The Function-as-a-Service (FaaS) paradigm supports many Cloud-native applications, but rising demand for low-latency services exceeds what the Cloud alone can deliver. Edge computing addresses this limitation; however, its heterogeneity and fragmented administrative domains, together with the workload dynamicity, greatly complicate resource coordination and function replica allocation across the Edge-Cloud continuum. This paper addresses these challenges through two complementary contributions. First, we formalize the Function Replica Allocation and Load Balancing (FRALB) problem in the Edge-Cloud continuum as a Distributed orchestration problem referred to as DiFRALB. The formulation jointly optimizes function placement, horizontal offloading among Edge nodes, and vertical offloading toward Cloud resources. Computation offloading plays a central role in this setting, as it enables workloads to be distributed across heterogeneous Edge and Cloud infrastructures while meeting performance requirements. Second, recognizing that centralized orchestration approaches suffer from scalability limitations and raise privacy concerns in multi-stakeholder environments, we propose FaaS-MACrO, a distributed multi-agent orchestration architecture designed to solve the DiFRALB problem. In FaaS-MACrO, each Edge node operates as an independent agent making local decisions, while a lightweight coordinator ensures global consistency by iteratively updating offloading prices to resolve conflicts among neighboring nodes. Crucially, coordination requires only minimal information exchange, thereby preserving operational privacy. Our solution approach jointly optimizes processing and offloading decisions by capturing the trade-offs among local execution efficiency, horizontal offloading latency, and vertical offloading costs. Extensive experiments across heterogeneous node configurations, diverse network topologies, and varying function characteristics demonstrate that FaaS-MACrO achieves solutions within 0.03-12.14% of the centralized optimum on average while significantly improving scalability, reducing solution times by up to three orders of magnitude in large-scale deployments with 200 nodes.

In this paper, we study the vehicle routing problem (VRP) for a fleet of cooperative autonomous agricultural robots (agribots) equipped with detachable implements, with the goal of efficiently and sustainably completing agricultural tasks in precision crop farming. State of the art in the area of agribot fleet routing with detachable implements is lacking. Consequently, we propose the Capacitated Agriculture Fleet Vehicle Routing Problem with Implements and Limited Autonomy (CAFVRPILA), designed to optimize the agribot fleet's routes across a set of given agricultural tasks while considering implement capacities, agribot-implement compatibilities, and agribots' limited battery autonomies. A heuristic two-phase decomposition approach is proposed for this problem. Simulation experiments show that minimizing travel distances and costs with CAFVRPILA enhances sustainable farming while maximizing productivity and resource use. The results also demonstrate that synchronizing multiple operations improves efficiency, particularly in larger fleets.

International audience

This paper proposes the Dynamic and Cooperative Multi-Agent Task Allocation (DC-MATA) problem, focusing on individually rational agents in a cooperative organization, which allocate dynamically changing tasks over time. DC-MATA aims at dynamically improving Nash equilibrium over time through learning in this context. Task utilities evolve dynamically, and learning, conducted in rounds, optimizes agents' task selection order to enhance system performance. Our proposed DC-MATA solution approach assigns agents to tasks with highest utility over time and tends towards the Nash equilibrium that aligns with agents' self-interest while improving the gap with system optimum. We propose priority-sensitive reward function and four action sampling algorithms (ε-greedy, ε-decay, Adapted Simulated Annealing, and Prior Sequence-Aware Sampling - PSAS) leveraging a Markov decision process (MDP) framework. Simulation experiments on our newly proposed GitHub benchmark instances confirm robust performance, facilitating efficient task allocation in the DC-MATA scenario.

In this paper, we propose and formulate the static and time-extended one-on-one multi-agent task allocation problem with implements and limited autonomy (MATAILA). The objective is to assign tasks to a team of agents statically or over a receding time horizon, while minimizing the overall multi-agent team's cost of performing the tasks and the penalty cost for unaccomplished tasks, all while maintaining sufficient battery level across the team. The basis of the studied problem is the (static one-on-one) axial 3-index assignment problem with the extensions on the time horizon and agents' autonomy. Time-extended MATAILA is a computationally expensive problem, that we simplify by a static MATAILA which focuses only at the tasks pending in the present period and is myopic towards the tasks appearing in the future. We compare the performance of the proposed models in scenarios where all tasks are known a priori. We analyze the performance and scalability of the two approaches experimentally in simulations and show their efficiency in dynamically changing scenarios.

This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time.

In crowdsourced delivery organizations, where individual vehicles with shared common goals may have conflicting individual interests, the preference is for collaboration over competition, provided it is less costly. However, achieving a balance between the efficiency of individual vehicles and the overall fleet poses a challenge. This paper introduces a novel Vehicle Routing Problem with Fair Profits and Time Windows (VRP-FPTW), which aims to meet customer demand and stringent time windows while maximizing the profit of the worst-off vehicle in the fleet. We propose a centralized and distributed vehicle routing model for this problem, both with quality of solution guarantees. The distributed approach is tailored for multi-agent systems relying on a coordination mechanism where each vehicle modeled as an individually rational agent finds its route autonomously in coordination with a fleet coordinator agent, without sharing its private information. The objective of a vehicle agent is to maximize its own profit while following the fleet's norms and regulations based on shared values. Simulation experiments provide compelling evidence of the robustness and scalability of the proposed distributed approach, showcasing significant enhancements in both solution quality and computational efficiency, particularly when dealing with larger vehicle fleets.

Quality of emergency services depend on the effectiveness of emergency vehicle fleets (e.g., police, fire trucks, and ambulances). Traditionally, these fleets are composed of emergency crews that must attend highly stochastic incident demand within a short target arrival time. Incidents must be attended immediately, which prevents that an a priori computed work break schedule is executed as planned and many crews may be left without a break during prolonged periods of time. Extended focused work periods decrease efficiency with related decline of attention and performance. Therefore, break schedule should be regularly updated on the fly to allow frequent and sufficiently long time for rest. In this paper, we propose a dynamic algorithm for on the fly work and break balancing for crews in emergency fleets. Based on the historical intervention data, the algorithm (re)arranges vehicles’ crews’ work breaks as the time evolves considering individual crews’ preferences. Moreover, it dynamically reallocates stand-by vehicles for improved coverage of a region of interest. We show the performance of the proposed algorithm on two simple functional examples.