Integrating waste and resource management: Data-driven optimization of urban mining logistics

整合废物和资源管理：数据驱动的城市矿业物流优化

• 批准号: RGPIN-2019-07172
• 负责人: Lamghari, Amina
• 金额: $ 1.89万
• 依托单位: Université du Québec à Trois-Rivières
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739224
• 关键词: Integrating; waste; resource; management; Data

This research program aims to contribute to an emerging field, urban mining, that has been increasingly studied in the past decade by industrial ecology, mineral engineering, and waste management researchers but has received little attention in the Operations Research (OR) community. Urban mining is both a concept and a set of practices that regards all of the waste generated within any urban built environment as being a pool of materials that can be extracted, processed, and reintegrated into manufacturing production cycles, much as a traditional mine prospects a geographical area of potentially valuable minerals for extraction, processing, and end use in manufactured goods. In a world dealing with the effects of climate change, environmental degradation, and resource depletion, urban mining has the potential to reduce waste and thereby minimize its impact, cut demand for primary raw materials, and create opportunities for new business models. To realize this potential, technology for the recovery of raw materials from waste is certainly necessary, but it is also of utmost importance to address other challenges: managing urban mining projects so as to obtain mutual benefits among the various stakeholders; analyzing potential investment decisions; accounting for inherent uncertainties and minimizing risks; etc. How OR can address such challenges and move urban mining initiatives forward drives this research program. Specifically, the aim of the proposed program is to develop novel decision-making tools for the planning of urban mining logistics under uncertainty. For the next five years, it is intended to conduct research in two areas where these decision-making tools can have a big impact: e-waste (waste from discarded electrical or electronic devices) and CRD waste (waste from construction, renovation, and demolition). Not only do these two waste streams contain critical raw materials of industrial interest (e.g., gold, silver, copper, iron) worth billions of dollars (e.g., worldwide, in 2016, the value of resources in e-waste was estimated at $83 billion CDN), but they are major parts of the overall waste stream. Additionally, most of these wastes end in landfills, creating a serious environmental concern due to the hazardous materials they contain. This research program will make a positive impact in the emerging field of urban mining and the more established field of reverse logistics by addressing some gaps in the current literature. In a broader context, it will also contribute to advancing knowledge on how to capitalize on the synergies between optimization and machine learning to make better decisions well-hedged against uncertainty and to derive computationally tractable solution procedures. Ultimately, the tools to be developed will help Canada manage its waste in a more economically efficient and environmentally sustainable way, shifting the way waste is thought of: from a pressing environmental problem to an economic asset.

该研究计划旨在为城市采矿这一新兴领域做出贡献，该领域在过去十年中越来越多地被工业生态学，矿物工程和废物管理研究人员研究，但在运筹学（OR）社区中很少受到关注。 城市采矿既是一个概念，也是一套实践，它将任何城市建筑环境中产生的所有废物视为一个可以提取、加工和重新融入制造业生产周期的材料池，就像传统矿山勘探一个地理区域，其中可能有价值的矿物可用于提取、加工和最终用于制成品一样。在一个应对气候变化、环境退化和资源枯竭影响的世界里，城市采矿有可能减少废物，从而最大限度地减少其影响，减少对初级原材料的需求，并为新的商业模式创造机会。为了实现这一潜力，从废物中回收原材料的技术当然是必要的，但应对其他挑战也至关重要：管理城市采矿项目，以便在各利益攸关方之间实现互利;分析潜在的投资决定;考虑到固有的不确定性并尽量减少风险; OR如何应对这些挑战并推动城市采矿计划向前发展，推动了这项研究计划。 具体而言，该计划的目的是开发新的决策工具，规划城市矿业物流在不确定性。今后5年，将在电子废弃物（废弃电器或电子设备的废弃物）和CRD废弃物（建筑、装修、拆除的废弃物）这两个决策工具能够发挥重要作用的领域进行研究。这两种废物流不仅含有工业上感兴趣的关键原材料（例如，金、银、铜、铁）价值数十亿美元（例如，在全球范围内，2016年，电子废物的资源价值估计为830亿加元），但它们是整个废物流的主要部分。此外，这些废物中的大多数最终被填埋，由于它们含有危险材料，造成了严重的环境问题。这项研究计划将通过解决当前文献中的一些空白，对新兴的城市采矿领域和更成熟的逆向物流领域产生积极影响。在更广泛的背景下，它还将有助于推进关于如何利用优化和机器学习之间的协同作用的知识，以做出更好的决策，充分对冲不确定性，并推导出计算上易于处理的解决方案程序。最终，将开发的工具将帮助加拿大以更经济有效和环境可持续的方式管理其废物，改变废物的思维方式：从紧迫的环境问题转变为经济资产。