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

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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.