4 Presentations

• 
  10:30 AM - 10:55 AM

  Forecast-driven collaborative repositioning optimization. An application to Madagascar

  • Birce Adsanver, presenter, HEC Montréal
  • Burcu Balcik, Ozyegin University
  • Valérie Bélanger, CIRRELT, HEC Montréal
  • Marie-Eve Rancourt, HEC Montréal

  Disasters are increasingly affecting human lives with greater frequency and severity over the years. Depending on the forecasting capabilities, it can be possible to anticipate and mitigate the impacts of certain events. Anticipatory actions, also known as forecast-based early actions, play a crucial role in this regard by allowing humanitarian actors to take proactive measures before the disasters strike. This study focuses on a repositioning problem in anticipation of storms within a collaborative countrywide network. The network involves multiple humanitarian agencies that strategically preposition relief supplies across different regions. When a storm is anticipated to hit the country, the agencies begin receiving forecast updates providing predictions on storm conditions. They estimate disaster impacts by utilizing the available forecasts and relocate the prepositioned supplies to the potentially affected regions for a faster response. We address this problem by proposing an analytical approach that provides relocation recommendations based on the forecasts. We collaborate with the Emergency Supply Prepositioning Group to design and integrate such a decision support tool into their “STOCKHOLM” platform, which offers prepositioned stock mapping and analyses to the stockholders. We test our approach and analyze the benefits of collaboration with a case study on Madagascar. The analyses are conducted by processing data related to the logistics network and storm forecasts.

• 
  10:55 AM - 11:20 AM

  A bi-level approach for shelter network design and evacuation planning problem: An application to flood preparedness in Haiti

  • Maedeh Sharbaf, presenter,
  • Marie-Eve Rancourt, HEC Montréal
  • Valérie Bélanger, CIRRELT, HEC Montréal

  We present a decision-support tool for flood preparedness developed through a collaboration with the World Bank in Haiti. The shelter network design and evacuation planning problem is formulated as a bi-level optimization model in which the leader is the decision-making authority, and the followers are the evacuees whose reactions to upper-level decisions is modeled. The model considers the inclusion of evacuees' compliance behavior and the temporal evolution of flood disaster over time. The model is tested using socio-demographic, regional characteristics, attributes of hazard, and GIS data.

• 
  11:20 AM - 11:45 AM

  Optimal Food Allocation Decisions within a Multi-dimensional Framework in Food Bank Supply Chains

  • Duygu Pamukcu, presenter,

  Food banks are crucial in mitigating food insecurity by allocating donations to assisting agencies. This study explores optimal allocation policies for food banks within a capacitated, multi-echelon, multi-product, and multi-period framework. Leveraging the Food Banks of Quebec network, it addresses logistical challenges to ensure equitable and efficient food allocation.

• 
  11:45 AM - 12:10 PM

  Mitigating fire risk towards critical and residential structures near a high ignition area using Critical Node Detection

  • Vittorio Nicoletta, presenter, HEC Montréal, Natural Resources Canada
  • Valérie Bélanger, CIRRELT, HEC Montréal
  • Denys Yemshanov, Natural Resources Canada
  • Raphaël D. Chavardès, Natural Resources Canada
  • Anne Cotton-Gagnon, Natural Resources Canada
  • Jonathan Boucher, Natural Resources Canada

  Wildfires pose a significant threat to both critical infrastructure and residential areas, especially in regions with a high proportion of wildland-urban interface (WUI) and prolonged fire seasons due to climate change. This study explores the application of Critical Node Detection (CND) to inform strategic landscape management plans aimed at limiting fire spread and intensity while considering the restrictive costs associated with hard-to-access areas.
  Our project focuses on a landscape with intensive military training, where ignitions are frequent. Our objective was to identify optimal fuel treatment locations to restrict wildfires from escaping the base and impacting neighboring communities. We integrated CND with fire-growth modeling and structural loss rate modeling to comprehensively assess wildland fire risk. Our preliminary results suggest which particular strategies to adopt according to the desired objective of the mitigation and the particular fire profiles, comparing their costs and relative efficacy.
  Our approach encompasses the assessment of fire hazards, impacts, and mitigation strategies, offering valuable insights for proactive wildfire management in comparable settings. This interdisciplinary framework serves as a robust tool for safeguarding communities and bridging the realms of fire science, land management, and military operations, thereby enhancing overall wildfire risk management effectiveness.