4 Presentations

• 
  10:30 AM - 10:55 AM

  Resource Allocation Optimization in Crowdsensed Parking Availability Data Collection

  • Elham Heydarigharaei, presenter,
  • Mehdi Nourinejad,

  Efficient parking occupancy detection is crucial for reducing congestion and optimizing mobility. Traditional methods, such as in-ground sensors and stationary cameras, are often expensive and difficult to scale.
  In contrast, mobile sensing devices, such as dashcams and ultrasonic sensors mounted on vehicles, offer a
  more flexible and cost-effective alternative. However, achieving comprehensive and efficient data collection
  requires the strategic deployment of these mobile sensors to balance accuracy, coverage, and operational costs. This paper presents an optimization framework for crowdsensed parking data collection that determines the optimal number of mobile sensing units and their routes. Unlike previous studies, which typically focus on either sensor allocation or path planning in isolation, our approach integrates both components while incorporating minimum and maximum revisitation thresholds to ensure data accuracy without excessive redundancy. The model represents the city as a graph of nodes and minimizes the total travel time of sensing units while maintaining the required data collection frequency. We develop a heuristic algorithm that efficiently approximates the optimal solution. The algorithm first applies K-Means clustering to partition the city into routes. Within each route, the Nearest Neighbor Algorithm determines the optimal sequence of visits, minimizing total travel distance. A linear resource allocation model then assigns the optimal number of sensing units per route, subject to headway constraints and the total number of available sensing units. To further refine the heuristic solution, we incorporate Lagrangian-inspired swapping heuristics that iteratively adjust node assignments within and between routes to improve efficiency. Numerical experiments demonstrate that our heuristic algorithm produces high-quality solutions with significantly reduced computational costs compared to the exact model. Our framework efficiently scales with city size, supports different sensing technologies, and enhances real-time parking monitoring for smart mobility solutions.

• 
  10:55 AM - 11:20 AM

  Optimizing Land Allocation in Mixed Circular Organic Farming

  • Hadi Gholizadeh, Universite Laval
  • Bahareh Mosadegh Sedghy, presenter, University of Lethbridge
  • Mohammadreza Nematollahi, University of Saskatchewan
  • Alireza Tajbakhsh, University of Newbrunswick

  Abstract

  Context:
  Circular Agriculture (CA) and Organic Farming (OF) have become increasingly important in modern agricultural systems, focusing on sustainability, resource efficiency, and minimizing environmental impact. Manure management, in particular, represents a critical challenge in optimizing organic farming systems. Effective optimization strategies can not only improve farm profitability but also enhance environmental sustainability by minimizing waste and improving resource use. However, despite the growing relevance of optimization in agriculture, there remains a need for detailed, practical models that can guide decisions on resource allocation, especially when considering the unique constraints of organic farming.
  Objectives:
  This study aims to develop and implement an optimization model to analyze resource allocation in organic corn and beef production by a rancher-farmer operating within a circular agriculture (CA) framework. Specifically, the objectives are:
  1. To identify the optimal allocation of manure and other production inputs to maximize profitability while adhering to organic farming principles.
  2. To evaluate the impact of varying input cost factors and environmental constraints on the optimization process.
  3. To provide actionable recommendations for farmers and policymakers on improving resource efficiency within organic farming systems, particularly in the context of manure management.
  Methods:
  An analytical optimization model is designed, incorporating both economic and environmental variables that influence organic farming practices. The model accounts for the resource allocation decisions. Nine scenarios are investigated based on the excess or shortage of manure and corn as inputs in production of corn and beef respectively. A sensitivity analysis is conducted to evaluate how changes in key parameters—such as input costs and environmental policies, —affect the optimal solutions. This analysis allows for a deeper understanding of the factors influencing decision-making in organic farming. Afterwards, in the numerical analysis section, using real-world data we calculated optimal resource allocation in beef and corn production.

  Results:
  This study provides a nuanced and comprehensive analysis of organic agricultural production, focusing on optimizing resource allocation within a mixed crop-livestock organic farming system.
  Profitability and Resource Balance
  The highest profits were observed in scenarios where farms operated either with a surplus of at least one key resource (corn or manure) or with a simultaneous deficit in both. This finding suggests that maximum profit is not achieved through equilibrium but through strategic imbalance, which allows more efficient use or monetization of surplus resources and cost savings from reduced waste or storage.
  Surplus and Deficit Dynamics
  Scenarios with excess manure demonstrated higher profitability increase, when the willingness to pay for manure increased.
  Sensitivity Analysis
  The sensitivity analysis revealed that:
  • Higher selling weight positively influenced profit, while a higher initial weight had a negative impact due to increased purchase costs. Optimal profit occurs when lighter animals are purchased, and feeding is optimized for weight gain.
  • Subsidies: The effect of organic subsidy on profitability was most significant in balanced systems (equal consumption and production of corn or manure). In contrast, surplus-based systems displayed less sensitivity due to near-capacity operation and diminishing marginal returns.
  • Livestock Cost: Balanced scenarios demonstrate resilience to rising cow purchase costs, while profit-maximizing surplus scenarios were more vulnerable, indicating a trade-off between profitability and risk tolerance.
  Implications for Practice
  These results emphasize that optimal resource allocation in CA systems does not rely on balance alone but on strategic surplus and deficit management tailored to market dynamics and policy environments. For instance, surplus manure becomes a valuable asset under favorable WTP conditions, while targeted imbalances can improve efficiency and profitability. Furthermore, weight optimization and selective feeding strategies can significantly affect economic outcomes.
  The variation in sensitivity across scenarios illustrates the need for adaptive strategies in farm management and policymaking. A uniform subsidy scheme is insufficient; performance-based or scenario-specific incentives are recommended. Infrastructure support for storage, nutrient recycling technologies, and cross-farm resource exchanges could further enhance sustainability and economic outcomes.

• 
  11:20 AM - 11:45 AM

  Optimisation de la confection des horaires des résidents par modélisation mathématique

  • Amine Bouzid, presenter, CIRRELT-FSA-ULaval
  • Monia Rekik, Université Laval

  Cette recherche propose une méthode d’optimisation pour la confection des horaires des résidents en médecine dans les Groupes de Médecine de Famille (GMF). L’objectif est d’automatiser le processus de planification en remplaçant les procédures manuelles par des modèles de Programmation Linéaire en Nombres Entiers Mixtes (MILP). Le problème est décomposé en plusieurs périodes et blocs, tenant compte de la typologie des résidents selon leur année et leur affectation. Chaque résident doit réaliser un nombre précis d’occurrences pour diverses tâches tout en respectant des contraintes temporelles et de qualification. Les modèles MILP, résolus avec des solveurs tels que CoinBC et CPLEX, garantissent une répartition équitable et équilibrée des tâches. Des tests sur des cas réels, issus d’un partenariat avec la Clinique Maizerets – GMF universitaire, confirment l’efficacité de la solution proposée. Cette approche permet ainsi de réduire la charge administrative et d’améliorer la qualité des horaires, ouvrant la voie à une intégration plus large dans la gestion des effectifs en santé.

• 
  11:45 AM - 12:10 PM

  Nurse Scheduling with built-in Flexibility

  • Flore Caye, presenter, Polytechnique Montréal
  • Antoine Legrain, GERAD - Polytechnique Montréal

  The nurse scheduling problem (NSP) involves assigning nurses to shifts while balancing institutional staffing requirements and individual preferences. Traditional approaches often produce rigid schedules that do not account for unforeseen last-minute constraints faced by nurses. To address this, we introduce novel flexibility mechanisms: shift flips and nurse swaps.

  A shift flip allows a nurse to interchange a working day with a day off, provided that the new schedule remains feasible. A nurse swap extends this concept by allowing two nurses to exchange assignments, ensuring mutual benefit without compromising staffing needs.

  We first solve the NSP without flexibility using a Column Generation algorithm to generate individual rosters. Because flips and swaps have to be mutually beneficial, their pricing cannot be embedded in the Column Generation framework.

  Then, a preprocessing step lists for each roster all the feasible flips and swaps. We formulate a mixed-integer programming (MIP) model, composed of the column generation restricted master problem to which is added a set of flexibility constraints. The model ensures a minimum level of swap opportunities while optimizing overall scheduling costs and respecting demand constraints.

  We propose different swap pricing strategies and analyse the balance between flexibility and operational costs. Experimental results demonstrate the possibility of incorporating flexibility without being detrimental to operating costs.

  By embedding these concepts into scheduling frameworks, we provide a practical solution for hospitals and healthcare institutions seeking to improve both efficiency and staff well-being.