Fleet: Coordination and Optimization

Updated 2 July 2026

Fleet is defined as a coordinated collection of mobile or distributed assets managed as a unified operational unit to achieve collective goals under resource, temporal, or environmental constraints.
Management involves sophisticated mathematical models and algorithms that optimize resource allocation, scheduling, and control strategies in complex, dynamic environments.
Real-world applications span transportation, robotics, federated learning, and distributed computing, demonstrating both rigorous theoretical formulations and practical system implementations.

A fleet is a coordinated collection of assets—generally mobile entities such as vehicles, robots, distributed devices, or workflows—managed as a single operational unit to achieve collective goals under resource, temporal, or environmental constraints. The concept encompasses heterogeneous settings: transportation (vehicles, drones), distributed computing (robot swarms, workflow executions), energy systems (mobile chargers), networked systems (Federated Learning participants), and even astronomical surveys. The management of a fleet involves problems of allocation, control, scheduling, scaling, and adaptation, which are central to a broad range of domains. Below, current research developments in fleet modeling, optimization, and system architecture are reviewed, with references to leading frameworks and representative case studies.

1. Fleet Modeling and Mathematical Formulations

Fleet management and design problems demand precise mathematical abstraction to capture structural, temporal, and operational complexity. In transportation and logistics, a fleet may be a set of heterogeneous vehicles with capacities, operational costs, and specialized features. Typical formulations include:

Vehicle Routing Problem (VRP) and its variants:

$\min \sum_{k \in K} \sum_{(i, j) \in A} c_{ij} x_{ij}^k$

subject to fleet size/mix, routing, capacity, and scheduling constraints, where $x_{ij}^k$ indicates a route leg for vehicle $k$ (Kotsi et al., 2023, Araghi et al., 2 Dec 2025).

Fleet Sizing and Mix: Integer programs jointly optimize the number and types of fleet resources (e.g., $F_t$ for count of type $t$ ), minimizing fixed and variable costs over long horizons with constraints ensuring day-to-day operational feasibility for each route or customer (Bertoli et al., 2017, Araghi et al., 2 Dec 2025).
Stochastic and dynamic settings: For shared mobility (e.g., bike-sharing), the evolution of the number of vehicles at each node is modeled as a (coupled or decoupled) continuous-time Markov process, with ODE approximations providing tractable surrogate models for quality-of-service guarantees (Čáp et al., 2018).
In distributed robotics or computing, the fleet is abstracted as agents or worker processes, each with state, capabilities, and communication interfaces; task allocation is cast as assignment or flow optimization, or as an adaptive policy for workflow steering (Gupta et al., 12 Oct 2025, Pruyne et al., 2024).

The core modeling challenge is balancing the fidelity required by real-world constraints (e.g., time windows, heterogeneity, infrastructure limits) with tractability for large-scale or online optimization (Araghi et al., 2 Dec 2025, Bertoli et al., 2017).

2. Fleet Management Algorithms and Control Strategies

A spectrum of algorithms addresses online, stochastic, and large-scale settings:

Competitive Online Algorithms: In autonomous transit (VIPAFLEET), modes such as "tram," "elevator," and "taxi" are handled by online policies (e.g., "Stop If Requested," "Move Away If Necessary") with provable bounds against the optimal offline solution. Mode switching, adaptive network partitioning, and capacity-aware rules are critical for tracking dynamic demand (Bsaybes et al., 2017, Bsaybes et al., 2016).
Robust and Adaptive Sizing: In vehicle sharing, decoupled ODE models yield per-station searches for vehicle and dock counts that upper-bound failure probabilities system-wide, vastly improving efficiency over naive global balancing (Čáp et al., 2018). Robust scenario aggregation—maximizing over historical demand days—effectively accommodates uncertainty in urban shared mobility (Hua et al., 2022).
Integration with Real-Time Data and Distributed Control: Fleet systems leverage real-time sensor feedback, V2X communications, or distributed scheduler updates to dynamically reallocate resources—critical in smart transportation or workflow-based scientific computing (Kotsi et al., 2023, Pruyne et al., 2024).
Learning-Based Multi-Agent and Transfer Approaches: Coregionalized Gaussian processes enable cross-member transfer learning for fleets of nearly identical agents (e.g., wind farms), yielding significant sample efficiency and robustness over naive aggregation (Verstraeten et al., 2019). LLMs are now employed for high-level goal decomposition, open-world reasoning, and dynamic task allocation in robot fleets (Gupta et al., 12 Oct 2025).

3. Architectures and System Implementations

Fleet management platforms reflect this algorithmic diversity in their system designs:

Microservices and Containerization: Modular designs (CTMaaS, RobotFleet) decompose fleet operations into specialized, independently scalable services—planning, execution, communication—that interact via standardized APIs and data stores. This enables flexible scaling, integration with legacy infrastructure, and the substitution of planning or inference modules at each layer (Gupta et al., 12 Oct 2025, Kotsi et al., 2023).
Declarative State Abstractions: Centralized world states or datastream aggregations unify resource and task visibility, allowing for transparent updates, bidirectional communication, and policy-based adaptation (see RobotFleet's key-value store and Braid's metric-driven policies) (Gupta et al., 12 Oct 2025, Pruyne et al., 2024).
Network Emulation and Federated Experimentation: In Federated Learning, high-fidelity emulation platforms emulate both learning and network stack behavior—capturing bandwidth limits, packet loss, and traffic bursts—so that algorithmic progress can be correlated directly with network dynamics (Hamdan et al., 30 Aug 2025).
Integrated Decision Engines: Fleet-wide steering is increasingly performed via external, cloud-hosted decision engines that flows or agents consult for coordinated adaptation (e.g., Braid for experiment fleets), abstracting adaptation as the evaluation of metric-driven policies over global resource and progress datastreams (Pruyne et al., 2024).

4. Domain-Specific Advances and Case Studies

Mobility and Logistics

Joint fleet size and routing optimization for mobile fast charging (FSMCVRPTW) shows strong economies of scale with higher customer density and highlights the need for co-design of fleet composition, charger power, and routing (Araghi et al., 2 Dec 2025).
Electric vehicle fleets optimize both vehicle and charger counts via spatial-queueing theory, revealing that EVs permit strictly lower marginal fleet requirements compared to ICE fleets, provided charging and dispatch policies exploit state-of-charge-aware matching (Power-of- $d$ policy) (Varma et al., 2023).

Multi-Robot and Autonomous Systems

LLM-augmented planners facilitate open-world task decomposition, while MILP and neural/LLM allocators compute task–robot assignments under capability and state constraints; containerized execution enables scalable, robust fleet deployments (Gupta et al., 12 Oct 2025).

Scientific Experimentation and Distributed Computing

The fleet abstraction in workflow-based experimental science enables collective steering of hundreds to thousands of independently executing flows, with policies encoding high-level synchronization and adaptation decisions based on system-level or experiment-driven metrics (Pruyne et al., 2024).

Federated Learning

FLeet and related systems introduce battery-aware profiling and staleness-adaptive SGD (AdaSGD) for truly online FL, maintaining strict privacy and device efficiency while achieving much faster convergence and higher predictive accuracy in temporally dynamic tasks (Damaskinos et al., 2020).

5. Performance, Adaptivity, and Scaling Laws

State-of-the-art fleet algorithms and architectures report substantial empirical and theoretical advances:

Size and Cost Reduction: Robust and dynamic models produce 50%+ reductions in fleet and docking infrastructure relative to traditional static methods, while maintaining 95–99% service levels (Čáp et al., 2018, Hua et al., 2022).
Scaling Exponents: Theoretical lower bounds for vehicle-sharing and EV fleets demonstrate that carefully designed spatial and temporal matching rules reduce surplus fleet and chargers from $\Theta(\lambda^{2/3})$ to $\Theta(\lambda^\nu)$ , where $\nu\in[1/2,2/3]$ (Varma et al., 2023).
Real-Time and Open-World Adaptation: Learning-based and LLM-driven fleet planners handle open-world or previously unmodeled tasks via prompt chaining, subspace-routing, and dynamic adaptation, substantially restoring system accuracy with few-shot updates (Wang et al., 30 Jun 2026, Gupta et al., 12 Oct 2025).
Computational and System Overheads: Braid and similar decision engines maintain sub-100 ms metric evaluation latencies and scale to hundreds of parallel clients, supporting in-flow, high-frequency adaptation with minimal overhead (Pruyne et al., 2024).

6. Open Challenges and Future Directions

Ongoing research addresses several outstanding challenges:

Open-World and Continual Adaptation: Designing replay-free, memory- and storage-efficient continual adaptation schemes for fleets responding to persistent distribution shifts or novel tasks, particularly in adversarial or non-stationary environments (Wang et al., 30 Jun 2026).
Data Privacy, Security, and Federated Coordination: Ensuring data security and robust role-based access control in distributed fleet and federated learning environments remains an area for further hardening, especially as real-world deployments cross institutional and jurisdictional lines (Kotsi et al., 2023, Hamdan et al., 30 Aug 2025).
Multimodal and Heterogeneous Fleet Integration: Integration of heterogeneous vehicles (e.g., legacy ICE, BEV, mobile chargers) and device types within a single fleet, with context-aware policy switching and control, is a priority for real-world deployment (Araghi et al., 2 Dec 2025, Kotsi et al., 2023).
Emission-Aware and Resilient Routing: Emission-optimized fleet operation, robust to failures and environmental incidents (e.g., traffic, weather, infrastructure outages), is identified as a future extension for both urban and highway settings (Kotsi et al., 2023).
Scaling to Ultra-Large Fleets: Matheuristic and combinatorial optimization methods (e.g., column generation, branch-and-price) are critical for scaling fleet design and reoptimization to annual, continental, or multi-agent scenarios with rich domains and constraints (Bertoli et al., 2017, Araghi et al., 2 Dec 2025).

7. Representative Implementations and Open Resources

A diverse suite of open-source frameworks supports research and deployment:

Framework/Algorithm	Domain	Notable Features
CTMaaS (Kotsi et al., 2023)	Urban transportation, C-ITS	Microservices, V2X messaging, dynamic VRP, web/mobile UI
RobotFleet (Gupta et al., 12 Oct 2025)	Multi-robot task planning	LLM/DAG planning, MILP/LLM allocation, container orchestration
Braid (Pruyne et al., 2024)	Large-scale workflow steering	Policy-based adaptation, datastream aggregation, Flows API
FLeet (Damaskinos et al., 2020)	Online federated learning (Android)	SLO-driven profiling, AdaSGD, privacy-preserving deployment
inference-fleet-sim (Chen et al., 17 Mar 2026)	LLM inference fleet sizing	Queueing+DES, GPU physics models, heavy-tail-aware planning
FLEET (Gomez et al., 2022)	SLSN photometric identification (astro)	Random-forest classifier, active learning, open-source pipe

All cited frameworks or codebases are publicly released and actively maintained (see references for repository links).

In summary, fleet—the collective abstraction for coordinated resource management in distributed, dynamic environments—acts as a central organizing principle cutting across physical, cyber-physical, and algorithmic contexts. The emerging suite of mathematical models, control algorithms, and system architectures enables practitioners to design, deploy, and adapt fleets at scale, with rigorous guarantees on performance, resource efficiency, and adaptability under nonstationary and uncertain real-world conditions.