Energetic reasoning is an efficient filtering technique for the Cumulative Scheduling Problem. In this paper we propose a new definition of the energy balance of intervals, together with a new checker that is more accurate for each interval. Our approach involves solving a tripartition problem. For checking the intervals, we also propose a cubic algorithm leveraging our approach. We report computational results that confirm that it is more efficient than the classical approaches.

Evaluating railway capacity is a challenging task due to the high complexity of the railway system, which involves various critical interdependent components. This paper presents an innovative approach to assessing the capacity of a railway node by iteratively evaluating the ability of its infrastructure to cover different combinations of train line frequencies. For each combination, an optimization problem is formulated to assess the node’s capacity by verifying whether a feasible timetable can be built, possibly introducing deviations with respect to a strictly cyclic timetable. To assess such feasibility, we consider a microscopic representation of the infrastructure. For this purpose, a mixed-integer linear programming model is utilized, allowing minor adjustments in train arrival and departure times while aiming to minimize the cumulative deviation from a strictly cyclic timetable. An experimental study conducted on the Pierrefitte-Gonesse railway node in France demonstrates the ability of our approach to quantify capacity under different combinations of line frequencies. In addition, the approach allows for the highlighting of how local rerouting can enhance capacity utilization.

The Société de transport de Montréal (STM), a public transport agency in Montreal (Quebec, Canada), has committed to electrifying its entire vehicle fleet as part of its broader sustainability initiatives. This research addresses a critical challenge that arises from this commitment: optimizing the deployment and dispatching of electric service vehicles assigned to operations supervisors to ensure minimal disruption to the bus network. The problem is formulated as a mixed-integer linear program that minimizes the number of deployed vehicles and total response time using a lexicographic approach. The effectiveness of our approach is evaluated through computational experiments using the commercial CPLEX optimization solver. This research provides the STM with a valuable strategic and operational decision-support tool, aimed at optimizing the size and deployment of its new electric vehicle fleet, therefore supporting its sustainability objectives.

A recent surge in interests regarding Digital Twins in the networking community has emphasized its potential across various applications, as of the useful big data it contains. One of these studied applications involving 3D Digital Twins of buildings is the optimization of indoor IoT networks deployments. Indeed, maximizing the coverage of the deployed devices while minimizing their number and fitting technical constraints is a tedious problem, particularly in complex environments. In this paper, we propose a detailed analysis of the impact of using the Digital Twin by comparing the effects of incorporating it to optimize IoT Networks deployments against scenarios where it is not utilized. We first present the impact of the 3D Digital Twin on the environment and channel modeling, before providing a performance evaluation of Quality of Service (QoS) using a deployment optimization method based on a genetic algorithm, conducted using numerical and simulation tools on two different environments, contrasting scenarios with and without the use of the Digital Twin onto two different buildings. The obtained results show that leveraging the Digital Twin yields enhanced QoS in terms of average quality of link and coverage rate, while providing new solutions not discerned in the alternative environment within the context of a challenging technical constraint.

Facilitated by the widespread adoption of Internet of Things (IoT) networks, Location-based services (LBS) have emerged as a new type of services, requiring a high quality of service (QoS) and to provide access to all devices within predefined zones of interest. This is made possible via specific IoT Networks architectures based on the Software Defined Network paradigm. To address the challenge of unstable IoT networks management, where devices can move, appear, or vanish unpredictably, we propose a novel architecture based on a selection process of dominant devices acting as gateways, ensuring continuity of service. We investigate two selection processes, respectively based on Connected Dominating Sets and Deep Q-Network techniques. The objective of this method is to optimize energy consumption while providing high QoS and extending network access to offline devices within predefined zones of interest. In order to evaluate the performance of the proposed architecture with different selection processes, we conducted experiments using emulation tools allowing communication mode demand generations. The metrics used were the proportion of dominant devices, the energy consumption savings, the quality of service and the network extension to offline devices. Ultimately, we present a recommendation concerning the selection process based on the needs of the system.

With the expected increase in railway transportation demand, the scheduling of preventive maintenance activities is becoming a critical issue in many industrial sectors, particularly for rolling stock. To address this difficult problem, we proposed an optimisation of maintenance scheduling based on reliability aspects, in order to accurately assess the cost of maintenance by also integrating the cost of corrective maintenance. Scheduling consists of determining the optimal date, for each maintenance activity associated with a specific component, when a train unit switches from operational to maintenance status to perform the activity. Furthermore, the proposed modeling considers the condition of the train when it should be out of service due to exceeding the imposed maintenance date. The maintenance schedule also considers the capacity limits of the maintenance centers as well as the resource requirements of the activities. The proposed formulation is based on an integer linear programming (ILP) approach, which is solved on randomly generated instances using a commercial solver.

Wireless Camera Sensor Networks (WCSN) have emerged as a promising technology in recent years, due to their extensive applications in various fields, including critical contexts such as evacuation. However, optimizing a WCSN layout to reduce its cost while maximizing coverage is tedious. Cameras can be interconnected thus may require connectivity, and the evacuation context requires full-view of the targets and zones of interest to cover in priority. In this paper, we propose a new method for optimizing WCSN indoor layouts under k-connectivity constraint while considering full-view requirement and predefined zones of interests. This method utilizes an ISO 23601 evacuation plan as input to generate grid environments and an Evacuation-Oriented Multigraph. This allow to model the full-view requirement and build a relevant set of initial solutions for a Pareto-based multi-objective genetic algorithm, which computes the optimized WCSN layout. The performance evaluation is conducted using numerical and simulation tools on scenarios with different connectivity constraints and fields of view. The obtained results show that the proposed solution outperforms existing solutions and random layouts in terms of coverage.

Unmanned Aerial vehicles (UAV) are a cost-effective and flexible alternative to bring computing to the edge network as close as possible to end devices. Nevertheless, UAV’s capabilities are also limited. Considering the facts that UAVs are generally deployed in cases where the terrestrial networks are congested, damaged or absent, and that end devices’ mobility and traffic distribution directly affects UAV’s load and performance, we investigate in this paper UAV’s cooperation in a cluster of UAVs. Therefore, we propose the use of the cluster head for the computing offloading scheduling and balancing using a minimum-cost flow algorithm and present an objective function for a reliable offload path selection.

IoT networks allow various new types of services, such as Location-Based Services (LBS), requiring an end-to-end Quality of Service (QoS) without any interruption. However, sustaining these networks is difficult when the devices can move and vanish at any time, as they can be carried around, might run out of battery, or be turned off by users. In this paper, we propose a new programmable multitier architecture and a continuity of service protocol named ConSerN, which are based on the Software Defined Network (SDN) approach and Connected Dominating Sets (CDS) techniques. Unlike the existing solutions, the proposed mechanism considers a dynamic topology where nodes are not static from a mobility and topology point of view. Furthermore, as most of the applications using LBS need to consider the geographic position of the connected objects, we have added it in the selection process of the dynamic gateways. The performance evaluation was conducted using experimentation and emulation tools allowing scenario generations. We used three main metrics i.e. the quality of link, the number of dominant nodes and the amount of control messages. The obtained results show that the proposed solution benefits from a high number of gateways to achieve great performance in terms of overhead and quality of link.