4 présentations

• 
  13h00 - 13h22

  Re-righting renewable energy research with Indigenous communities in Canada

  • Serasu Duran, University of Calgary
  • Jordyn Hrenyk, University of Toronto
  • Feyza G. Sahinyazan, prés., Simon Fraser University
  • Emily Salmon, Simon Fraser University

  The global call to address climate change and advance sustainable development has created rapid growth in research, investment, and policymaking regarding the renewable energy transition of Indigenous communities. From a rightsholder perspective, Indigenous Peoples’ vision of sustainability, autonomy, and sovereignty should guide research on their energy needs. In this paper, we present a multi-method, inductive examination to identify gaps between Indigenous communities’ expressed needs and rights, and the questions researchers and policymakers investigate in energy transition research conducted in the context of Indigenous communities located in Canada. We combine a systematic review of the extant literature, a scoping review of the grey literature on offgrid communities by Indigenous and non-Indigenous governments and non-governmental policy bodies, qualitative primary data collected via fieldwork, and an in-depth study of an Indigenous-led renewable energy transition study conducted by Haíɫzaqv Nation’s Climate Action Team. We holistically examine these different perspectives and identify emergent themes to recommend ways to bridge the gaps between off-grid renewable energy research and stated Indigenous community priorities. Specifically, we recommend designing equitable research practices, understanding community worldviews, developing holistic research goals, respecting Indigenous data sovereignty, and sharing or co-developing knowledge with communities to align with community priorities closely.

• 
  13h22 - 13h44

  Optimal Learning and Management of Threatened Species

  • Jue Wang, prés., Queen's University
  • Xueze Song, University of Alabama
  • Roozbeh Yousefi, Queen's University
  • Zhigang Jiang, Institute of Zoology, Chinese Academy of Sciences

  Amid an unprecedented loss of biodiversity, a pressing issue is how to improve the efficiency of conservation with limited resources and information. Collecting data on species with a small population is costly and time-consuming, and many high-stake decisions need to be made based on limited data. We develop a partially observable Markov decision processes (POMDP) model with unknown parameters to jointly optimize the information collection and protection efforts for threatened species. The model takes into account uncertainties about the state, detectability, and dynamics of the species, and adaptively adjusts the efforts of surveying and protection in real time. Although the standard formulation is intractable, we exploit the structure of ecological problems to identify a hybrid belief state in low dimension, and
  reformulate the stochastic dynamic program as a piecewise deterministic optimal control problem. We also conduct a case study on the conservation of Hainan Gibbon, the rarest primate species, in which we extend the model to optimize the spatiotemporal allocation of limited resources.

• 
  13h44 - 14h06

  Optimizing Relief Distribution and Evacuation Planning with Social Considerations in Disaster Management

  • Marziye Seif, prés., School of Engineering, University of British Columbia, Kelowna, BC, Canada
  • Mohammad reza Rezayat, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
  • Hossein Zolfagharinia, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, Canada
  • Babak Mohamadpour Tosarkani, School of Engineering, University of British Columbia, Kelowna, BC, Canada

  Effective disaster management requires robust strategies to address the complexities of distributing relief items and facilitating evacuations under uncertainty. The unpredictable nature of disasters and fluctuating demand present significant challenges for planning and response efforts. This study introduces a distributionally robust optimization method to enhance the distribution of relief items and support evacuation operations. An out-of-sample analysis demonstrates the superiority of the proposed model over two-stage stochastic programming in terms of robustness within the humanitarian supply chain. This study also considers critical social factors, such as education levels and trust in government, which influence optimal strategies. The model's performance is evaluated through a case study of the Fort McMurray wildfire in Alberta. The results highlight the model's effectiveness in minimizing unmet demand, reducing operational costs, ensuring equitable distribution of relief resources across affected regions, and facilitating the evacuation of people to shelters while accounting for blocked roads and varying social parameters.

• 
  14h06 - 14h28

  Optimizing Wildfire Suppression with Branch-and-Price-and-Cut

  • Leonard Boussioux, University of Washington
  • Alexandre Jacquillat, prés., Massachusetts Institute of Technology
  • Jacob Wachspress, MIT Operations Research Center

  In periods of intense, synchronous wildfire activity, fire system managers must make rapid fire prioritization decisions over a disperse geographic area with limited suppression resources. This paper defines the Wildfire Suppression and Crew Assignment Problem, which optimizes resource allocation to triage fires based on damage risk, crew availability and spatiotemporal dynamics. We formulate a two-sided set partitioning model on time-space-rest networks for crew assignments and time-state networks for fire damage, with linking constraints between both; this representation can encode a broad class of non-linear wildfire spread models and diverse suppression objectives. To solve it, we develop a two-sided column generation algorithm that generates fire suppression plans and crew routes iteratively. We embed it into a branch-and-price-and-cut algorithm to retrieve an optimal integer solution, using novel special-purpose cuts that augment generalized-upper-bound cover cuts and a novel branching rule that leverages dual information from the linking constraints. Extensive computational experiments show that the algorithm scales to practical problems that remain otherwise intractable. The optimization methodology can provide high-quality solutions by jointly optimizing wildfire triaging and crew assignments, resulting in enhanced wildfire suppression effectiveness.