Technician routing and scheduling with employees’ learning through implicit cross-training strategy

Abstract

With record high talent shortages and skill mismatches around the world, this paper investigates a variant of multi-period dynamic technician and routing problem that can be modeled as a Markov decision process. To deal with the double tradeoffs between the routing and service time costs, as well as the current and future costs, we propose an approximate dynamic programming (ADP)-based cost function approximation (CFA) algorithm — the implicit cross-training strategy (ICT). A two-phase routing and scheduling heuristic is developed to account for both employees’ learning and future information, and to facilitate an efficient implementation of CFA. Extensive computational results show that ICT can provide a better solution in the current decision with a global view in comparison with the myopic strategy. In depth analysis demonstrates that ICT trains the workforce with more balanced skillsets and workloads, which ensures the flexibility of the workforce and helps buffer against the future uncertainties with substantial routing cost savings. Additionally, ICT has much more advantages in large-scale problems with more diversified service requests and randomly distributed customers.