Dynamic pricing of regulated field services using reinforcement learning
研究了受监管垄断企业在提供付费安装和免费维护两种现场服务时，通过动态定价和灵活资源分配提升服务质量和盈利能力的条件，并利用多臂老虎机算法找到近优定价策略。
Resource flexibility and dynamic pricing are effective strategies in mitigating uncertainties in production systems; however, they have yet to be explored in relation to the improvement of field operations services. We investigate the value of dynamic pricing and flexible allocation of resources in the field service operations of a regulated monopoly providing two services: installations (paid-for) and maintenance (free). We study the conditions under which the company can improve service quality and the profitability of field services by introducing dynamic pricing for installations and the joint management of the resources allocated to paid-for (with a relatively stationary demand) and free (with seasonal demand) services when there is an interaction between quality constraints (lead time) and the flexibility of resources (overtime workers at extra cost). We formalize this problem as a contextual multi-armed bandit problem to make pricing decisions for the installation services. A bandit algorithm can find the near-optimal policy for joint management of the two services independently of the shape of the unobservable demand function. The results show that (i) dynamic pricing and resource management increase profitability; (ii) regulation of the service window is needed to maintain quality; (iii) under certain conditions, dynamic pricing of installation services can decrease the maintenance lead time; (iv) underestimation of demand is more detrimental to profit contribution than overestimation.