4 présentations

• 
  13h30 - 13h55

  Mathematical programming modelling in support of Northern Operational Support Hub facility location and requirements definition

  • D. Gregory Hunter, prés., Defence Research and Development Canada

  The Canadian Joint Operations Command Operational Research and Analysis team has been tasked with supporting the Northern Operational Support Hub facility location and selection process. To do this, the team is developing a series of optimization models representing the logistical aspects of implement domestic contingency plans in the Canadian North. We describe the development to date on a mathematical model of a response to a major maritime disaster and evacuation involving a cruise ship. The framework constructed permits the evaluation of the operational value of existing and hypothetical infrastructure. This will be used in a second global optimization of the set of infrastructure investments within a fixed budget. The model also optimizes the evacuation planning and delivery of personnel and supplies to the forward operation locations supporting the evacuation such that loss of life is minimized.
• 
  13h55 - 14h20

  Exploring set covering, packing, and partitioning for portfolio optimization

  • Fred Ma, prés., Public Service of Canada

  In past years, a visual analytics tool was developed to optimize portfolios of projects under a "capital budgeting" model. Projects were selected to maximize their total additive values while still remaining within resourcing limits. The current incarnation of the tool generalizes this so that models and input data are highly configurable. The first step in expanding the supported models has been to implement a set covering model. Projects are selected to maximize overall capability coverage by the portfolio rather than simple summation of individual project values. This significantly expands the solvable kinds of portfolio problems as viewed by clients. To better exploit the tool's configurability and illuminate ways to develop its modelling support, a critical literature survey was undertaken into the potential application of set covering, packing, and partitioning to military problems. This review delves into the strengths of weaknesses of various studies for portfolio-like problems. It establishes a compendium of problem and model archetypes, and identifies features that make them suitable for each other. The presentation will provide broad cross-cutting observations.
• 
  14h20 - 14h45

  Data-Driven Multi-Venue Location Optimization for Military Recruitment Activities

  • Shervin Shams-Shoaaee, prés., Director General Military Personnel Research and Analysis, Department of National Defence

  The Canadian Armed Forces (CAF) is currently facing recruitment challenges. It is therefore important to maximize the reach of military recruitment activities, especially in geographical areas with a high potential of generating recruits. The CAF organize recruitment and outreach activities at various venues throughout the country. Venue locations need to be chosen carefully to maximize resource efficiency and increase recruitment. Although the selection and optimization of venue locations is a relatively simple problem in rural areas, it is particularly complex in urban areas where (a) there are many candidate venues to choose from, and (b) there are overlaps in geographical areas each venue can attract audiences from. This problem can be expressed as a mixed integer nonlinear problem (MINLP). This study develops a mathematical model to maximize the total expected reach of recruitment activities using scoring of geographical areas based on historical recruitment and area demographics. To solve the resulting MINLP an exact optimization algorithm is introduced. Distances to event venues are calculated using a Euclidean distances metric and are filtered by shortest path travel times. Results are provided for a sample geographical area of the National Capital Region and can be expanded nation wide.
• 
  14h45 - 15h10

  Simulating Technician Populations with Tandem Analytic and Discrete Event Models

  • François-Alex Bourque, prés., Defence Research and Development Canada Centre for Operational Research and Analysis
  • Ryan Ambrose, Defence Research and Development Canada Centre for Operational Research and Analysis

  Military workforce modelling is typically limited to either a series of analytic equations, or a simulation model. However, developing two such models in tandem has the benefit of cross-validation as well as the opportunity to explore problem space not easily accessed by a single approach. In particular, business rules for force employment are not easily described by closed-form equations while simulation models require exceedingly large computational resources to reach the asymptotic behaviour provided by analytic equations. This work leverages the benefits of both approaches to describe the population and career trends of technician individuals. As this career tends to have well defined training requirements, hence clear delineation between semi-functional apprentices and fully-functional journeymen, it is well suited to population modelling. Notional distributions for career parameters are assumed and the results for career progression and fleet readiness are compared.