Onsite Job Scheduling is a specialized variant of Vehicle Routing Problem
(VRP) with multiple depots. The objective of this problem is to execute jobs
requested by customers, belonging to different geographic locations by a
limited number of technicians, with minimum travel and overtime of technicians.
Each job is expected to be completed within a specified time limit according to
the service level agreement with customers. Each technician is assumed to start
from a base location, serve several customers and return to the starting place.
Technicians are allotted jobs based on their skill sets, expertise levels of
each skill and availability slots. Although there are considerable number of
literatures on VRP we do not see any explicit work related to Onsite Job
Scheduling. In this paper we have proposed an Adaptive Genetic Algorithm to
solve the scheduling problem. We found an optimized travel route for a
substantial number of jobs and technicians, minimizing travel distance,
overtime duration as well as meeting constraints related to SLA.

本研究提出了一种自适应遗传算法来解决现场作业调度问题，在最小化旅行距离和加班时长的同时，满足与 SLA 相关的约束条件，并为大量工作和技术人员找到了优化的旅行路线。

自适应遗传算法实现的现场工作调度

Onsite Job Scheduling by Adaptive Genetic Algorithm

The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging
technology enabling the development of smart city applications focused on
improving traffic safety, traffic efficiency, and the overall driving
experience. These applications have stringent requirements detailed in Service
Level Agreement.
Since vehicles have limited computational and storage capabilities,
applications requests are offloaded onto an integrated edge-cloud computing
system. Existing offloading solutions focus on optimizing the application's
Quality of Service (QoS) in terms of execution time, and respecting a single
SLA constraint. They do not consider the impact of overlapped multi-requests
processing nor the vehicle's varying speed. This paper proposes a novel
Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA)
to optimize the application's execution time for multi-request offloading in a
heterogeneous edge-cloud computing system, which considers the impact of
processing multi-requests overlapping and dynamic vehicle speed. The proposed
genetic algorithm integrates an adaptive penalty function to assimilate the SLA
constraints regarding latency, processing time, deadline, CPU, and memory
requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to
random offloading, and baseline genetic-based approaches. Results show
QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the
random offloading approach and with 59.9% fewer SLA violations. In contrast,
the baseline genetic-based approach increases the requests' performance by 1.14
times, with 19.8% more SLA violations.

该论文提出一种基于人工智能的 QoS-SLA-aware 自适应遗传算法，包括车辆的速度和多请求处理重叠的影响，以优化异构边缘云计算系统中多请求卸载应用的执行时间。与基于随机卸载和基线基因法相比，实验结果表明该算法可以平均提高 1.22 倍的请求数执行速度，并减少 59.9％的 SLA 违规。