Modelling and analysis of emergent technologies in logistics and distribution

物流配送新兴技术的建模与分析

• 批准号: RGPIN-2020-04498
• 负责人: Gzara, Fatma
• 金额: $ 3.13万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758145
• 关键词: Modelling; analysis; emergent; technologies; logistics

The e-commerce market has seen an unprecedented growth in Canada and world-wide. Global e-retail sales were valued at $2.3 trillion US in 2017 and are expected to reach 4.9 trillion in 2021 (Statista 2019). In Canada, e-commerce represented 7.3% of total retail in 2017, increasing from 4.5% in 2013. This growth is pushing the industry to optimize last mile operations and adopt innovative technologies to enable cost-effective same-day delivery. Industry experts as well as major e-retailers recognize that same-day delivery represents a major competitive advantage as it combines the convenience of ordering online and the immediacy of shopping at the store. However, it should be offered free or at minimal cost to become the standard of last mile delivery in the future. While there is a large body of literature on distribution planning for e-retailing, novel technologies, such as multi-modal, drone, and crowdsourced delivery, create new challenges both in terms of modelling and optimization. There is limited work that explicitly examines problems arising from crowdsourced last mile delivery and the assumptions do not reflect industry trends. Similarly, most of the research on drone delivery focuses on operational aspects and ignores the tactical and strategic issues like feasibility, profitability, and capacity planning. Same day and last mile deliveries are characterized by short service times, uncertainty in demand which may arrive dynamically, and uncertainty in supply as is the case in crowdsourced delivery. These features call for new models and new solution methods that are fast and reliable. The research program aims to address new problems that have not been studied in the literature, and to develop appropriate models and solution methods to enable the effective use of the new technologies. In particular, the dynamic and uncertain aspects of same-day delivery lead to complex optimization problems that challenge the state-of-the-art. The e-retailing industry generates an abundance of data on all aspects of operation from demand, to weather and traffic conditions, to supply, that is underutilized at best. Transforming this data into information provides a golden opportunity to create competitive advantage for Canadian and global e-retailers. Data may be used to assess, optimize, and validate operations, processes, and business strategies. We will combine data availability with the improvements in computational power to build realistic data-driven optimization models and solution methods. These are bound to benefit dynamic and uncertain systems that go beyond logistics such as telecommunications and emergency medical service systems. Through this program, seven HQP will be trained in quantitative problem solving and data analytics skills in data analysis, prediction, and action prescription. These skills are very much sought by Canadian employers in such industries as logistics and supply chain, banking, health care, and consulting.

电子商务市场在加拿大和世界范围内出现了前所未有的增长。2017年，全球电子零售销售额为2.3万亿美元，预计到2021年将达到4.9万亿美元（Statista 2019）。在加拿大，2017年电子商务占零售总额的7.3%，高于2013年的4.5%。这种增长正在推动行业优化最后一英里的运营，并采用创新技术，以实现经济高效的当日送达。行业专家和主要的电子零售商都认识到，当日送达是一个主要的竞争优势，因为它结合了在线订购的便利性和在商店购物的即时性。然而，它应该免费或以最低成本提供，以成为未来最后一英里配送的标准。虽然有大量关于电子零售分销计划的文献，但新技术，如多式联运、无人机和众包交付，在建模和优化方面都带来了新的挑战。明确研究“最后一英里”众包配送带来的问题的工作有限，而且这些假设也没有反映行业趋势。同样，大多数关于无人机配送的研究都集中在操作方面，而忽略了可行性、盈利能力和容量规划等战术和战略问题。当日配送和最后一英里配送的特点是服务时间短，需求不确定（可能动态到达），以及供应不确定（众包配送）。这些特点需要快速可靠的新模型和新解决方法。该研究计划旨在解决文献中未研究的新问题，并开发适当的模型和解决方法，以使新技术的有效利用。特别是，当天交货的动态和不确定性导致了复杂的优化问题，这对最先进的技术提出了挑战。电子零售行业产生了从需求、天气、交通状况到供应等各个方面的大量数据，这些数据充其量也没有得到充分利用。将这些数据转化为信息为加拿大和全球电子零售商创造竞争优势提供了一个绝佳的机会。数据可用于评估、优化和验证操作、流程和业务策略。我们将结合数据可用性和计算能力的改进来构建现实的数据驱动优化模型和解决方法。这必然会使电信和紧急医疗服务系统等超越物流的动态和不确定系统受益。通过该项目，7名HQP将在数据分析、预测和行动处方方面接受定量问题解决和数据分析技能的培训。这些技能是加拿大雇主在物流和供应链、银行、医疗保健和咨询等行业非常需要的。