Simulated annealing based optimized driver scheduling for vehicle delivery

Abstract

Simulated annealing based optimized driver scheduling for vehicle delivery ehicle delivery is a major business where third-party drivers are hired to deliver vehicles when they are relocated, sold, or while returning rental cars. This is motivated due to the busy schedule of individuals and companies, convince, and cost saving. A vehicle delivery company typically operates in a chosen geography varying from a region of a country to a set of countries that are nearby. Hence, the drivers are also geographically dispersed. This is a complicated process due to the wide variation in collection/delivery locations, driver availability, time bounds, types of vehicles, special skills required by drivers, and impact due to traffic and weather. Currently the process is manipulated manually by a scheduling manager who creates next day’s schedule at the end of the working day based on the jobs received. However, as the number of jobs and drivers increase, it is hard to decide on the most appropriate driver for the job such that both the customer and company goals are optimally satisfied. We propose an automated driver scheduling solution to maximize the number of vehicle deliveries and customer satisfaction while minimizing the delivery cost and distributing driver income based on their availability. Proposed solution consists of a rule checker and a scheduler. Rule checker enforces constraints such as deadlines, vehicle types, license types, skills, and working hours. Scheduler uses simulated annealing to assign as many jobs as possible while minimizing the overall cost. Using a workload derived from an actual vehicle delivery company, we demonstrate that the proposed solution has good coverage of jobs while minimizing the cost and equitably distributing the income among drivers based on their availability. Moreover, the proposed solution has the flexibility to tolerate exceptions due to breakdowns, excessive traffic, and bad weather without a considerable impact on the majority of the already scheduled jobs.