Onsite Job Scheduling by Adaptive Genetic Algorithm
This addresses scheduling inefficiencies for companies managing onsite technicians, though it is incremental as it applies an existing algorithmic approach to a new problem variant.
The paper tackles the Onsite Job Scheduling problem, a variant of the Vehicle Routing Problem with multiple depots, by proposing an Adaptive Genetic Algorithm to optimize travel routes for technicians, minimizing travel distance and overtime while meeting service level agreement constraints.
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.