SPEAKERS
Claudia Archetti (ESSEC Business School)
Biography:
Since September 2021, Claudia Archetti is Full Professor in Operations Research at ESSEC Business School in Paris. The main areas of the scientific activity are related to combinatorial optimization problems, with a focus on transportation and logistics. Claudia Archetti has carried out the scientific activity in collaboration with Italian and foreign colleagues and published joint papers with some of the best researchers at the international level. She is author of more than 100 papers in international journals. She is co-Editor in Chief of Networks. She is Associate Dean of Chairs at ESSEC.
Presentation Title:
"The Freight on Transit problem: a novel strategy for urban parcel delivery"
Abstract:
We consider a delivery system for last-mile deliveries in urban areas based on the use of Public Transport Service. The idea is to exploit the spare capacity of public transport means to transport parcels within urban areas, thus reducing externalities caused by commercial delivery vans. Specifically, the system is such that parcels are first transported from origins to drop-in stations on public vehicles itineraries. Then, they are transported through public vehicles to drop-out stations, from where they are delivered to destination by freighters using green vehicles. The system is known as Freight-On-Transit (FOT). We present optimization problems related with strategic, tactical and operational decision levels, as well as ad-hoc solution methodologies and simulations on synthetic data.
Diego Cattaruzza ( Univeristé de Lille)
Biography:
Diego Cattaruzza works as Associate Professor of Operations Research and Informatics at Centrale Lille, a leading engineering school in France. He received a master degree in Mathematics from the Universtity of Udine (Italy) and holds a phD in Industrial Engineering from Ecole des Mines de Saint-Étienne (France). He joined Centrale Lille in 2015 and entered the Cristal laboratory, attached to the French National Center for Scientific Research (CNRS). He is also part of the INOCS team at the Inria centre at the University of Lille. His research interests concern, among others, the new practices in logistics with particular focus in vehicle routing and warehouse optimization.
Presentation Title:
"Synchronized Deliveries with a Bike and a Self-Driving Robot"
Abstract:
Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call Traveling Salesman Problem with Bike-and-Robot (TSPBR) where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.
Joint work with: Yanlu Zhao, Ningxuan Kang, Roberto Roberti
Elena Fernandez (Universidad de Cádiz)
Biography:
Elena Fernandez is a professor in Operations Research. She has spent much of her academic career at the Universitat Politècnica de Catalunya in Barcelona; since 2019 she is affiliated to the University of Cádiz. Her research interest focuses on mathematical optimization models for discrete optimization, mainly on applications for transportation and logistics involving discrete location, network design and vehicle routing. She has published scientific papers in the flagship OR journals, with about 70 co-authors from a dozen of different countries.
Presentation Title:
"The Multi-Commodity Flow Problem with Outsourcing Decisions"
Abstract:
In recent years, there has been a significant increase in the outsourcing of various practices. This trend has been particularly prominent in the logistics sector, where it encompasses activities like last-mile delivery and full integration with external operators, including 3-PL logistics partners. Similarly, the airline industry has also experienced outsourcing in processes such as check-in, luggage management, cabin crew and even flights, among others. The outsourcing of these processes offers several advantages, including enhanced flexibility and a reduced dependency on hiring and training specialized staff. Given the increasing prevalence of this trend, it is crucial to study and model this type of situation from a network design perspective to gain a better understanding of how the outsourcing decisions contribute to the overall revenue of a major firm when it faces such a process. It is equally important to consider the companies' viewpoint as they too aim to optimize their revenues. The objective of this research is to address the transportation of demand between different origins and destinations (commodities) when the major firm, referred to as the Leader, already possesses a hub network of major hubs and chooses to outsource the demands coming from and going to the remaining non-hub locations using third-party companies (carriers) in order to maximize its overall profit. We model this problem as a Bi-Level Mixed Integer Non-Linear Programming (MINLP) model, which we subsequently linearize to obtain a Bi-Level Mixed Integer Linear Programming (MILP) formulation. Leveraging the inherent properties associated with the independence of assignments and costs of each carrier, we discretize the outsourcing cost decisions, enabling us to express the model as a Single-Level MILP. The costs and solutions derived from solving this model are shown to be bi-level optimal. Computational results demonstrate that we are able to solve instances of 200 nodes and 6 carriers to optimality within one hour. These findings provide motivation for studying more complex systems in the future.
Roberto Wolfler-Calvo (Université Sorbonne Paris Nord)
Biography:
Roberto Wolfler-Calvo received his degree at "Politecnico di Milano" where he did also the Ph.D. Then he did the post-doc at the Joint Research Centre (JRC) of the European Commission located in Ispra (Italie). He spents one year more in Ispra as a temporal agent of the European Commission. In 2001 he joined the LOSI team at the University of Troyes as Maître de conférences and at the same time, he was lecturer at Politecnico di Milano, INDACO Departement. In September 2008 he joined LIPN as a full professor of Operations Research. He is leader of the AOC team. His main research interests are Combinatorial Optimisation, Mixed Integer Programming, Reformulation and Decomposition Methods, Vehicle Routing and Scheduling Problems, Design and Analysis of Combinatorial Algorithms, Metaheuristics, Reoptimization, Environmental Decision Support Systems. He is interested in both Academic research as well Industrial applications.
Presentation Title:
"Improving the Demand-Responsive Transport System by combining Machine Learning and Optimization"
Abstract:
The main goal of urban collectivities to reduce CO2 emissions implies changing in the two following domains of logistic: last miles of transport of merchandises and last miles of transport of people. In this context Demand-Responsive Transport (DRT) has grown over the last decade as an ecological solution to both metropolitan and suburban areas. It provides a more efficient public transport service in metropolitan areas and satisfies the mobility needs in sparse and heterogeneous suburban areas. Traditionally, DRT operators build the plannings of their drivers by relying on myopic insertion heuristics that do not take into account the dynamic nature of such a service. In this talk we present a survey of three differents aspects we have taken into account working with a famous On-Demand Transport's company.
First of all, we present on enhancing an existing optimization framework used by the company with a data-driven metaheuristic. Our objective is to produce a more flexible dispatch of the vehicles during the offline optimization phase based on historical data and supervised learning methods. In order to achieve this target, we designed a metamodel based on Artificial Neural Networks that approximate efficiently a simulation framework within a metaheuristic. Then, we investigate in this talk the potential of a Demand Prediction Framework used specifically to build more flexible routes within a Dynamic Dial-a-Ride Problem (DaRP) solver. We show how to obtain a Machine Learning forecasting model that is explicitly designed for optimization purposes. The prediction task is further complicated by the fact that the historical dataset is significantly sparse. We finally show how the predicted travel requests can be integrated within an optimization scheme in order to compute better plannings at the start of the day.