One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, $\sum_{k}$-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.

翻译：大规模、开放、协作且商业化的车辆编队协调面临的主要挑战之一是动态任务分配。具有自利动机且个体理性的车辆驾驶员兼具局部与全局目标，需要采用某种公平高效的任务分配方法进行协调。本文针对可扩展动态任务分配问题，聚焦确定性及二维动态线性指派问题开展文献综述。我们重点探讨开放车辆编队的多智能体系统建模，其中动态出现的车辆由软件智能体表征，需将其分配至一组动态出现的任务。通过对集中式、分布式与去中心化求解方案的最新研究成果进行比较与批判性分析，进一步针对组合优化中已知的以下指派问题的动态变体提出数学模型：指派问题、瓶颈指派问题、公平匹配问题、动态最小偏差指派问题、$\sum_{k}$-指派问题、半指派问题、带约束条件的指派问题以及考虑智能体资质的指派问题；所有研究均围绕开放车辆编队的核心特征展开：任务与车辆（智能体）的随机抵达——这些车辆可能在完成先前任务后重新可用，或根据个体利益在特定时间节点加入编队。