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研究生： Nabila Yuraisyah Salsabila
Nabila Yuraisyah Salsabila
論文名稱： 不確定故障下的備料庫存與計畫性維修之聯合最佳化模型
Joint Optimization Model of Spare Parts Inventory and Planned Maintenance under Uncertain Failures
指導教授： 喻奉天
Vincent F. Yu
郭伯勳
Po-Hsun Kuo
口試委員: 喻奉天
Vincent F. Yu
郭伯勳
Po-Hsun Kuo
吳 政 鴻
Cheng-Hung Wu
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 98
中文關鍵詞： Inventory management 、Stochastic programming 、Metaheuristics
外文關鍵詞： Inventory management, Stochastic programming, Metaheuristics
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  •  上一筆

    • Spare parts are often considered as Class C items, because of their low cost
    and low demand among the stocked items, but the availability of spare parts is
    essential to support maintenance requirements. Optimizing inventory parameters
    is the main problem of spare parts management to maintain a small number of
    SKUs kept in a store, and optimization techniques are commonly used to balance
    inventory cost and spare parts availability. Thus, this research proposes a joint
    optimization model of single-item multi-period spare parts inventory management
    and planned maintenance under uncertain failures. We present a Mixed Integer
    Nonlinear Programming (MINLP) formulation of the inventory optimization
    model under (s, S) policy with T periods of the order interval. Second, we
    combine this formulation with the predictive maintenance interval, representing
    the uncertain failures under predefined distribution. Since the model is nonlinear
    and stochastic, it is difficult to use exact methods to tackle it. Therefore, we
    combine the previously introduced MINLP formulation with a metaheuristic
    approach to solve the problem. Lastly, we perform a computational study on some
    instances and a real case study to demonstrate the proposed approach’s
    effectiveness and efficiency.

    Spare parts are often considered as Class C items, because of their low cost
    and low demand among the stocked items, but the availability of spare parts is
    essential to support maintenance requirements. Optimizing inventory parameters
    is the main problem of spare parts management to maintain a small number of
    SKUs kept in a store, and optimization techniques are commonly used to balance
    inventory cost and spare parts availability. Thus, this research proposes a joint
    optimization model of single-item multi-period spare parts inventory management
    and planned maintenance under uncertain failures. We present a Mixed Integer
    Nonlinear Programming (MINLP) formulation of the inventory optimization
    model under (s, S) policy with T periods of the order interval. Second, we
    combine this formulation with the predictive maintenance interval, representing
    the uncertain failures under predefined distribution. Since the model is nonlinear
    and stochastic, it is difficult to use exact methods to tackle it. Therefore, we
    combine the previously introduced MINLP formulation with a metaheuristic
    approach to solve the problem. Lastly, we perform a computational study on some
    instances and a real case study to demonstrate the proposed approach’s
    effectiveness and efficiency.

    ABSTRACT............................................................................................................ i ACKNOWLEDGMENT ...................................................................................... ii TABLE OF CONTENTS..................................................................................... iii LIST OF FIGURES ...............................................................................................v LIST OF TABLES .............................................................................................. vii CHAPTER 1 INTRODUCTION ..........................................................................1 1.1 Background ...............................................................................................1 1.2 Research Statement ...................................................................................6 1.3 Objectives..................................................................................................6 1.4 Scopes and Assumptions ...........................................................................6 1.5 Thesis Organization...................................................................................7 CHAPTER 2 LITERATURE REVIEW ..............................................................8 2.1 Spare Parts Inventory Management...........................................................8 2.2 Planned Maintenance ..............................................................................11 2.3 Spare Parts Inventory Management under Uncertainty...........................12 2.4 Solution Method ......................................................................................15 CHAPTER 3 MODEL DEVELOPMENT.........................................................23 3.1 Problem Description................................................................................24 3.2 System Characterization..........................................................................27 3.3 Problem Assumptions..............................................................................28 3.4 Mathematical Model................................................................................28 CHAPTER 4 SOLUTION METHODOLOGY.................................................32 4.1 Solution Representation ..........................................................................32 4.2 1st Stage Genetic Algorithm Procedure ...................................................34 4.2.1 Population initialization ...............................................................34 4.2.2 Updating Variables and Evaluating the Population .....................35 4.2.3 Elitism Operation .........................................................................37 4.2.4 Crossover Operation.....................................................................37 4.2.5 Mutation Operation ......................................................................38 4.2.6 Evaluating the Terminating Condition.........................................38 4.2.7 Final Solution...............................................................................38 4.3 2nd Stage GA Procedure ..........................................................................38 4.3.1 Population initialization ...............................................................39 4.3.2 Updating Variables and Evaluating the Population .....................39 4.3.3 Elitism Operation .........................................................................41 4.3.4 Crossover Operation.....................................................................41 4.3.5 Mutation Operation ......................................................................42 4.3.6 Evaluating the Terminating Condition.........................................42 4.3.7 Final Solution...............................................................................42 CHAPTER 5 RESULTS AND DISCUSSION...................................................46 5.1 Parameter Setting ....................................................................................46 5.2 Generating Stock Review and PM Schedule...........................................50 5.3 Modeling Random Components..............................................................51 5.3.1 Data Collection.............................................................................51 5.3.2 Data analysis ................................................................................52 5.3.3 Time series data modeling ...........................................................52 5.3.4 Goodness-of-fit testing.................................................................52 5.4 Generating Random Spare Parts Requirements ......................................52 5.5 Algorithm Testing ...................................................................................54 5.6 Evaluation on Inventory Policies and PM Policies .................................58 5.7 Evaluation on the Modeling Accuracy....................................................59 5.8 Application: A Petrochemical Company in Gresik, Indonesia ...............60 5.9 Sensitivity Analysis.................................................................................64 CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS .....................69 6.1 Conclusions .............................................................................................69 6.2 Recommendations for Future Research ..................................................70 REFERENCES.....................................................................................................71 APPENDICES ......................................................................................................74

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