Advanced data-driven approaches to design and plan robust and sustainable network for the forest biorefinery value chains taking into account uncertainty

当今社会的主要挑战之一在于如何管理资源的可持续利用，以及如何将资源转化和分配为对环境影响小、社会和经济影响大的商品和服务。供应链网络的设计是复杂的，涉及战略和敏感决策。在森林业，战略决策包括森林管理战略、道路建设、生产能力、技术投资、产品和市场开发、生产战略和库存地点。加拿大森林业寻求通过创新的生物产品和新技术实现经济转型，使其产品组合多样化，并以高端产品进入新市场。由于新市场、新先进材料、相关技术、新供应以及与现有价值链整合的不确定性，这种转变增加了额外的风险。不确定性也是林业管理的一大特点，因为它涉及不确定的生物和自然过程、虫害、大片森林领地和长期规划范围内的决策。基于运筹学的决策支持系统(DSS)已经被引入到森林价值链的管理中，目前正被政府和工业界使用。大多数都是基于确定性模型，没有考虑不确定性。这一缺陷限制了使用，并降低了决策者对这些DSS的信任以及应用DSS提供的决策的能力。林业部门价值链使用大量的处理和商业信息系统数据。这些数据来自工业价值链以及外部传感器和系统中的许多来源，并与不同程度的不确定性有关。这些信息对所有规划都至关重要，但其质量和不确定性程度各不相同，因此很难评价所取得的成果。缺乏方法和工具，包括可用于回答挑战性问题的不确定性和风险建模，以支持利益攸关方逐步向更具竞争力和可持续的森林工业过渡。在过去，由于种种原因，随机规划、稳健(RO)、数据驱动的自适应稳健优化(ARO)都没有被用来处理不确定性和风险。在这笔发现赠款中，我们的目标是开发先进的数据驱动方法，在考虑到不确定性的情况下，为森林生物精炼价值链设计和规划一个强大和可持续的网络。该计划将带来新的解决方案，以帮助各种利益相关者在考虑各种不确定性来源的同时，解决经济、环境和社会可持续的价值链的设计和规划问题。

One of the key challenges in today's society resides in managing a sustainable usage of the resources as well as of their transformation and distribution into goods and services with both low environmental impact and high positive social and economic impacts. Design of the supply chain network is complex and deals with strategic and sensitive decisions. In the forest industry, strategic decisions include forest management strategy, roads construction, production capacity, technology investments, product and market development, production strategy and inventory location. The Canadian forest industry seeks to transform its economy through novel bioproducts and new technologies to diversify its products portfolio and entering into new markets with high end-value products. This transformation adds additional risks due to the uncertainty of the new markets, new advanced materials, associated technologies, new supply, and integration with existing value chains. Uncertainty is also a major feature in forestry management since it deals with uncertain biological and natural processes, infestations, large forest territories and decisions over long-term planning horizon. Decision supports systems (DSSs) based on operations research has been introduced to manage value chain in the forest and now are used by governments and industry. Most of are based on deterministic models and do not consider uncertainty. This drawback limits the usage and reduces the trust of decision makers on these DSSs and ability to apply the decisions provided by the DSSs. Forest sector value chains use huge volumes of processing and business information systems data. The data comes from many sources in the industrial value chain and external sensors and systems, and are associated with varying degrees of uncertainty. This information is critical for all planning but has different level of quality and uncertainty making it problematic to evaluate the results obtained. There is a lack of approaches and tools which include uncertainty and risk modeling that can be used to answer the challenging questions to support the stakeholders in the transition of the forest industry to more competitive and sustainable over time. In the past, stochastic programming, robust (RO), data-driven adaptive robust optimization (ARO) have not been used to deal with uncertainty and risk due to many reasons. In this Discovery grant, we aim to develop advanced data-driven approaches to design and plan a robust and sustainable network for forest biorefinery value chains taking into account uncertainty. This program will lead to new solutions to assist the various stakeholders address the design and planning of value chains that are economically, environmentally and socially sustainable while considering various sources of uncertainty.