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    ํŒ๋งค์ด‰์ง„์„ ๋„์ž…ํ•œ ์ˆ˜์š” ๋ถˆํ™•์‹ค์„ฑ ์žฌ๊ณ ๊ด€๋ฆฌ ๋ชจํ˜•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.As the globalization of markets accelerates competition among companies, sales promotion, which refers to short-term incentives promoting sales of products or services, plays a prominent role. Although there are various types of sales promotions, such as price reduction, buy-x-get-y-free, and trade-in program, the common purpose is to induce the purchase of customers by offering benefits. This successful strategy has caught the attention of researchers, including operations management and supply chain management. Thus, various studies have been conducted to examine strategies for ongoing operations and to demonstrate the effects of the sales promotion, which are based on the strategic level. However, research at the tactical or operational level has been conducted insufficiently. This dissertation examines the inventory models considering (i) markdown sale, (ii) buy one get one free (BOGO), and (iii) trade-in program. First, the newsvendor model is considered. By introducing the decision variable, which represents the start time of markdown sale, the retailer can obtain the optimal combination of the start time of a markdown sale and an order quantity. Under certain conditions in a decentralized system, however, the start time of a markdown sale where the retailer obtains the highest profit is the least profitable for the manufacturer. To avoid irrational ordering behavior by a retailer against a manufacturer, a revenue-sharing contract is proposed. Second, the mobile application, ``My Own Refrigerator'', is considered in the inventory model. It enables customers to store BOGO products in their virtual storage for later use. That is, customers can drop by the store to pick up the extra freebies in the future. The promotion involves a high degree of uncertainty regarding the revisiting date because customers who buy the product do not need to take both products on the day of purchase. To deal with this uncertainty, we propose a robust multiperiod inventory model by addressing the approximation of a multistage stochastic optimization model. Third, the trade-in program is considered. It is one of the sales promotions that companies collect used old-generation products from customers and provide them with new-generation products at a discount price. It also helps to acquire the additional products which are required for the refurbishment service. A multiperiod stochastic inventory model based on the closed-loop supply chain system is proposed by incorporating the trade-in program and refurbishment service simultaneously. The stochastic optimization model is approximated to the robust counterpart, which features a deterministic second-order cone program.์‹œ์žฅ์˜ ์„ธ๊ณ„ํ™”์— ๋”ฐ๋ฅธ ๊ธฐ์—… ๊ฐ„์˜ ๊ฒฝ์Ÿ์ด ๊ฐ€์†ํ™”๋จ์— ๋”ฐ๋ผ, ๋‹จ๊ธฐ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ํ†ตํ•ด ๊ณ ๊ฐ์˜ ์ œํ’ˆ ๋˜๋Š” ์„œ๋น„์Šค ๊ตฌ๋งค๋ฅผ ์œ ๋„ํ•˜๋Š” ํŒ๋งค์ด‰์ง„์˜ ์—ญํ• ์ด ์ค‘์š”ํ•ด์กŒ๋‹ค. ๊ฐ€๊ฒฉ ์ธํ•˜, ํ–‰์‚ฌ์ƒํ’ˆ ์ฆ์ •, ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ํŒ๋งค์ด‰์ง„ ์ „๋žต์ด ์กด์žฌํ•˜์ง€๋งŒ, ๊ณตํ†ต๋œ ์ฃผ์š” ๋ชฉ์ ์€ ๊ธฐ์—…์ด ๊ณ ๊ฐ์—๊ฒŒ ํ˜œํƒ์„ ์ œ๊ณตํ•˜์—ฌ ๊ณ ๊ฐ์˜ ์ˆ˜์š”๋ฅผ ์ฆ๋Œ€์‹œํ‚ค๋Š” ๊ฒƒ์ด๋‹ค. ํŒ๋งค์ด‰์ง„์˜ ์„ฑ๊ณต์ ์ธ ์ „๋žต์€ ๊ฒฝ์˜๊ณผํ•™ ๋˜๋Š” ๊ณต๊ธ‰๋ง๊ด€๋ฆฌ ๋ถ„์•ผ๋ฅผ ํฌํ•จํ•œ ๊ด€๋ จ ํ•™๊ณ„์˜ ๊ด€์‹ฌ์„ ์ด๋Œ์—ˆ๋‹ค. ์ง€์†์ ์ธ ์šด์˜์„ ์œ„ํ•œ ์ „๋žต์„ ๊ฒ€ํ† ํ•˜๊ณ  ์ „๋žต์  ์ˆ˜์ค€ ๊ณ„ํš์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํŒ๋งค ์ด‰์ง„์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ์šด์˜ ์ˆ˜์ค€์˜ ์†Œ๋งค์—…์ฒด ์ž…์žฅ์—์„œ์˜ ์—ฐ๊ตฌ๋Š” ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” (i) ๋งˆํฌ ๋‹ค์šด (ii) buy one get one free (BOGO), ๋ฐ (iii) ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์„ ๊ณ ๋ คํ•œ ์žฌ๊ณ ๊ด€๋ฆฌ๋ชจํ˜•์„ ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ €, ์‹ ๋ฌธ๊ฐ€ํŒ์› ๋ชจํ˜•์— ๋งˆํฌ ๋‹ค์šด ์‹œ์ž‘ ์‹œ์ ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒฐ์ • ๋ณ€์ˆ˜๋ฅผ ๋„์ž…ํ•˜์—ฌ ์ตœ์ ์˜ ๋งˆํฌ ๋‹ค์šด ์‹œ์ž‘ ์‹œ์ ๊ณผ ์ฃผ๋ฌธ๋Ÿ‰์˜ ์กฐํ•ฉ์„ ์ œ๊ณตํ•˜๋Š” ๋ชจํ˜•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์˜ ํŠน์ • ์กฐ๊ฑด์—์„œ๋Š” ์†Œ๋งค์—…์ž๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ์ด์ต์„ ์–ป๋Š” ์‹œ์ ์ด ์ œ์กฐ์—…์ž์—๊ฒŒ ๋‚ฎ์€ ์ˆ˜์ต์„ฑ์„ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ œ์กฐ์—…์ž์— ๋Œ€ํ•œ ์†Œ๋งค์—…์ž์˜ ๋น„ํ•ฉ๋ฆฌ์  ์ฃผ๋ฌธ์„ ๋ง‰๊ธฐ ์œ„ํ•œ ์ด์ต๋ถ„๋ฐฐ๊ณ„์•ฝ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด์ต๋ถ„๋ฐฐ๊ณ„์•ฝ์„ ํ†ตํ•œ ์ค‘์•™์ง‘๊ถŒํ™” ์‹œ์Šคํ…œ์€ ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์—์„œ ์–ป์€ ์ด์ต์— ๋น„ํ•ด ์†Œ๋งค์—…์ž์™€ ์ œ์กฐ์—…์ž์˜ ์ด์ต์„ ํ–ฅ์ƒ์‹œํ‚ด์„ ์ˆ˜์น˜์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๋ชจ๋ฐ”์ผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ``๋‚˜๋งŒ์˜ ๋ƒ‰์žฅ๊ณ ''๋ฅผ ๊ณ ๋ คํ•œ ์žฌ๊ณ ๋ชจํ˜•์„ ๊ณ ๋ คํ•œ๋‹ค. ์ด ์•ฑ์„ ํ†ตํ•ด BOGO ํ–‰์‚ฌ์ œํ’ˆ์„ ๊ตฌ๋งคํ•œ ๊ณ ๊ฐ์€ ์ฆ์ •ํ’ˆ์„ ๊ตฌ๋งค ๋‹น์ผ ๋‚  ๊ฐ€์ ธ๊ฐ€์ง€ ์•Š๊ณ  ๋ฏธ๋ž˜์— ์žฌ๋ฐฉ๋ฌธํ•˜์—ฌ ์ˆ˜๋ นํ•  ์ˆ˜ ์žˆ๋Š” ํ˜œํƒ์„ ๋ฐ›๋Š”๋‹ค. ํ•˜์ง€๋งŒ ์†Œ๋งค์—…์ž ์ž…์žฅ์—์„œ๋Š” ๊ณ ๊ฐ์ด ์ฆ์ •ํ’ˆ์„ ์–ธ์ œ ์ˆ˜๋ นํ•ด ๊ฐˆ ์ง€์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์ด ์กด์žฌํ•˜๋ฉฐ ์ด๋Š” ๊ธฐ์กด์˜ ์žฌ๊ณ ๊ด€๋ฆฌ ์šด์˜๋ฐฉ์‹์—๋Š” ํ•œ๊ณ„์ ์ด ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ๊ฐ์˜ ์žฌ๋ฐฉ๋ฌธ์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•œ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์„ ์ˆ˜๋ฆฝํ•˜๋ฉฐ ์ด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ณ„์‚ฐํ•˜๊ธฐ ์œ„ํ•œ ๊ฐ•๊ฑด์ตœ์ ํ™” ๋ชจํ˜•์œผ๋กœ ๊ทผ์‚ฌํ™”ํ•˜์˜€๋‹ค. ์…‹์งธ, ๋ฆฌํผ์„œ๋น„์Šค์™€ ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์„ ๊ณ ๋ คํ•œ ํํšŒ๋กœ ๊ณต๊ธ‰๋ง ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜์˜ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์žฌ๊ณ ๊ด€๋ฆฌ๋ชจํ˜•์„ ์ œ์•ˆํ•œ๋‹ค. ์‹ ์„ธ๋Œ€ ์ œํ’ˆ, ๋ฆฌํผ์„œ๋น„์Šค ๋ฐ ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์— ๋Œ€ํ•œ ์„ธ ๊ฐ€์ง€ ์œ ํ˜•์˜ ๋ถˆํ™•์‹คํ•œ ์ˆ˜์š”์— ๋Œ€ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ฐ˜์˜ํ•จ์— ๋”ฐ๋ผ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์ด ์ˆ˜๋ฆฝ๋œ๋‹ค. ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์˜ ๊ณ„์‚ฐ์ด ์–ด๋ ต๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ๊ฐ•๊ฑด์ตœ์ ํ™” ๋ชจํ˜•์œผ๋กœ ๊ทผ์‚ฌํ™”ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Sales promotion 1 1.2 Inventory management 3 1.3 Research motivations 6 1.4 Research contents and contributions 8 1.5 Outline of the dissertation 10 Chapter 2 Optimal Start Time of a Markdown Sale Under a Two-Echelon Inventory System 11 2.1 Introduction and literature review 11 2.2 Problem description 17 2.3 Analysis of the decentralized system 21 2.3.1 Newsvendor model for a retailer 21 2.3.2 Solution procedure for an optimal combination of the start time of the markdown sale and the order quantity 25 2.3.3 Profi t function of a manufacturer 25 2.3.4 Numerical experiments of the decentralized system 27 2.4 Analysis of a centralized system 35 2.4.1 Revenue-sharing contract 35 2.4.2 Numerical experiments of the centralized system 38 2.5 Summary 40 2.5.1 Managerial insights 41 Chapter 3 Robust Multiperiod Inventory Model with a New Type of Buy One Get One Promotion: "My Own Refrigerator" 43 3.1 Introduction and literature review 43 3.2 Problem description 51 3.2.1 Demand modeling 52 3.2.2 Sequences of the ordering decision 54 3.3 Mathematical formulation of the IMMOR 56 3.3.1 Mathematical formulation of the IMMOR under the deterministic demand 58 3.3.2 Mathematical formulation of the IMMOR under the stochastic demand 58 3.3.3 Distributionally robust optimization approach for the IMMOR 60 3.4 Computational experiments 76 3.4.1 Experiment 1: tractability of the RIMMOR 77 3.4.2 Experiment 2: robustness of the RIMMOR 78 3.4.3 Experiment 3: e ect of duration of the expiry date under the different customers' revisiting propensities 78 3.5 Summary 83 3.5.1 Managerial insights 83 Chapter 4 Robust Multiperiod Inventory Model Considering Refurbishment Service and Trade-in Program 85 4.1 Introduction 85 4.2 Literature review 91 4.2.1 Effects of the trade-in program and strategic-level decisions for the trade-in program 91 4.2.2 Inventory or lot-sizing model in a closed-loop supply chain system 94 4.2.3 Distinctive features of this research 97 4.3 Problem description 100 4.3.1 Demand modeling 103 4.3.2 Decision of the inventory manager 105 4.4 Mathematical formulation 108 4.4.1 Mathematical formulation of the IMRSTIP under the deterministic demand model 108 4.4.2 Mathematical formulation of the IMRSTIP under the stochastic demand model 110 4.4.3 Distributionally robust optimization approach for the IMRSTIP 111 4.5 Computational experiments 125 4.5.1 Demand process 125 4.5.2 Experiment 1: tractability of the RIMRSTIP 128 4.5.3 Experiment 2: approximation error from the expected value given perfect information 129 4.5.4 Experiment 3: protection against realized uncertain factors 130 4.5.5 Experiment 4: di erences between modeling demands from VARMA and ARMA 131 4.5.6 Experiments 5 and 6: comparisons of backlogged refurbishment service with or without trade-in program 133 4.6 Summary 136 Chapter 5 Conclusions 138 5.1 Summary 138 5.2 Future research 140 Bibliography 142 Chapter A 160 A.1 160 A.2 163 A.3 163 A.4 164 A.5 165 A.6 166 Chapter B 168 B.1 168 B.2 171 B.3 172 Chapter C 174 C.1 174 C.2 174 ๊ตญ๋ฌธ์ดˆ๋ก 179Docto

    A)Study on the benefit estimation of Han river water quality improvement by using contingent valuation method

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์ œํ•™๋ถ€ ๊ฒฝ์ œํ•™์ „๊ณต,1997.Docto

    ๋‹จ๋ฐฑ์งˆ ๊ฒฐ์ •๊ตฌ์กฐ๋ฅผ ํ†ตํ•œ Rab6Aโ€ฒ๊ณผ Rab11A์˜ ํšจ์†Œ์  ํ™œ์„ฑ ๋ฐ ๋‹จ๋ฐฑ์งˆ ์•ˆ์ •์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผํ•™๊ณผ ์˜๊ณผํ•™ ์ „๊ณต, 2016. 8. ์ „์ฃผํ™.The Ras-superfamily of small GTP-binding proteins (GTPase), plays critical roles in various cellular processes. The activity of this family is controlled by the states of nucleotide binding. When GTP binds to GTPase, it becomes the active form. Once GTP is hydrolyzed to GDP + Pi, the GTPase becomes the inactive form. Rab GTPase is Ras-superfamily and plays an especially important role in vesicle trafficking, which is essential for endocytosis, biosynthesis, secretion, cell differentiation and growth. Because a functional loss of the Rab pathways has been implicated in a variety of disease, the Rab GTPase family has been extensively investigated. Mutant form of Rab GTPase has been commonly utilized for biochemical study of Rab and Rab-related functional studies. Generally, most mutants in catalytic region of Rab GTPase have been constructed by sequence alignment from other GTPases which are already known as active- or inactive form by structural study or biochemical study. Since similar structural of GTPases could show different functional profile, such as binding affinity of ligand, rate of enzyme activity and interacting partners, it is possible that mutation of similar position of amino acid likely showed different activity between Rab GTPases. Therefore, functional activity and molecular mechanism of Rab GTPase mutants must be validated by biochemical analysis and structural analysis. Due to this reason, I undertook to characterize the activity of Rab GTPase mutants. In this study, I crystallized mutant form of Rab6Aโ€ฒand Rab11A and solved crystal structures of them. In the first part, I solved crystal structure of active form of Rab6A and I found that active form of GTPase could trap GDP. This observation indicated that binding of ligand could be various dependent on protein-type and environment around protein. In second part, I found that the stability of inactive Rab11A depended on the occupation of GDP. In the third part, active Rab11A could trap GTP instead of hydrolyzing it, because of absence of water molecule around catalytic pocket. These results provide better understanding for Rab-related study.Abstract .................................................................................I Contents ............................................................................III List of tables and figures ................................................IV List of abbreviations ........................................................VII Introduction...........................................................................1 Chapter 1...........................................................................14 Crystal structure of Rab6A'(Q72L) mutant reveals unexpected GDP/Mgยฒโบ binding with opened GTP-binding domain Materials and Methods.................................................15 Results and Discussion.........................................19 Chapter 2.............................................................................39 Molecular mechanism of constitutively active Rab11A was revealed by crystal structure of Rab11A S20V Materials and Methods.................................................40 Results and Discussion.........................................45 Chapter 3.............................................................................70 Occupation of nucleotide in the binding pocket is critical to the stability of Rab11A Materials and Methods.................................................71 Results and Discussion.........................................77 Conclusion...........................................................................97 References................................................................100 Abstract in Korean....................................................116Docto

    ์ค‘๋“ฑํ•™๊ต ์ฒด์œก๊ต์‚ฌ์˜ ๊ต๊ณผ๊ด€ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ฒด์œก๊ต์œก๊ณผ,2003.Maste

    ์ง„์ž…๊ณ„ํš๊ณผ ๋Œ€ํ”ผ๊ณ„ํš์„ ๋™์‹œ์— ๊ณ ๋ คํ•œ ์ˆ˜๋ฆฌ๋ชจํ˜• ๋ฐ ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฐ์—…๊ณตํ•™๊ณผ, 2017. 2. ๋ฌธ์ผ๊ฒฝ.Numerous emergency situations, such as the terrorist attacks in France in 2015 and Belgium in 2016, have transpired throughout the world. Accordingly, research on evacuation plans for people experiencing terrorism, fire, and natural disaster has gained significant attention. However, previous researchers have primarily focused on evacuation planning. For responders, such as special weapons and tactics teams or firefighters, entrance planning is considered as important as evacuation planning for effective amelioration of dangerous situations. Therefore, this paper presents mathematical models that address the simultaneous evacuation of victims and entrance of responders. By developing the model based on the dynamic network flow model, the solution provides the optimal routes for evacuees and responders within a critical timeframe. However, the mathematical model has two weaknesses: difficulty solution interpretation and lengthy computation time. To overcome these problems, a heuristic algorithm based on the capacity constrained route planner is also developed. This study pioneers mathematical models and a heuristic algorithm that account for both evacuation and response plans.Chapter 1. Introduction 1 1.1. Background 1 1.2. Literature review 2 1.3. Contributions 4 Chapter 2. Mathematical Models 9 2.1. General features of mathematical models 9 2.2. Four types of mathematical models 12 2.3. Model I 14 2.4. Model II 18 2.5. Model III. 20 2.6. Model IV 22 Chapter3. Heuristic Algorithm 24 3.1. Procedure of Heuristic algorithm 24 3.2. Numerical example 29 Chapter 4. Case Study 32 4.1. Backdrop of the case study 32 4.2. Case study I 34 4.3. Results of the Case study I 35 4.4. Case study II 43 4.5. Results of the Case study II 44 Chapter 5. Conclusions 45 Bilbliography 46 ์ดˆ ๋ก 50Maste

    ์ง€์—ญ๊ฐˆ๋“ฑ์˜ ์‚ฌํšŒ์  ๋น„์šฉ์— ๊ด€ํ•œ ์—ฐ๊ตฌ(A study on social cost of regional conflict in development projects)

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