4 Presentations

• 
  10:30 AM - 10:55 AM

  Vehicle Routing Problem with Occasional Drivers for E-Commerce Last-Mile Delivery: A Metaheuristic Approach

  • Matheus Meirim, Pontifícia Universidade Católica do Rio de Janeiro
  • Rafael Martinelli, presenter, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)

  The last-mile vehicle routing problem, propelled by the exponential growth of online shopping, poses significant logistical challenges impacting consumer choices. Integrating occasional drivers has emerged as a strategy to enhance route efficiency and reduce delivery costs. This study extends the Vehicle Routing Problem with Occasional Drivers by incorporating a heterogeneous fleet and assigning occasional drivers multiple deliveries. Leveraging an Iterated Local Search metaheuristic, we aim to optimize parcel routing for a prominent Brazilian e-commerce company. Computational experiments using actual delivery data reveal the algorithm's superiority over the current commercial routing solution. Integration of occasional drivers consistently improves routing efficiency, reducing delivery times and costs. From 121 real-world instances, ranging from one to 344 vertices, drawn from actual customer deliveries, our proposed algorithm outperforms the commercial routing solution in 53.72% of cases. Additionally, incorporating occasional drivers into the fleet enhances routing times in 76.03% of instances and reduces average routing costs by 10.30%

• 
  10:55 AM - 11:20 AM

  Flexible Park-and-Loop Routing Problem with Parking Availability

  • Panca Jodiawan, presenter, Université Laval
  • Jean-François Côté, Université Laval
  • Leandro C. Coelho, Université Laval

  In order to cope with the increasing complexity experienced in last-mile delivery operations, we address the Flexible Park-and-Loop Routing Problem with Parking Availability. Two types of flexibility are considered: (1) customer-centric flexibility, i.e., multiple delivery locations with specific time windows for each customer, and (2) system-centric flexibility, i.e., parcel lockers as alternative delivery locations and compensations for customers who receive early or late deliveries. These features are incorporated in the city logistics context where deliveries are conducted by the combination of driving and walking modes, the latter requiring a driver to park the vehicle at a selected parking location. Multiple time windows are considered at each parking location and the parking activity must occur within a selected time window. To solve the problem, we propose a Mixed-Integer Linear Programming Model and devise a Hybrid Large Neighborhood Search (HLNS) algorithm with problem-specific destroy and repair operators and a tailored dynamic programming for finding high-quality configurations of driving and walking modes. A classical Set Partitioning Problem is solved within the HLNS periodically in order to further improve the solution quality. The effectiveness of HLNS is demonstrated on a set of newly generated instances and two special cases existing in the literature.

• 
  11:20 AM - 11:45 AM

  Optimization of team orienteering problem by considering fairness – A case study in tourist trip design

  • Mansoureh Hasannia Kolaee, presenter, Université Laval

  The tourism industry is a key driver of economic growth and contributes to the achievement of sustainability goals. This study presents a multi-objective group tourist trip design problem that is categorized as a team orienteering problem. The proposed model minimizes total cost and environmental impacts while maximizing the total collected prizes from tourists' interests. We introduce the lost profit opportunity for the cost of tours from an economic perspective for the first time. Due to the complexity of this problem, we develop a metaheuristic algorithm to solve the proposed multi-objective group tourist problem. Extensive analyses and computations are conducted to demonstrate the performance of the proposed multi-objective optimization model and algorithm in solving large-scale instances. The results demonstrate the effectiveness of our approach in aiding tourism managers to make decisions and balance objectives.

• 
  11:45 AM - 12:10 PM

  Online Stochastic Optimization for Real-Time Transfer Synchronization in Public Transportation Networks

  • Laura Kolcheva, presenter,
  • Antoine Legrain, Polytechnique Montréal
  • Martin Trépanier, Polytechnique Montréal, CIRRELT

  Transfer speed and protection are critical factors that influence passengers' willingness to use public transportation. Due to unpredictable traffic patterns, fixed transfer schedules may not always align. This work proposes two online stochastic optimization algorithms for the transfer synchronization problem using the hold, skip-stop, and speedup tactics. First, we design an offline arc-flow model using time-expanded graphs to enumerate all possible tactics. The model minimizes total passenger travel time by reducing transfer times. Then we implement two online stochastic optimization algorithms based on the offline model in a discrete-event dynamic environment. Decisions are made based on predictions of the future state of the bus network using sampling to generate scenarios from historical and real-time data. The performance of the algorithms is compared using a real dataset from the public transit system of Laval, Canada. The results show significant improvements in both the number of successful transfers and total passenger travel time across 29 bus lines. This supports the practicality of using online stochastic optimization algorithms to solve the transfer synchronization problem in real-world transportation systems.