ESS: Scheduling, Inventory Optimization, and Coordination of Maintenance Networks

许多行业依赖于其关键资产的正常运作，例如航空公司的喷气发动机，电力公司的发电机，以及直升机上的军队。 运营效率的主要障碍是由于强制停机和定期维护操作导致的资产不可用。 后者虽然是有计划的，但一般都具有持续时间长和不确定的特点。 趋势是监控资产状况，并根据状况执行维护操作，以提高资产的可用性和可靠性，而不是实施更密集的预防性维护计划。 这对维护服务提供商网络（包括大修店、修理店、零件分销商和备件制造商）提出了重大挑战，即在传统上具有巨大不确定性的系统中，在减少库存的同时，具有短的和可预测的周转时间。以及动态不确定环境下维修服务网络的协调，以应对状态维修的挑战。 通过与我们的工业合作伙伴联合技术研究中心和i2技术公司密切合作，将执行三项任务。 第一个是基于我们的工业合作伙伴提供的现实离散事件仿真模型，开发大规模维修服务网络的随机调度模型。 将开发一种协同结合拉格朗日松弛、随机优化和模拟的解决方案方法，以提供具有可量化质量和信心的接近最佳的解决方案。 第二个任务是优化零件库存，平衡库存成本和零件可用性，以用于任务1中开发的调度方法。 将检查连续和定期审查政策，并将使用顺序优化来优化政策参数。 在第三个任务中，将研究维修服务网络中各种组织的自治性质。 任务1和任务2的模型将是分散的。 将建立一个分布式和异步协调机制，建立在与任务1中开发的方法一致的定价概念基础上。 一个移动的多代理系统，然后将设计和实现基于互联网的部署的方法，铺平了道路，为下一代电子维修服务网络。 我们的目标是通过缩短和可预测的周转时间，同时降低库存水平，充分利用资产状况信息和信息技术基础设施，充分利用新的维护服务模式。 本研究将对服务企业的生产调度、库存管理和供应链管理的基础理论和实践做出重要贡献，提高服务企业的竞争力和可靠性。

Many industries rely on the proper functioning of their key assets, such as airlines on jet engines, electric utilities on generators, and army on helicopters. A major obstacle on operation efficiency is asset unavailability due to forced outages and regular maintenance operations. The latter, although planned, are generally characterized by long and uncertain durations. Instead of embarking on more intensive preventative maintenance programs, the trend is to monitor asset conditions, and perform maintenance operations based on conditions to increase asset availability and reliability. This imposes major challenges on the networks of maintenance service providers, including overhaul shops, repair shops, part distributors, and spare part manufacturers, to have short and predictable turn-around-times while reducing inventory in systems traditionally characterized by massive uncertainties.The research is on scheduling, inventory optimization, and coordination of maintenance service networks under dynamic and uncertain environments to meet the challenges of condition-based maintenance. By closely collaborating with our industrial partners United Technologies Research Center and i2 Technologies, Inc., three tasks will be performed. The first is to develop stochastic scheduling models for large-scale maintenance service networks based on a realistic discrete event simulation model provided by our industrial partners. A solution methodology that synergistically combines Lagrangian relaxation, stochastic optimization, and simulation will be developed to provide near-optimal solutions with quantifiable quality and confidence. The second task is to optimize part inventory, balancing inventory costs and part availability for the scheduling method developed in Task 1. Continuous and periodic review policies will be examined, and ordinal optimization will be used to optimize policy parameters. In the third task, the autonomous nature of various organizations within a maintenance service network will be investigated. The models of Tasks 1 and 2 will be decentralized. A distributed and asynchronous coordination mechanism will be established, building on the pricing concept that is consistent with the methods developed in Task 1. A mobile multi-agent system will then be designed and implemented for Internet-based deployment of the methods, paving the way for the next generation e-maintenance service networks. Our goal is to reap the full benefit of the new maintenance service paradigm by having short and predictable turn-around-times while reducing inventory levels, making the best use of asset condition information and the information technology infrastructure. The research shall also significantly contribute to the fundamental theory and practice of scheduling, inventory management, and supply chain management, improving the competitiveness and reliability of service enterprises.