CAREER: System Design of Crowd Logistics via Participatory Agent-based Modeling

职业：通过基于参与式代理的建模进行人群物流的系统设计

• 批准号: 2046632
• 负责人: Caroline Krejci
• 金额: $ 53.26万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046632&HistoricalAwards=false
• 关键词: CAREER; System; Design; Crowd; Logistics

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by yielding insights into crowd logistics platform design. Crowd logistics is a term that describes crowdsourced transport and delivery of goods and freight. Senders source cost-effective logistics services from individual carriers via an online platform, which is controlled by a digital management system. To be successful, a platform must rapidly scale up its sender and carrier networks. If there are too few participants, senders and recipients will be dissatisfied by unfilled service requests, carriers will have insufficient opportunities, and the initiative will fail. This award supports a fundamental understanding of crowd logistics platform design for robust network growth and performance by attracting and retaining participants having a variety of economic and social motivations. The knowledge gained from this research will facilitate the use of crowd logistics for resilient food distribution in the face of large-scale disruptions, such as the COVID-19 emergency. The educational plan seeks to implement participatory modeling activities that will improve STEM students’ capacity for systems thinking, which is critical in the design and analysis of complex logistics systems. This research will produce a novel agent-based modeling approach to systematically explore the impacts of a crowd logistics platform’s design on its ability to quickly achieve a critical mass of participants. This approach will model the degree of control that a platform management system assumes over participants’ digital exchanges. At one end of the spectrum is a platform characterized by centralized control, full automation, and algorithmic management; at the other end is a platform with decentralized control and community-driven exchanges. Existing research on crowdsourcing platform design suggests that the degree of centralization of control impacts platform growth over time. However, understanding and predicting network effects that will lead to long-term platform growth is challenging, because the feedback loop perpetuated by participant decisions and interactions yields complex and dynamic system behavior. This research will address this challenge by representing carriers and senders as autonomous, heterogeneous, and adaptive agents, whose decisions to participate in a crowd logistics platform impact the participation of other agents over time. Participatory modeling sessions with crowd logistics practitioners will be leveraged for model validation, to ensure that agent behaviors correspond to those of their real-world counterparts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该学院早期职业发展计划（CAREER）拨款将通过对人群物流平台设计的深入了解，为国家繁荣和经济福利的发展做出贡献。 众包物流是一个术语，描述了众包运输和货物交付。 发件人通过由数字管理系统控制的在线平台从各个承运商处获得具有成本效益的物流服务。 为了取得成功，平台必须迅速扩大其发送者和运营商网络。 如果参与者太少，客户和接受者将对未满足的服务请求感到不满，运营商将没有足够的机会，该倡议将失败。 该奖项通过吸引和留住具有各种经济和社会动机的参与者，支持对人群物流平台设计的基本理解，以实现强大的网络增长和性能。 从这项研究中获得的知识将有助于在面临大规模中断（如COVID-19紧急情况）时使用人群物流进行弹性食品分配。 该教育计划旨在实施参与式建模活动，以提高STEM学生的系统思维能力，这对复杂物流系统的设计和分析至关重要。 这项研究将产生一种新的基于代理的建模方法，系统地探讨了一个人群物流平台的设计，其能力，以快速实现参与者的临界质量的影响。 这种方法将模拟平台管理系统对参与者数字交易所的控制程度。 在频谱的一端是一个以集中控制，全自动化和算法管理为特征的平台;另一端是一个分散控制和社区驱动交换的平台。 关于众包平台设计的现有研究表明，随着时间的推移，控制的集中程度会影响平台的增长。然而，理解和预测将导致长期平台增长的网络效应是具有挑战性的，因为参与者决策和交互所延续的反馈回路会产生复杂和动态的系统行为。本研究将通过将运营商和分销商表示为自主、异构和自适应代理来解决这一挑战，这些代理参与人群物流平台的决策会随着时间的推移影响其他代理的参与。 与人群物流从业者的示范性建模会议将被用于模型验证，以确保代理人的行为与现实世界中的同行相对应。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。