Towards the sustainable economy through digital technology: A drone-aided after-sales service scheduling model

Many companies are implementing emerging digital technologies to improve service quality. The application of unmanned aerial vehicles (i.e., drones) in electronic products after-sales service operations is a prominent example. With drones, the products that need after-sales services (e.g., repairment) can be delivered between the company store and consumers without requiring consumers to visit the store, which not only enhances consumer satisfaction but also helps improve environmental sustainability as fewer electronic products will be abandoned with the higher after-sales service levels. However, how to optimize drone-aided after-sales operations is still under-explored. An efficient decision support system may leverage the performance of the new service model. This work thus develops a new drone-aided after-sales service optimization model and proposes efficient solution algorithms. In particular, the company provides quick pick-up, repair, and delivery services through online reservation platforms. The store uses drones to perform pick-up and delivery services, and a set of technicians is available to perform the repair tasks. Given a set of service requests, the store must schedule limited resources, i.e., technicians and drones, to maximize the total profit of a workday. We formally describe the scheduling problem under this new model and formulate it as a mixed-integer linear programming model. We then show how the problem can be piece-wisely transformed into a variant of the flexible job shop scheduling problem. We propose several new formulations based on this idea. To handle practical-sized instances, we develop a new fix-and-solve matheuristic that consists of a sorting rule determination process and an approximate model solving process. Numerical experiments are conducted to demonstrate the performance of the proposed models and matheuristic. Sensitivity analyses are also performed to provide useful and practical implications for decision-makers.