Simon Tamayo

Simon Tamayo, Thibaud Monteiro

This study explores the interactions between order sizes, batch sizes and potential product recalls within a FIFO assignment policy. Evidence is provided that the extent of a product recall is related to the fragmentation of the batches of input materials as it amplifies the impact of a crisis. A new management indicator is proposed in order to quantify the expected number of fragments composing a customer order FrBO. A probabilistic analysis reveals that for a given likelihood of crisis, the presence of different batches in a customer order will largely increase its risk. Accordingly, a new equation is proposed for calculating the expected recall size. Taking into account the fragmentation measure allows, for the first time, for the integration of a proactive product recall policy in the batch sizing decision process. A Monte Carlo simulation is performed to validate the effectiveness of this approach.

Simon Tamayo, François Combes, Gaudron Arthur

City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analysing social media content could help in understanding the public perception of City logistics. This paper offers a methodology for collecting content from Twitter and implementing Machine Learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.

Salma Benslimane, Simon Tamayo, Arnaud de La Fortelle

Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Classical methods for counting vehicles use mechanical, electromagnetic or pneumatic sensors, but these devices are costly, difficult to implement and only detect the presence of vehicles without giving information about their category, model or trajectory. This paper proposes a Deep Learning tool for classifying vehicles in a given image while considering different categories of logistic vehicles, namely: light-duty, medium-duty and heavy-duty vehicles. The proposed approach yields two main contributions: first we developed an architecture to create an annotated and balanced database of logistic vehicles, reducing manual annotation efforts. Second, we built a classifier that accurately classifies the logistic vehicles passing through a given road. The results of this work are: first, a database of 72 000 images for 4 vehicles classes; and second two retrained convolutional neural networks (InceptionV3 and MobileNetV2) capable of classifying vehicles with accuracies over 90%.

Augustin Lombard, Simon Tamayo, Frédéric Fontane

This paper presents an open data approach to model and solve the vehicle routing problem with time-dependent travel times (TDVRP). The proposed model is based on a multi-layer matrix composed of travel times, replacing the traditional distance matrix. Online cartography services are queried in order to build this matrix. Travel times are obtained for every step in the time discretization. Thus, the model integrates the fact that the travel time between two points is modified during the time horizon. This model is applied to a medium-sized problem in the urban area of Paris using an enhanced Greedy Randomized Adaptive Search Procedure (GRASP). This work intends to build on the current state of the art by proposing a straightforward and open-access method to model and solve the VRP with traffic variability.