4 présentations

• 
  15h40 - 16h05

  Not anymore impacts in shadows of disaster

  • Babak Mohamadpour Tosarkani, School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, BC, V1V 1V7, Canada
  • Mahsa Mohammadi, prés., School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, BC, V1V 1V7, Canada

  The environmental emergency refers to a situation that requires immediate action due to natural disasters (e.g., wildfires, hurricanes, floods, earthquakes). In 2021, the extreme heatwaves caused frequent wildfires, and following that, rapid and excessive rainfalls led to flood damage and widespread devastation (e.g., loss of life). An emergency response (i.e., disaster relief operations such as optimal scheduling of emergency vehicles and repair crew) to natural disasters is operationally challenging given the constraints imposed by space, time, and resources. Disaster management operations are performed before, during and the occurrence of environmental hazards. The objective of this paper is threefold, including (i) To develop predictive analytics model for humanitarian health care aid, including beneficiary demand (e.g., medicine, food, and water) and support demand (e.g., ambulance and emergency vehicle deployment), (ii) To propose a multi-objective optimization model for strategic (e.g., the number and location of relief distribution centers) and operational decisions (e.g., transportation planning and inventory management of humanitarian health care aid) during natural disasters, and (iii) To improve the efficiency of victim evacuation and coordination among different entities (e.g., hospitals, temporary medical services and relief distribution centers) in humanitarian supply chain networks in a post-disaster environment. The outcomes of this research enable policymakers in federal and provincial governments to predict humanitarian aid (medicine and emergency vehicles) and allocate them effectively during natural disasters.
• 
  16h05 - 16h30

  Vulnerability-Based Prioritization in Disaster Response Efforts

  • Feyza G. Sahinyazan, prés., Simon Fraser University
  • Halenur Sahin, TOBB ETU
  • Irfan Mahmutogullari, KU Leuven

  Equity, Efficiency and Effectiveness (3E) are commonly used objectives in disaster literature. In this paper, we introduce an alternative measure that prioritizes vulnerable populations during disaster response efforts and we compare its performance against the 3E measure using the socioeconomic characteristics of Istanbul residents in a potential earthquake scenario.
• 
  16h30 - 16h55

  On designing a resilient green supply chain to mitigate ripple effect: a two-stage stochastic optimization model

  • Hossein Mirzaee, prés.,
  • Hamed Samarghandi, University of Saskatchewan
  • Keith Willoughby, University of Saskatchewan

  Disasters and disruptions such as the COVID-19 pandemic can significantly interrupt supply chains and industries. To control these disruptions, decision-makers must focus on supply chain resiliency. This paper proposes a multi-stage, multi-period green supply chain design model and six resilience strategies, with downstream and upstream disruptions taken into account to analyze both the ripple and bullwhip effect, respectively. To control the mentioned disruptions and handle the uncertainties of parameter estimations, a two-stage stochastic optimization approach is devised. The objectives are to minimize the total cost of disruption, and CO2 emission under the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by sensitivity analysis. The results show the optimum structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decline in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap, and supply chain managers to predict the demand behavior of essential and non-essential products in the event of disruptions.
• 
  16h55 - 17h20

  CANCELED : Designing a Sustainable, Resilient, and Responsive Wheat Supply Chain Network under an Uncertain Environment

  • Ebrahim Sharifi, prés., Toronto Metropolitan University
  • Saman Hassanzadeh Amin, Ryerson University
  • Liping Fang, Ryerson university

  Abstract
  The resiliency and sustainability of agri-food supply chain networks are threatened due to the rapid population increase and other global disturbances, such as the COVID-19 pandemic. This work develops a hybrid multi-objective robust fuzzy stochastic programming model to consider the sustainability, resiliency, and responsiveness of a food supply chain in an unpredictable environment. The objective functions of sustainability aim to reduce the total cost and negative environmental and social impacts. Resiliency's objective function minimizes the nodes’ criticality, complexity, and level of customer de-service. In addition, the minimization of the total delivery time is regarded as a responsiveness metric. Also, an Interval Type-2 Trapezoidal (IT2Tr) interactive fuzzy programming model is proposed to handle the multi-objective aspect of the model. The proposed model is applied to a Canadian wheat supply chain, considering its specific characteristics, such as wheat blending and quality, climatic conditions, and multimodal transportation modes. Comparing the results from the deterministic and proposed fuzzy robust stochastic models validates the proposed model's capacity to deal with uncertain mixed parameters. Moreover, the results show that an increase in the responsivity of the proposed supply chain leads to an increase in the total cost. Finally, the proposed IT2Tr interactive fuzzy approach provides trade-offs between various objective functions, enabling managers and policymakers to use this approach as a decision-support tool in their decision-making processes.
  Keywords: Sustainable agri-food supply chain; Interactive fuzzy programming; Fuzzy robust stochastic programming; Multi-objective programming; Resiliency