A two-stage dynamic capacity planning approach for agricultural machinery maintenance service with demand uncertainty

Reasonable capacity planning is important to improve the efficiency of agricultural operations and reduce the operating cost for maintenance service providers during the harvesting season. Many studies involve staffing and scheduling approaches that account for nonstationary demand. However, these methods are not applicable in the field of agricultural operations because of the explosive growth of the failure rate during the harvesting season. In addition, few studies have involved allocation methods and related models between different planning levels, especially for the uncertain demand in agricultural machinery maintenance service, which has a strong reliance on results between the different management levels. Motivated by this observed gap, this paper proposes a two-stage analytical methodology that connects the data between different planning levels and aims to develop a dynamic capacity planning method of maintenance service for agricultural machinery fleets. At the first stage, we develop a scheduling model for agricultural machinery fleets based on the time window of harvesting. At the second stage, we propose a following-service mode and a dynamic covering model based on the scheduling results, in which queuing theory is used to solve the service parameters. This study satisfies the needs of service providers to find the optimum balance between high service quality and reasonable costs. A real-life case study is presented to illustrate the applicability of the proposed model as well as the effectiveness of the designed approach.