臺灣博碩士論文加值系統

摘要
咖啡是越南排在石油後面的交易量最多的商品。越南咖啡豆產業對其價值鏈的低效率一直有不少的擔憂，對於咖啡價格不確定性的分佈遍布全球。供應商的選擇對製造商來說一直是一個挑戰，因為供應商可能對公司的整體表現會做出不利的和負面的影響。本研究，提出優化咖啡豆價格的預測，以及運用整數線性規劃進行供應商的選擇，在實際操作條件下，掌控了變化的市場，降低庫存成本，改善客戶服務和提高競爭力等重大問題的解決。本研究使用預測非線性灰色伯努利模型，提出了一項預測方法來處理咖啡豆價格以及需求波動的不確定性。本研究以NGBM(1,1）與傅里葉模型相結合，用以預測咖啡豆價格，接著，根據規格、策略和買方的要求，設計整數線性規劃來幫助製造商確定所需的數量，及能夠避免供應商的虧損，並使用過去六年收集的實際數據，在不確定的系統中使用微分方程計算咖啡豆的價格，結果顯示，價格和位置變化是供應商選擇的決定因素，通過本研究提出的方法進行深入分析，能為供應商選擇和產品採購提供一些策略，以優化咖啡公司的供應鏈成果，此外，由研究結果得知，本研究提出的方法有效，可以高精度地預測未來的咖啡豆價格，此可以幫助公司管理者在不確定的風險中，控制咖啡價格，並降低食品供應鏈管理的風險。
Keyword: 本研究,以,咖啡,與傅里葉模,數線性規, 供應商選

Coffee the most traded commodity after petroleum. The Vietnamese coffee bean industry has raised concerns over an inefficient coffee value chain, the bets on coffee price uncertainty are all around the world. The selection of suppliers will always be of a challenge for the manufacturer because suppliers can have specific, disadvantageous, and negative impacts on the general performance of a company. In this paper, Accurate optimization of coffee bean prices and mixed binary linear programming with supplier selection, a significant issue under practical operating conditions to control a changeable market, reduce their costs of inventory, improve customer service, and enhance competitiveness. The authors propose a forecasting method to deal with demand volatility and uncertainty in volumes and coffee bean prices. In this paper, we used the forecasting Nonlinear Grey Bernoulli Model. The (NGBM) (1,1) was integrated with Fourier residual modification model to forecast coffee bean prices, which are a crucial factor in the coffee bean supply chain in Vietnam. Then, based on the specifications, strategies, and buyer requirements, multiple-objective mixed integer binary linear programming is used to help the manufacturer to identify the quantity needed to avoid supplier deficits. The price of coffee beans is calculated by using a differential equation in an uncertain system, using actual data collected in the past six years. The results found that prices and location change are determinants for supplier selection, and supplier deficit is a huge barrier for the food industry. This paper aims to prove that in-depth analysis can be conducted through combined models and provide some strategies for supplier selection and product purchase to optimize the food supply chain of companies. The results of this study demonstrate that our integrated model is an effective method for forecasting future coffee bean prices with high accuracy. This research can help company managers control for the risk of uncertain coffee prices, as well as reduce the risk in food supply chain management.
Keywords: Nonlinear Grey Bernoulli Model, Fourier, Coffee bean, Linear Programming, Multiple – Objective mixed linear programming, supplier selection model.

摘要 2
ABSTRACT 3
ACKNOWLEDGMENTS 5
LIST OF TABLES 8
LIST OF FIGURES 9
CHAPTER 1: INTRODUCTION 10
1.1 Research background 10
1.1.1 The Coffee Industry in Vietnam 11
1.1.2 Application case study of Vietnamese Coffee industry 14
1.2 Research problems 21
1.3 Research motivation 22
1.4 Research objective 23
1.5 Dissertation process 23
2.1 Review of Supplier Selection of Literature 26
2.1.1 Supplier Selection in Agricultural 26
2.1.2 Method Selection for supplier selection in Agri-food 27
2.2 Criteria of supplier selection 32
2.3 Grey system theory 34
2.3.1 Introduction of grey system theory 34
2.3.2 Grey GM (1,1) 35
2.3.3 Nonlinear Grey Bernoulli Model - NGBM (1,1) 36
2.3.4 Fourier Service 38
2.3.5 MAPE - Evaluative Precision of Forecasting Models 39
2.4 Binary Integer linear programming 40
2.4.1 Binary Integer linear programming 40
2.4.2 Approaches to Multi-Objective Optimization - ε-Constraint Method 41
2.5 Related research 42
CHAPTER 3: METHODOLOGY 43
3.1 A general research framework 43
3.2 Grey Theory 45
3.2.1 Grey forecasting model 45
3.2.2 Nonlinear Grey Bernoulli Model – (NGBM (1,1) 46
3.2.3 The Residual of Fourier Series Modification by Fourier Series NGBM (1,1) 48
3.2.4 Valuation performances 49
3.2.5Reasons to choose the forecasting prediction 50
3.3 Develop Multiple Objective Mix Integer linear programming supplier selection model for Coffee bean Industrial 50
3.3.1 Multiple Objective Mix Integer linear programming 50
3.3.2 ε-Constraint Method 53
3.4 Reasons to choose Binary Integer linear programming supplier selection model for Agri-food Industrial 55
CHAPTER 4: EMPIRICAL RESULTS 56
4.1 Decide the criteria affect supplier selection in coffee bean chain 56
4.1.1 Penalty 56
4.1.2 Price 57
4.2 Validation of the Forecasting Model 58
4.3 Check forecasting accuracy 58
4.4. Fluctuating raw data sequence example 60
4.5 Multiple-Objective Mixed Integer Linear Programming Supplier Selection Model 61
4.6 ε-Constraint Method 63
4.7 Discussion 74
CHAPTER 5: CONCLUSION 76
5.1 Contributions 76
5.2 Research limitation and future research directions 78
REFERENCES 80

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