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.

翻译：现场作业调度是车辆路径问题（VRP）的一个特殊变体，涉及多个出发点。该问题的目标是利用有限数量的技术人员，以最小化出行时间和加班时长，完成由不同地理位置客户请求的作业。每项作业需根据与客户签订的服务水平协议在规定时间内完成。每位技术人员从基地出发，服务多个客户后返回起点。技术人员根据其技能组合、各项技能的熟练程度及可用时间段被分配作业。尽管关于VRP的文献数量可观，但目前尚未见到与现场作业调度直接相关的工作。本文提出了一种自适应遗传算法来解决该调度问题，我们为大量作业和技术人员找到了优化的出行路线，从而最小化出行距离和加班时长，并满足服务水平协议的相关约束条件。