An Effective Mitigation Strategy to Hedge Against Absenteeism of Occasional Drivers

Companies can use occasional drivers to increase efficiency on last-mile deliveries. However, as occasional drivers are freelancers without contracts, they can decide at short notice whether they perform delivery requests. If they do not perform their tasks, this is known as driver absenteeism, which obviously disrupts the operations of companies. This paper tackles this problem by developing an auction-based system, including a mitigation strategy to hedge against the absenteeism of occasional drivers. According to this strategy, a driver can bid not only for serving bundles but also to act as a reserved driver. Reserved drivers receive a fee to ensure their presence but are not guaranteed to be assigned to a specific bundle. The problem is modeled as a two-stage stochastic problem with recourse activation. To solve this problem, this paper develops a self-learning matheuristic (SLM) and an iterated local search (ILS) that exploits SLM as a local search operator. Through an extensive computational study, this paper shows the clear dominance of the newly proposed approach in terms of solution quality, run times, and customers’ perceived quality of service compared against three different deterministic approaches. The Value of the Stochastic Solution, a well-known stochastic parameter, is also analyzed. Finally, the identikit of the perfect reserved driver, based on data observed in optimal solutions, is discussed.