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

采用先进的数据驱动方法，在考虑不确定性的情况下，为森林生物炼制价值链设计和规划强大且可持续的网络

• 批准号: RGPIN-2020-07141
• 负责人: Ouhimmou, Mustapha
• 金额: $ 1.89万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718710
• 关键词: Advanced; data; driven; approaches; design

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.

当今社会的主要挑战之一在于管理资源的可持续利用，以及资源转化和分配为低环境影响和高积极的社会和经济影响的商品和服务。供应链网络的设计是复杂的，涉及战略和敏感的决策。在森林工业中，战略决策包括森林经营战略、道路建设、生产能力、技术投资、产品和市场开发、生产战略和库存定位。