The widespread use of digital technology enables the development of practical solutions to real-life problems. One such challenge is the Pickup and Delivery Problem with Time Windows (PDPTW), an extension of the Vehicle Routing Problem (VRP), in which vehicles transport goods from pickup locations to delivery locations within specific time windows. This research proposes a decentralized approach based on Agent Modeling and an auction-based mechanism to solve the PDPTW. In this model, each courier independently determines its own trajectory, and can keep it as a private information. The main objective is to minimize the number of vehicles used, while each vehicle seeks to reduce its travel distance and serve as many requests as possible. Finally, the results of this approach are compared with those presented in the existing literature, with empirical evidence of the impact of flexibility on fleet size and travel distance compared to centralized methods.

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The task of mining gradual itemsets holds significant importance in pattern mining, particularly when working with numerical data. It involves the discovery of covariations between attributes in the form of “The more/less X,…, the more/less Y,” referred to as gradual itemsets. However, discovering these itemsets remains challenging, partly due to the exponential combinatorial search space involved in large-scale data processing. Consequently, existing algorithms for gradual itemset mining encounter difficulties, such as slow processing speeds, and occasional failures to terminate due to the overwhelming number of candidate itemsets requiring exploration. A large number of candidates is generated, but a large proportion of them turns out to be infrequent once their supports are computed. This paper introduces an approach to streamline this process by efficiently reducing the number of candidates for which support needs to be computed through the introduction of a stricter upper-bound criterion. By circumventing the costly support computation for numerous candidate itemsets, our approach exhibits efficiency in terms of speed when applied to real databases, including large-scale databases that pose challenges for existing algorithms. Furthermore, we establish a connection in terms of pattern coverage between the two principal gradualness semantics commonly employed in the literature.

The building deconstruction field is one of the main generators of waste. Although recovery techniques exist to valorize this waste, most of it is lost due to the scant management of waste flows. This study aims to model this sector in order to optimize the various flows emanating from buildings undergoing deconstruction and thus improve the overall recovery rate of the induced waste. Modeling of the deconstruction sector by a bi-level problem hinging on a weighted flexible job shop problem (FJSP) evaluated by a non-regular criterion is propounded. A hybrid resolution method based on a genetic algorithm is introduced. A new encoding, taking account of machine idle times, and its associated genetic operators are proposed. Two resolution approaches for the lower-level problem are assessed. Experiments are carried out on simulated but realistic datasets. The first results exhibit the potential of the proposed resolution method.

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The traffic flow measurement is one of the most important components in the traffic management systems. The existing traditional measurement methods are highly time-consuming and costly to continuously gather the required data, such as loop detectors and video cameras. However the travel duration provided by the emerging Floating Car Data (FCD) on Google Maps offers a novel way to estimate traffic flows. Therefore, this work presents a novel multi-model for urban traffic flows by applying a Gaussian Process Regressor (GPR) tuned using machine learning method based on FCD. The FCD on roads, requested through the Google Maps API, only provides information as congestion and travel duration. Traffic flows is estimated with GPR, including different models built by aggregating together data from days sharing similar configuration. The aggregation is performed manually or using unsupervised classification. At last, a series of experiments are conducted to compare the estimated traffic flow and the real one from actual sensors data. The obtained results show that, the proposed modeling can always reproduce and capture the tendency of real traffic flow. The aggregation permits effectively to increase the performance and to conclude on the capability of the approach to replace traditional loop detectors for the measurement of traffic flows.

International audience

In an expected increase of the inland navigation transport demand, the management of the navigation networks requires the design of new optimal management approaches of the water resource. This resource is necessary for the navigation accommodation. In this paper, two dynamic optimization methods are designed with the aim to improve the management of the water resource at the minimal operating cost. They aim at determining the optimal water allocation planning over a future time horizon. They are based on the proposal of a weighted directed flow graph. This flow graph is composed of dynamic capacities on each node and dynamic constraints on each edge. The proposed optimization approaches are tested and compared considering an inland navigation network composed of three reaches.

The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.

Maintaining the network connectivity in mobile Multi-Robot Systems (MRSs) is a key issue in many robotic applications. In our view, the solution to this problem consists of two main steps: (i) making robots aware of the network connectivity; and (ii), making use of this knowledge to plan robots tasks without compromising connectivity. In this paper, we view the ad-hoc network connectivity as an abstraction that is independent from application issues. We propose a new distributed algorithm executing on individual robots to build the connectivity-awareness. The correctness and theoretical analysis of the proposed algorithm are given. We also show how our solution allows checking network bi-connectivity more efficiently than existing work and can be used, for example, during distributed control motion.

Exploration of an unknown environment is one of the major applications of multi-robot systems. Many works have proposed multi-robot coordination algorithms to accomplish exploration missions based on multi-agent techniques. Some of these works focus on multi-robot exploration under communication constraints. In this paper, we propose an original way to formalize and solve this issue. Our proposal relies on distributed constraint satisfaction problems (disCSP) which are an extension of classical constraint satisfaction problems (CSP). Compared to other works, our proposal is fully distributed and guaranties the exploration of an unknown environment with maintenance of connectivity between all the members of a robots' team.

Our system, based on a multiagent framework called collaborative understanding of distributed knowledge (CUDK), is designed with the overall goal of balancing agents' conceptual learning and task accomplishment. The tradeoff between the two is that w