4 présentations

• 
  10h30 - 10h55

  Production routing problem with simultaneous pickup and delivery

  • Kenza Alioui, prés., AOTI ESG UQAM
  • Matthieu Gruson, UQÀM - CIRRELT
  • Franklin Djeumou Fomeni, Universite du Quebec a Montreal

  This work presents a new Production Routing Problem (PRP) variant in a two-echelon supply chain with multiple production plants, distribution centers (DCs), and retailers. We integrate the reverse logistics by collecting recyclable packaging from retailers to plants, aiming to minimize total costs, including production, inventory holding, and transportation costs, over a multi-period horizon.
  The problem involves two transportation layers: from plants to DCs and from DCs to retailers. Each layer is modeled as a Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPPD) using a heterogeneous vehicle fleet, where vehicles distribute products and collect packaging. Retailers’ demand and packaging return quantities are predetermined, allowing for proactive planning of production, inventory, and transportation.
  Inventory management is considered at all stages of the supply chain, with capacity and holding costs applied to all facilities. Each plant produces a single product, allowing DCs to be visited multiple times per period, whereas retailers are visited at most once per period.
  In this study, we aim to develop a mixed integer linear programming (MILP) model and solution methods to address this complex problem, integrating production, inventory, distribution, and routing problem with simultaneous pickup and delivery and reverse logistics.

• 
  10h55 - 11h20

  Integrated maintenance preventive and production: degradation and cost analysis

  • Harcènage Dansou, prés., ESG-UQÀM
  • Matthieu Gruson, UQÀM - CIRRELT
  • Francois Lamothe, UQAM
  • Julien Legavre, ESG-UQÀM

  Preventive maintenance and production operations are two major activities in the manufacturing industry. On one hand, production operations are planned to produce multiple items while managing available stock to meet customer demand over a time horizon. On the other hand, preventive maintenance operations are planned for the same time horizon to prevent production equipment from breaking down suddenly. Recent research has shown that integrating preventive maintenance and production offers an opportunity to reduce costs and better coordinate the components of both activities.
  Our research is part of this approach, proposing a mathematical model for a problem that integrates preventive maintenance with the capacitated lot-sizing problem, which we call CLSPM. We consider a single-machine production line capable of producing multiple items over a time period with limited production capacity. This machine degrades gradually when production activity takes place, and this degradation is modeled as a reduction in its available capacity. The proposed model is a mixed integer linear program in which we consider three types of degradation: exponential, linear, and Dirac-type punctual degradation.
  To solve CLSPM, we use the Gurobi optimization solver on randomly generated instances of the problem. Several levels of degradation and maintenance costs (low, medium, and high) are considered in the experiments. First, we analyze the solver's performance by observing the resolution time, the optimality gap, and the number of nodes explored. The results show that when maintenance costs are high, some instances of the problem become difficult to solve, to the point that an optimal solution cannot be found in less than two hours. Next, an analysis of the impact of different types of degradation on the total cost shows that a high level of machine degradation leads to an increase in operational costs, regardless of the type of degradation considered. However, exponential degradation proves to be the most costly.

• 
  11h20 - 11h45

  A neighborhood search-based matheuristic for poultry production-distribution optimization.

  • Samuel Gbeya, prés.,
  • Maryam Darvish, Université Laval
  • Jacques Renaud, Université Laval, CIRRELT

  This study describes models and solves a production and distribution planning problem in the poultry supply chain. Production planning comprises the number of chicks to be raised on each farm, the start time and the duration of their breeding. The delivery schedule focuses on the timing and the number of chickens to be delivered to each slaughterhouse in each period, considering the transportation cost and the allocations of the quota. This paper aims to develop efficient production and delivery schedules that reduce the total production and distribution costs while meeting the numerous constraints of the industry. We first model the problem as a mixed-integer linear programming formulation and propose a neighborhood search-based matheuristic to solve real-size instances of the problem. The computational results on instances generated based on a Qu{\'e}bec poultry producer prove the efficiency of the proposed algorithm.

• 
  11h45 - 12h10

  Heuristics for the one-warehouse multi-retailer problem

  • Félix Houdouin, Polytechnique Paris
  • Marilène Cherkesly, ESG UQÀM
  • Matthieu Gruson, prés., UQÀM - CIRRELT

  In this work, we address the One Warehouse Multi-Retailer (OWMR) problem. In the OWMR, a central warehouse replenishes multiple retailers that face a dynamic and deterministic demand for a single item over a discrete and finite time horizon. We design several simple yet efficient heuristics to solve the problem. The heuristics we designed are significantly faster than the best-known exact methods, with the most effective among them achieving feasible solutions that typically have costs less than 1% higher than the optimal solution, while being a hundred times faster. Our heuristics leverage the uncapacitated lot sizing and two-level uncapacitated lot sizing algorithms extensively. We introduce heuristics with low polynomial complexity, specifically O(NT^2 log(T)) and O(NT log(T)) where N and T denote the number of retailer and time periods, respectively. Additionally, we newly introduce a powerful process to improve any feasible solution in almost-linear time. While most of our heuristics do not have theoretical guarantees on their approximation ratios, we propose a dynamic method to estimate an approximation guarantee using a heuristic to compute a lower bound. This approach typically yields approximation guarantees around 1.4, which is tighter than the current best-known constant-approximation heuristic that has an approximation guarantee of 1.8.