2025

Heure:	10:30 - 11:30
Lieu:	Salle B107, bâtiment B, Université de Villetaneuse
Résumé:	A Bi-level Approach for Last-Mile Delivery
Description:	Maria Elena Bruni Last-mile delivery is regarded as an essential, yet challenging problem in city logistics. One of the most common initiatives, implemented to streamline and support last-mile activities, are satellite depots. These intermediate logistics facilities are used by companies in urban areas to decouple last-mile activities from the rest of the distribution chain. Establishing a business model that considers different stakeholders' interests and balances the economic and operational dimensions, is still a challenge. In this seminar, we will introduce a novel problem that broadly covers such setting, where the delivery to customers is managed through satellite depots and the interplay and the hierarchical relation between the problem agents are modeled in a bi-level framework. Two mathematical models and an exact solution approach, properly customized for our problem, will be presented, and extensive computational experiments on benchmark instances and a real case study discussed. Finally, we will shed light on future research directions on how the proposed approach can be extended for other relevant problem classes.

Heure:	10:30 - 11:00
Lieu:	Salle A303
Résumé:	Decision aid for tactical transportation problems
Description:	Guillaume Joubert Due to the complexity of real-world planning processes addressed by major transportation companies, decisions are often made considering subsequent problems at the strategic, tactical, and operational planning phases. However, these problems still prove to be individually very challenging. This talk will present two examples of tactical transportation problems motivated by industrial applications: the Train Timetabling Problem (TTP) and the Service Network Scheduling Problem (SNSP). The TTP aims at scheduling a set of trains, months to years before actual operations, at every station of their path through a given railway network while respecting safety headways. The SNSP determines the number of vehicles and their departure times on each arc of a middle-mile network while minimizing the sum of vehicle and late commodity delivery costs. For these two problems, the consideration of capacity and uncertainty in travel times are discussed. We present models and solution approaches including MILP formulations, Tabu search, Constraint Programming techniques, and a Progressive Hedging metaheuristic.

Heure:	10:30 - 11:30
Lieu:	Salle B107
Résumé:	A probing-enhanced stochastic programming approach for the capacitated multi-item lot-sizing problem.
Description:	Franco Quezada In traditional stochastic programming, decisions are made based on known probabilities or distributions of uncertain parameters. However, in real-world scenarios, decision-makers often have opportunities to gather additional information about these uncertainties through a process known as probing. Probing allows for observation of certain random variables, which can provide valuable insights into the behavior of related uncertainties. However, probing is not free—it involves a cost that must be taken into account in the decision-making process. This cost could represent financial expenditure, time, or resource allocation necessary to gather data or perform exploratory actions. Thus, probing involves taking actions or gathering data to learn more about the uncertain variables before making the final decision. In two-stage stochastic programs, this can mean performing certain preliminary actions (probing decisions) that help to reveal more information about future states (first and second-stage decisions). We investigate a probing-enhanced stochastic programming approach for the two-stage stochastic multi-item capacitated lot-sizing problem, which is a classic inventory management problem where decisions are made in two stages to minimize costs while considering uncertain future demand. Two-stage problems, except for very simple models, are generally intractable to solve exactly. Even with complete knowledge of the demand distribution, explicitly integrating the second-stage costs is computationally prohibitive. A common approach to address this complexity is the Sample Average Approximation (SAA) method, which approximates the expectation by sampling from the original distribution to create a finite set of scenarios. Then we adopt a non-anticipative formulation that enables us to ensure that decisions are consistent with the information available at the time of decision-making. The critical aspect of this approach is that the enforcement of non-anticipativity conditions depends on the decisions themselves. Thus, the resulting model includes a vast number of conditional non-anticipativity constraints, proportional to the square of the number of scenarios. This leads to a mixed-integer linear programming (MILP) formulation characterized by a large number of big-M constraints, which can be challenging to solve efficiently. We propose a decomposition approach to solve the resulting non-anticipative formulation, exploiting the structure of the problem by breaking it into smaller, more manageable sub-problems that can be solved efficiently, providing a more practical and scalable solution approach. Preliminary computational results demonstrate that the proposed decomposition algorithm significantly outperforms the other approaches in terms of both solution quality and computation time, achieving improvements by several orders of magnitude. Joint work with Céline Gicquel, Safia Kedad-Sidhoum and Bernardo Pagnoncelli.

Heure:	10:30 - 11:30
Lieu:	Salle C212
Résumé:	Designing sustainable diet plans by solving tri-objective 0-1 programs
Description:	Marianna De Santis We present an algorithm for triobjective nonlinear integer programs that combines the eps-constrained method with available oracles for biobjective integer programs. We prove that our method is able to detect the nondominated set within a finite number of iterations. Specific strategies to avoid the detection of weakly nondominated points are devised. The method is then used to determine the nondominated solutions of triobjective 0-1 models, built to design nutritionally adequate and healthy diet plans, minimizing their environmental impact. The diet plans refer to menus for school cafeterias and we consider the carbon, water and nitrogen footprints as conflicting objectives to be minimized. Energy and nutrient contents are constrained in suitable ranges suggested by the dietary recommendation of health authorities. Results obtained on two models and on real world data are reported and discussed. Coauthors: Luca Benvenuti, Alberto De Santis, Daniele Patria