本研究討論接單式生產環境中單一退化機台的接單與維修決策。在接單式生產環境下，未來不確定訂單假設為隨機之非均質普瓦松(non-homogeneous Poisson process) 過程，且每張訂單有不同的最早可加工時間、交期、正常處理時間、及利潤。此外對於退化性機台而言，訂單的加工時間會隨著機台的耗損與退化而增加，透過預防性維修，機台退化的情形可被改善，然而維修期間機台停止生產會造成訂單延遲生產，機台的退化使得產能會隨著生產與維修排程而變動，並影響未來訂單的處理時間。因此本研究整合隨機訂單選擇問題與維修決策問題，在有限的產能限制下，權衡接單獲利與維修成本以最大化系統整體利潤。
完全維修與部分維修為兩種常見的維修策略，本研究運用Yeh (2012) 提出的動態模擬期望收益決策步驟法，解決此兩種環境下的維修與接單問題，並提出一個靜態的完全維修與接單決策方法。根據完全維修實驗的結果，此兩種方法相較於不考慮配給的先來先服務接單策略結合最大退化率維修策略，利潤高出約63%。由於部分維修的決策需要較長的計算時間，因此部分維修的實驗與決策方法留待未來更進一步的研究與改善。

This study investigates the decisions with the integration of order selection and maintenance decision problem for make-to-order (MTO) manufacturers with single deteriorating machine. In MTO manufacturing environments, future orders with varying ready dates, due dates, normal processing times, and revenues arrive randomly from a non-homogeneous Poisson process. Moreover, for a deteriorating machine, the more jobs processed before a maintenance activity, the longer the processing time of an order due to machine wear. A maintenance activity restores the deteriorating status of the machine and hence increases its production rate. During a maintenance activity, the machine is shut down, which results in production delay. However, the processing times of future accepted orders will be reduced. With the objective of maximizing overall long-term profit, proper decisions of both maintenance and order acceptance are necessary to better utilizing the limited capacity.
Two types of maintenance environments, full maintenance and partial maintenance, are considered in this study. For full maintenance environment, a static maintenance and order selection (SMAOS) decision method is proposed to solve the decision problems. In addition, a dynamic decision method called simulated expected revenue decision procedure (SER) proposed by Yeh (2012) is adopted and modified in this study. Simulation experiments for full maintenance environment are conducted to compare the performance of SER and SMAOS with a simple FCFS with maximum deteriorating rate (FMDR) policy. According to the experiment results, both SMAOS and SER outperform FMDR by an average profit of 63%. However, due to long computation times for the partial maintenance environment, the experiments or the improvements in decision algorithms are left as future research directions.

摘要 I
Abstract II
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII
1. Introduction 1
1.1. MTO manufacturing environment 1
1.2. Deteriorating system 2
1.3. Problem assumptions 6
2. Literature Review 11
2.1. Deteriorating system 11
2.1.1. Maintenance in deteriorating systems 11
2.1.2. Deteriorating models 12
2.1.3. Rate-modifying activities 13
2.1.4. Deteriorating maintenance 16
2.2. Revenue management in manufacturing sector 19
2.3. Research motivations 20
3. Proposed Methods 22
3.1. Notations 22
3.2. Concepts 24
3.2.1. Order acceptance decision 25
3.2.2. Maintenance decisions 26
3.3. Optimal decision under perfect information 27
3.4. Estimation of the expected revenue under ODUPI 32
3.5. Simulated expected revenue decision procedure 35
3.5.1. The simulated order acceptance decision procedure 35
3.5.2. The simulated full maintenance decision procedure 37
3.5.3. The simulated partial maintenance decision procedure 39
3.6. Dispatching rule for accepted orders 45
3.7. Static maintenance and order selection policy 46
4. Experiment Design and Result Analysis 51
4.1. Experiment design 51
4.2. Procedure of problem generation 54
4.3. Comparisons 55
4.5. Experiment results and analysis 59
5. Conclusion 69
Reference 71

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