在带时间窗约束的云机器人系统动态任务调度中缩短完工时间（Makespan）
Improving Makespan in Dynamic Task Scheduling for Cloud Robotic Systems with Time Window Constraints
AI总结：
针对带时间窗约束的云机器人系统动态任务调度问题，提出全任务网格结构及均衡算法，在满足时间窗约束下最小化完工时间，经理论证明与仿真验证有效。
摘要：
机器人网络云系统中采用完工时间最短的调度方法具有重要价值，可让系统以最快速度完成所有分配的任务。机器人网络云系统可转化为图结构：节点代表具备独立计算能力的硬件，边代表节点间的数据传输。任务的时间窗约束是对任务排序的自然方式。完工时间指从首个接收任务的节点开始执行其首个调度任务起，到所有节点完成其最后一个调度任务的最长时长。负载均衡分配与调度可确保首个节点完成调度任务的时间，与其余所有节点完成调度任务的时间间隔尽可能短。我们提出一种全任务网格结构，以确保满足任务的时间窗约束；还提出了全任务网格均衡算法（grid of all tasks balancing algorithm），用于以最短完工时间分配和调度任务。我们从理论上证明了所提算法的正确性，并通过仿真实验验证了所得结果。
英文摘要：
A scheduling method in a robotic network cloud system with minimal makespan is beneficial as the system can complete all the tasks assigned to it in the fastest way. Robotic network cloud systems can be translated into graphs where nodes represent hardware with independent computing power and edges represent data transmissions between nodes. Time window constraints on tasks are a natural way to order tasks. The makespan is the maximum amount of time between when the first node to receive a task starts executing its first scheduled task and when all nodes have completed their last scheduled task. Load balancing allocation and scheduling ensures that the time between when the first node completes its scheduled tasks and when all other nodes complete their scheduled tasks is as short as possible. We propose a grid of all tasks to ensure that the time window constraints for tasks are met. We propose grid of all tasks balancing algorithm for distributing and scheduling tasks with minimum makespan. We theoretically prove the correctness of the proposed algorithm and present simulations illustrating the obtained results.
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