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You are cordially invited to theÌýGraduate Student Research SeminarÌýof theÌýDepartment of Industrial Engineering

Date: Wednesday, October 13, 2021
Time: 1:00 - 4:30 PM
Venue: Online Event

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Schedule:

1:00-1:25 PMÌýLuana Almeida
A Greedy Randomized Adaptive Search Procedure (GRASP) for the multivehicle prize collecting arc routing for connectivity problem

1:25-1:50 PMÌýPranitha Vattoni
Critical regions and locations for the road clearing and relief supplies distribution model in Vancouver Island after a Cascadia earthquake

1:50-2:15 PMÌýTanmoy Das
Modeling resource allocation of emergency response in Arctic oil spills

2:15-2:40 PMÌýLinden Smith
Identifying changes in demand for perishable products using statistical process control and machine learning forecasting

2:40-2:50 PMÌýBREAK

2:50-3:15 PMÌýWheejae Kim
Impacts on human-machine team trust, workload and accuracy by altering reliability information display of Automated Aid System Cognitive Shadow

3:15-3:40 PMÌýCeilidh Bray
A Comparative framework in healthcare decision-making of the functional characteristics and the financial costs of stockpiling two respirator types in pandemic settings

3:40-4:05 PMÌýYun Yin
Modelling and solving of the multi-calendar naval surface ship work period

4:05-4:30 PMÌýHyojae Kim
Developing a decomposition matheuristic method to solve the multi-calendar naval surface ship work period problem

Abstracts:

A Greedy Randomized Adaptive Search Procedure (GRASP) for the multivehicle prize collecting arc routing for connectivity problem
Luana Almeida, PhD Candidate

Natural disasters such as earthquakes can severely impact road networks. Depending on the disaster intensity and the size of the affected area, the network may be divided into multiple disconnected parts. In a disaster response context, decision-makers need to determine the roads that should be unblocked to facilitate relief activities such as search and rescue, evacuation, and distribution of emergency supplies. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) is a well-known problem dealing with such a scenario. A matheuristic to solve the KPC-ARCP was proposed in previous research, which tested instances with fewer than 400 vertices and 700 edges. However, it is unknown whether the matheuristic can handle larger instances. This article proposes a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic with the hypothesis that GRASP is faster and can solve more extensive networks. Two sets of tests are performed on randomly generated instances with increasing size. The gap in the objective function values and the execution times of GRASP versus the matheuristic are compared. The results indicate that GRASP can achieve objective function values as good as the matheuristic and is significantly faster depending on the parameter settings.

Critical regions and locations for the road clearing and relief supplies distribution model in Vancouver Island after a Cascadia earthquake
Pranitha Vattoni, MASc Student

Vancouver Island lies on the Cascadia Subduction Zone, which makes the region extremely vulnerable to large-scale earthquakes and tsunamis that may follow. To improve the preparedness for such an event, the SIREN project research team has developed several models to identify: communities that may need assistance, delays in transportation operations, identification and reconstruction of damaged roads, optimizing the delivery of supplies from the mainland to the islands using ferries, barges, or helicopters, and from the ports to the communities using trucks within a specific time limit. With this work, we aim to run what-if analyses and conduct an extensive sensitivity analysis of the Road Clearing and Relief Supply Distribution model to identify critical roads, communities, and regions on the island. The inputs to the model are classified into four different types and some output parameters such as the number of communities served, the total population served, and the percentage of roads repaired were studied for changes made in the inputs. By changing the location of the road clearing teams’ depot, we have identified the ‘best’ location for the depot, as well as roads, communities, and regions on Vancouver Island that are critical to the model.

Modeling resource allocation of emergency response in Arctic oil spills
Tanmoy Das, PhD Candidate

Accidental oil spills result in significant contamination in the marine environment and postspill response recovery operations are expensive as well as time-consuming. The problem become exacerbating when the spill size is large. Hence, minimizing the consequences of oil spills is a prime concern for decision-makers. However, unified decision support tools that can estimate oil spills, rank, and allocate response resources optimally, and capture uncertainty are still underdeveloped. The overall purpose of the proposed research is to model emergency resource allocation in the form of a decision support tool (DST). This DST can be used to provide reasonable estimates of likely spill volumes quickly and response allocation and uncertainties herein, ultimately contributing to risk management. This DST will be implemented in hypothetical oil spill scenarios in Arctic Canada. This research will deliver a decision-making modeling framework with practical relevance in pollution preparedness and response risk assessment, ultimately broadening the available toolboxes. The modeling outcome includes long-term planning of resource prioritization and allocation options of an oil spill, which is helpful for the Canada Coast Guard, oil spill response organizations e.g., Eastern Canada Respo