臺灣博碩士論文加值系統

本文提出了多階整合存貨在延遲付款下之模型。我們認為在供應鏈中，生產製造與運輸配送的過程是不可靠的，因此零售商會接收到不良品，但零售商會將不良品送回至製造商，經過修復後再運送回給零售商。另外，我們也考慮了允許延遲付款的交易行為到本供應鏈存貨模型中，在傳統的經濟訂購量模型(EOQ)中，是不考慮延遲付款的，即為買方在每一次的運送批量送達時，就要立即付給賣方本次的費用，但在實際交易模式中，賣方是會允許買方可以延遲付款的。因此我們假設供應商會允許製造商延遲付款，相同的，製造商也會允許零售商延遲付款，各階層在允許付款的時段內，可以運用原本要付款的費用，靈活的運用資金，以降低資金運用上的壓力。本研究的目的為整體供應鏈成本的最小化，而本問題為NP-hard problem，因此我們使用基因演算法Genetic Algorithm (GA)來求解出最佳解。最後以數值範例和不同參數變化的敏感度分析來展現本研究之模型。

This article proposes a three-echelon integrated production-inventory supply chain model. We consider inventory include some defective items, because of the production or transportation process is unreliable, and defective items will sent back to manufacturers, retailers will receive the repaired products after manufacturer’s reworking. We also consider trade credit policy in our integrated inventory supply chain model, suppliers will give a fixed delay payment period to manufacturers, and manufacturers will also offer a fixed delay payment period to retailers, manufacturers and retailers can use fee (have to pay for upstream) during delayed payment period respectively. The objective is to minimize the joint total cost, and we using genetic algorithm (GA) to solve NP-hard problem. The model is illustrated with some numerical examples and sensitivity analysis has been done graphically with the variation of different inventory parameters.

Contents
致謝 II
Abstract III
中文摘要 IV
Contents V
List of figure VII
List of table VIII
1. Introduction 1
1.1 Research background and motivation 1
1.2 Research purposes 2
1.3 Research framework and process 2
2. Literature review 4
2.1 Integrated inventory in supply chain 4
2.2 Defective item in inventory 5
2.3 Trade credit policy 6
2.4 Application of Genetic Algorithm (GA) in supply chain management 6
3. Assumptions and notation 8
3.1 Assumptions 8
3.2 Notation 8
4. Formulation of the model 10
4.1 Retailer’s total cost 10
4.2 Manufacturer’s total cost 11
4.3 Supplier’s total cost 14
4.4 Joint total cost 15
4.5 Different cases for trade credit period 15
4.5.1 Supplier allows manufacturer to delay payment 15
4.5.2 Manufacturer allows retailer to delay payment 18
4.5.3 Joint total cost under permissible delay in payment 23
4.6 Genetic algorithm (GA) 31
4.6.1 Encoding 31
4.6.2 Generating initial population 32
4.6.3 Fitness function evaluation 32
4.6.4 Selection 32
4.6.5 Crossover 32
4.6.6 Mutation 32
5. Numerical example 34
5.1 Non-delay in payment's integrated inventory model 35
5.1.1 Scenario 1.1 (1 supplier – 1 manufacturers – 7 retailers) 35
5.1.2 Scenario 1.2 (1 supplier – 3 manufacturers – 7 retailers) 35
5.1.3 Scenario 1.3 (6 suppliers – 8 manufacturers – 24 retailers) 36
5.2 Delay in payment's integrated inventory model 37
5.2.1 Scenario 2.1 (1 supplier – 1 manufacturer – 7 retailer) 37
5.2.2 Scenario 2.2 (1 supplier – 3 manufacturers –7 retailers) 42
5.2.3 Scenario 2.3 (6 suppliers – 8 manufacturers – 24 retailers) 46
5.3 Sensitivity analysis 55
5.3.1 Sensitivity analysis for supplier and manufacturer’s capital opportunity cost (Isp , Imp) 55
5.3.2 Sensitivity analysis for manufacturer and retailer’s interest earned (Ime , Ire) 56
5.3.3 Sensitivity analysis for manufacturer and retailer’s permissible delay period (Z , F) 58
5.3.4 Sensitivity analysis for percentage of defective items (Y=f(Y)) 59
6. Conclusion and future work 61
Appendix 62
References 66

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