對於設備的租賃，實務上出租者與承租者會在設備租賃的契約中明訂雙方的權利與義務。一般來說，當設備在租期內失效時，出租者必須負責維修失效的設備至正常運作狀態。然而，設備失效的頻率會隨著年齡與使用率的增長而愈頻繁，使得出租者必須負擔的維修費用就愈多，因此通常會考慮在租賃期間執行預防保養以降低設備的失效次數。對於單一設備來說，最佳預防保養策略相較於多個設備更容易被安排。而在多個設備情況下，出租者如何規劃設備的預防保養是一個非常重要的課題。此外，在過去安排好多個設備的保養時程後，對於設備的派工皆採取「分別派工」的方式，分別派出多組員工對多個設備執行保養。但這種派工方式可能會造成許多成本的浪費。因此本論文提出兩種分群方式來節省期望總成本，(1)「統一派工」，意即多個設備在同時間皆須保養時，將該時間下所有設備分至同一群，統一派出一組員工執行多個設備的保養。(2)延長對設備進行決策的週期時間，使保養時間較相近的設備能於同時間執行保養，以便在「統一派工」下可分至同一群。在此情況下，本論文將以出租者的立場針對多個租賃設備於不同決策週期下進行「統一派工」預防保養，建構出多個設備於租賃期內之總維修成本模式，並尋求最佳決策週期使期望總成本最小。另外，進一步以數值範例分析探討設置成本與變動決策週期對設備預防保養之影響，最後比較租賃設備採取「統一派工」與「分別派工」預防保養策略對於期望總成本之差異。

For the leased devices, the lessor usually signs a contract about leased devices with lessee to stipulate their rights and responsibilities. In general, any failure of a leased device within the lease period is rectified by a repair provided by the lessor. To reduce the number of failures of the device, additional preventive maintenance(PM) actions might be carried out during the lease period. For a single device, the optimal PM policy can be easily derived. However, when there are multiple devices in the lease contract, the PM scheme of leased devices is an important issue for the lessor. Moreover, it is used to adopt “individual dispatch” for multiple devices in the past, but this dispatch may bring a lot cost for lessor. Therefore, this paper proposes two methods of group for multiple devices. (1) “joint dispatch” means that when multiple devices are required to be maintained at the same time, a group of employees is sent out to perform the maintenance of multiple devices. (2) extending the cycle time for making decision on devices so that devices with similar maintenance scheme can perform maintenance at the same time. Under this situation, this paper takes lessor’s viewpoint to investigate the optimal decision cycle of PM for multiple leased devices under “joint dispatch”. In addition, the “joint dispatch” PM mathematical cost model under various decision cycle is constructed, Base on the model, the optimal group PM policy is derived such that the expected total maintenance cost is minimized. Furthermore, the impact of the setup cost and decision cycle on the optimal PM policy is illustrated through numerical examples.

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