Managing production-inventory-maintenance systems with condition monitoring
研究了设备制造商在状态监测下管理生产、库存和维护的联合系统，通过马尔可夫决策过程优化备件生产与维修调度，发现与单独管理维护系统相比，识别更多退化状态能提升绩效，但增加维修能力可能适得其反。
Providing spare parts and maintenance services to customers has become an important revenue stream for equipment manufacturers. This paper studies a Production-Inventory-Maintenance (PIM) system where a manufacturer serves an installed base of machines. Each machine contains the same critical component, whose degradation behaviour is described by a Markov process and captured via condition monitoring. When the state of a component reaches a prespecified replacement threshold, the machine is considered to have failed and incurs downtime penalties until being repaired. There are a few repair servers and queueing dynamics follows the Machine Interference Problem (MIP). Component deterioration and server congestion provide advance demand information, which helps plan the production of spare parts in a make-to-stock routine. The unsatisfied requests for spare parts are fulfilled by emergency replenishments with lost-sales penalties. We formulate the production-control problem as a continuous-time Markov decision process, characterize the structure of the optimal policy, and develop an effective heuristic policy. Through numerical experiments, we reveal a number of notable differences between managing PIM systems and operating MIP systems alone: (1) Although identifying more degradation states has no value to MIP systems, PIM systems benefit substantially from such a refined state identification. (2) While MIP systems prefer more repair servers and higher repair operation efficiency, greater repair capacity may worsen the performance of PIM systems. (3) When the product of repair service rate and server number is fixed, a single server is always preferred in MIP systems. However, PIM systems could perform better with multiple less-efficient servers.