业转型,加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况,提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制,缩减问题求解空间,...新零售带动传统企业转型,加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况,提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制,缩减问题求解空间,同时为了减少单独配送导致的路径重叠,采用协同配送的模式整合路径,并通过订单拆分与配送之间的调整优化降低订单履行成本。集成广度优先搜索和局部搜索算法,构造TNILS(top-N&improved local search)混合启发式算法求解问题。在合成数据集的基础上,通过协同配送与单独配送的结果对比,证明了协同配送的有效性及提出算法的可行性。通过与其他算法的实验结果对比,验证TNILS算法的有效性和稳定性。展开更多
文中针对移动机器人履行系统(Robotic Mobile Fulfillment System,RMFS)中的订单分批问题,首先,以最小化货架搬运次数和订单批次之间相同货架数量之和为目标,建立该问题的0-1整数规划模型;其次,根据订单批次之间相同货架数量和货架搬运...文中针对移动机器人履行系统(Robotic Mobile Fulfillment System,RMFS)中的订单分批问题,首先,以最小化货架搬运次数和订单批次之间相同货架数量之和为目标,建立该问题的0-1整数规划模型;其次,根据订单批次之间相同货架数量和货架搬运次数,构建相应的权重指标,以减少货架冲突次数;再次,设计求解订单分批问题的改进萤火虫算法,该算法在萤火虫算法的基础加入破坏解、修复解等操作进行局部搜索,以增强萤火虫算法的局部搜索能力;最后,在定义货架冲突的概率计算方法的基础上,对比分析改进萤火虫算法和萤火虫算法、贪婪算法的求解效果。分析结果表明,改进萤火虫算法在货架搬运次数、订单批次间相同货架数量、货架冲突的概率,都要优于贪婪算法和萤火虫算法。展开更多
文摘This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
文摘To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS （order fulfillment system） are built. Then after discussing three major processes of order fulfillment, we summarize functional and quality attributes of the OFS. Subsequently, we investigate SOA （Service Oriented Architecture） and present a SOA meta-model to be an integrated framework and to fulfill quality requirements. Moreover,based on the SOA meta-model, we construct a conceptual framework model that aims to conveniently integrate other functions fiom different systems into the order fulfillment system. This model offers enterprises a new approach to implementing order fulfillment.
文摘新零售带动传统企业转型,加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况,提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制,缩减问题求解空间,同时为了减少单独配送导致的路径重叠,采用协同配送的模式整合路径,并通过订单拆分与配送之间的调整优化降低订单履行成本。集成广度优先搜索和局部搜索算法,构造TNILS(top-N&improved local search)混合启发式算法求解问题。在合成数据集的基础上,通过协同配送与单独配送的结果对比,证明了协同配送的有效性及提出算法的可行性。通过与其他算法的实验结果对比,验证TNILS算法的有效性和稳定性。
文摘文中针对移动机器人履行系统(Robotic Mobile Fulfillment System,RMFS)中的订单分批问题,首先,以最小化货架搬运次数和订单批次之间相同货架数量之和为目标,建立该问题的0-1整数规划模型;其次,根据订单批次之间相同货架数量和货架搬运次数,构建相应的权重指标,以减少货架冲突次数;再次,设计求解订单分批问题的改进萤火虫算法,该算法在萤火虫算法的基础加入破坏解、修复解等操作进行局部搜索,以增强萤火虫算法的局部搜索能力;最后,在定义货架冲突的概率计算方法的基础上,对比分析改进萤火虫算法和萤火虫算法、贪婪算法的求解效果。分析结果表明,改进萤火虫算法在货架搬运次数、订单批次间相同货架数量、货架冲突的概率,都要优于贪婪算法和萤火虫算法。
