45 research outputs found

    ๊ณต์ปจํ…Œ์ด๋„ˆ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•œ ํšจ์œจ์ ์ธ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ๋ฌธ์ผ๊ฒฝ.Due to a remarkable surge in global trade volumes led by maritime transportation, shipping companies should make a great effort in managing their container flows especially in case of carrier-owned containers. To do so, they comprehensively implement empty container management strategies and accelerate the flows in a cost- and time-efficient manner to minimize total relevant costs while serving the maximal level of customers demands. However, many critical issues in container flows universally exist due to high uncertainty in reality and hinder the establishment of an efficient container supply chain. In this dissertation, we fully discuss such issues and provide mathematical models along with specific solution procedures. Three types of container supply chain are presented in the following: (i) a two-way four-echelon container supply chain; (ii) a laden and empty container supply chain under decentralized and centralized policies; (iii) a reliable container supply chain under disruption. These models explicitly deal with high risks embedded in a container supply chain and their computational experiments offer underlying managerial insights for the management in shipping companies. For (i), we study empty container management strategy in a two-way four-echelon container supply chain for bilateral trade between two countries. The strategy reduces high maritime transportation costs and long delivery times due to transshipment. The impact of direct shipping is investigated to determine the number of empty containers to be repositioned among selected ports, number of leased containers, and route selection to satisfy the demands for empty and laden containers for exporters and importers in two regions. A hybrid solution procedure based on accelerated particle swarm optimization and heuristic is presented, and corresponding results are compared. For (ii), we introduce the laden and empty container supply chain model based on three scenarios that differ with regard to tardiness in the return of empty containers and the decision process for the imposition of fees with the goal of determining optimal devanning times. The effectiveness of each type of policy - centralized versus decentralized - is determined through computational experiments that produce key performance measures including the on-time return ratio. Useful managerial insights on the implementation of these polices are derived from the results of sensitivity analyses and comparative studies. For (iii), we develop a reliability model based on container network flow while also taking into account expected transportation costs, including street-turn and empty container repositioning costs, in case of arc- and node-failures. Sensitivity analyses were conducted to analyze the impact of disruption on container supply chain networks, and a benchmark model was used to determine disruption costs. More importantly, some managerial insights on how to establish and maintain a reliable container network flow are also provided.ํ•ด์ƒ ์ˆ˜์†ก์ด ์ฃผ๋„ํ•จ์œผ๋กœ์จ ์ „ ์„ธ๊ณ„ ๋ฌด์—ญ๋Ÿ‰์ด ๊ธ‰์ฆํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํšŒ์‚ฌ ์†Œ์œ  ์ปจํ…Œ์ด๋„ˆ๋Š” ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์„ ๊ด€๋ฆฌํ•˜๋Š” ๋ฐ ๋งŽ์€ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์—ฌ์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ด€๋ฆฌ ์ „๋žต์„ ํฌ๊ด„์ ์œผ๋กœ ๊ตฌํ˜„ํ•˜๊ณ  ํšจ์œจ์ ์ธ ์ˆ˜์†ก ๋น„์šฉ ๋ฐ ์‹œ๊ฐ„ ์ ˆ๊ฐ ๋ฐฉ์‹์œผ๋กœ ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์„ ์›ํ™œํžˆ ํ•˜์—ฌ ๊ด€๋ จ ์ด๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๋™์‹œ์— ๊ณ ๊ฐ์˜ ์ˆ˜์š”๋ฅผ ์ตœ๋Œ€ํ•œ ์ถฉ์กฑํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์‹ค์—์„œ๋Š” ๋†’์€ ๋ถˆํ™•์‹ค์„ฑ ๋•Œ๋ฌธ์— ์ปจํ…Œ์ด๋„ˆ ํ๋ฆ„์— ๋Œ€ํ•œ ๋งŽ์€ ์ฃผ์š”ํ•œ ์ด์Šˆ๊ฐ€ ๋ณดํŽธ์ ์œผ๋กœ ์กด์žฌํ•˜๊ณ  ํšจ์œจ์ ์ธ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๊ตฌ์ถ•์„ ๋ฐฉํ•ดํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ด์Šˆ์— ๋Œ€ํ•ด ์ „๋ฐ˜์ ์œผ๋กœ ๋…ผ์˜ํ•˜๊ณ  ์ ์ ˆํ•œ ํ•ด๋ฒ•๊ณผ ํ•จ๊ป˜ ์ˆ˜๋ฆฌ ๋ชจํ˜•์„ ์ œ๊ณตํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ธ ๊ฐ€์ง€ ์œ ํ˜•์˜ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์„ ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ € (i) ์–‘๋ฐฉํ–ฅ ๋„ค ๋‹จ๊ณ„ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง, (ii) ๋ถ„๊ถŒํ™” ๋ฐ ์ค‘์•™ ์ง‘์ค‘ํ™” ์ •์ฑ…์— ๋”ฐ๋ฅธ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง; ๊ทธ๋ฆฌ๊ณ  (iii) disruption ์ƒํ™ฉ ์†์—์„œ ์‹ ๋ขฐ์„ฑ์„ ๊ณ ๋ คํ•˜๋Š” ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ ์„ธ ๊ฐ€์ง€ ๋ชจํ˜•์€ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์— ๋‚ด์žฌ ๋œ ๋†’์€ ์œ„ํ—˜์„ ์ง์ ‘ ๋‹ค๋ฃจ๋ฉฐ ๊ณ„์‚ฐ ์‹คํ—˜์€ ํ•ด์šด ํšŒ์‚ฌ์˜ ๊ฒฝ์˜์ง„์ด๋‚˜ ๊ด€๊ณ„์ž๋ฅผ ์œ„ํ•ด ์ฃผ์š”ํ•œ ๊ด€๋ฆฌ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. (i)์˜ ๊ฒฝ์šฐ, ๋‘ ์ง€์—ญ ๊ฐ„ ์–‘์ž ๋ฌด์—ญ์„ ์œ„ํ•œ ์–‘๋ฐฉํ–ฅ ๋„ค ๋‹จ๊ณ„ ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง์—์„œ ๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ด€๋ฆฌ ์ „๋žต์„ ์—ฐ๊ตฌํ•œ๋‹ค. ์ด ์ „๋žต์€ ํ™˜์ ์œผ๋กœ ์ธํ•œ ๋†’์€ ํ•ด์ƒ ์šด์†ก ๋น„์šฉ๊ณผ ๊ธด ๋ฐฐ์†ก ์‹œ๊ฐ„์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์งํ•ญ ์ˆ˜์†ก์˜ ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜์—ฌ ์„ ํƒ๋œ ํ•ญ๊ตฌ ์ค‘ ์žฌ๋ฐฐ์น˜ ํ•  ๊ณต ์ปจํ…Œ์ด๋„ˆ ์ˆ˜, ์ž„๋Œ€ ์ปจํ…Œ์ด๋„ˆ ์ˆ˜, ๋‘ ์ง€์—ญ์˜ ์ˆ˜์ถœ์—…์ž์™€ ์ˆ˜์ž…์—…์ž์˜ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๋Œ€ํ•œ ์ˆ˜์š”๋ฅผ ๋งŒ์กฑํ•˜๊ธฐ ์œ„ํ•œ ๊ฒฝ๋กœ ์„ ํƒ์„ ๊ฒฐ์ •ํ•˜๊ฒŒ ๋œ๋‹ค. APSO ๋ฐ ํœด๋ฆฌ์Šคํ‹ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ•ด๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ ๋น„๊ต ์‹คํ—˜์„ ํ•˜์˜€๋‹ค. (ii)์˜ ๊ฒฝ์šฐ ์ตœ์  devanning time ๊ฒฐ์ •์„ ๋ชฉํ‘œ๋กœ ๊ณต ์ปจํ…Œ์ด๋„ˆ์˜ ๋ฐ˜ํ™˜ ์ง€์—ฐ๊ณผ ํ•ด๋‹น ์ˆ˜์ˆ˜๋ฃŒ ๋ถ€๊ณผ ๊ฒฐ์ • ํ”„๋กœ์„ธ์Šค์™€ ๊ด€๋ จํ•˜์—ฌ ์„œ๋กœ ๋‹ค๋ฅธ ์„ธ ๊ฐ€์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ โˆ™๊ณต ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๋ชจํ˜•์„ ์ œ์‹œํ•œ๋‹ค. ๊ฐ ์œ ํ˜•์˜ ์ •์ฑ…์ (๋ถ„๊ถŒํ™” ๋ฐ ์ค‘์•™ ์ง‘์ค‘ํ™”) ํšจ๊ณผ๋Š” ์ •์‹œ ๋ฐ˜ํ™˜์œจ์„ ํฌํ•จํ•œ ์ฃผ์š” ์„ฑ๋Šฅ ์ธก์ •์„ ๊ณ ๋ คํ•˜๋Š” ๊ณ„์‚ฐ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒฐ์ •๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ •์ฑ… ์‹คํ–‰์— ๋Œ€ํ•œ ์œ ์šฉํ•œ ๊ด€๋ฆฌ ์ธ์‚ฌ์ดํŠธ๋Š” ๋ฏผ๊ฐ๋„ ๋ถ„์„ ๋ฐ ๋น„๊ต ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์—์„œ ๋„์ถœํ•œ๋‹ค. (iii)์˜ ๊ฒฝ์šฐ, ๋ณธ ๋…ผ๋ฌธ์€ ์ปจํ…Œ์ด๋„ˆ ๋„คํŠธ์›Œํฌ ํ๋ฆ„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์‹ ๋ขฐ์„ฑ ๋ชจํ˜•์„ ๊ฐœ๋ฐœํ•˜๋Š” ๋™์‹œ์— ์•„ํฌ ๋ฐ ๋…ธ๋“œ failure๊ฐ€ ์žˆ์„ ๋•Œ street-turn ๋ฐ ๊ณต ์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋น„์šฉ์„ ํฌํ•จํ•œ ๊ธฐ๋Œ€ ์ด ๋น„์šฉ์„ ๊ตฌํ•œ๋‹ค. ์ค‘๋‹จ์ด ์ปจํ…Œ์ด๋„ˆ ๊ณต๊ธ‰๋ง ๋„คํŠธ์›Œํฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ–ˆ์œผ๋ฉฐ disruption ๋น„์šฉ์„ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋ฒค์น˜๋งˆํฌ ๋ชจํ˜•์„ ํ™œ์šฉํ•œ๋‹ค. ๋”๋ถˆ์–ด ์‹ ๋ขฐ์„ฑ์„ ๊ณ ๋ คํ•œ ์ปจํ…Œ์ด๋„ˆ ๋„คํŠธ์›Œํฌ ํ๋ฆ„์„ ๊ตฌ์ถ•ํ•˜๊ณ  ์‹ ๋ขฐ์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ด€๋ฆฌ์  ์ธ์‚ฌ์ดํŠธ๋„ ์ œ๊ณตํ•œ๋‹ค.Abstract i Contents ii List of Tables vi List of Figures viii 1. Introduction 1 1.1 Empty Container Repositioning Problem 1 1.2 Reliability Problem 3 1.3 Research Motivation and Contributions 4 1.4 Outline of the Dissertation 7 2. Two-Way Four-Echelon Container Supply Chain 8 2.1 Problem Description and Literature Review 8 2.2 Mathematical Model for the TFESC 15 2.2.1 Overview and Assumptions 15 2.2.2 Notation and Formulation 19 2.3 Solution Procedure for the TFESC 25 2.3.1 Pseudo-Function-based Optimization Problem 25 2.3.2 Objective Function Evaluation 28 2.3.3 Heuristics for Reducing the Number of Leased Containers 32 2.3.4 Accelerated Particle Swarm Optimization 34 2.4 Computational Experiments 37 2.4.1 Heuristic Performances 39 2.4.2 Senstivity Analysis of Varying Periods 42 2.4.3 Senstivity Analysis of Varying Number of Echelons 45 2.5 Summary 48 3. Laden and Empty Container Supply Chain under Decentralized and Centralized Policies 50 3.1 Problem Description and Literature Review 50 3.2 Scenario-based Model for the LESC-DC 57 3.3 Model Development for the LESC-DC 61 3.3.1 Centralized Policy 65 3.3.2 Decentralized Policies (Policies I and II) 67 3.4 Computational Experiments 70 3.4.1 Numerical Exmpale 70 3.4.2 Sensitivity Analysis of Varying Degree of Risk in Container Return 72 3.4.3 Sensitivity Analysis of Increasing L_0 74 3.4.4 Sensitivity Analysis of Increasing t_r 76 3.4.5 Sensitivity Analysis of Decreasing es and Increasing e_f 77 3.4.6 Sensitivity Analysis of Discounting ใ€–pnใ€—_{f1} and ใ€–pnใ€—_{f2} 78 3.4.7 Sensitivity Analysis of Different Container Fleet Sizes 79 3.5 Managerial Insights 81 3.6 Summary 83 4. Reliable Container Supply Chain under Disruption 84 4.1 Problem Description and Literature Review 84 4.2 Mathematical Model for the RCNF 90 4.3 Reliability Model under Disruption 95 4.3.1 Designing the Patterns of q and s 95 4.3.2 Objective Function for the RCNF Model 98 4.4 Computational Experiments 103 4.4.1 Sensitivity Analysis of Expected Failure Costs 106 4.4.2 Sensitivity Analysis of Different Network Structures 109 4.4.3 Sensitivity Analysis of Demand-Supply Variation 112 4.4.4 Managerial Insights 115 4.5 Summary 116 5. Conclusions and Future Research 117 Appendices 120 A Proof of Proposition 3.1 121 B Proof of Proposition 3.2 124 C Proof of Proposition 3.3 126 D Sensitivity Analyses for Results 129 E Data for Sensitivity Analyses 142 Bibliography 146 ๊ตญ๋ฌธ์ดˆ๋ก 157 ๊ฐ์‚ฌ์˜ ๊ธ€ 160Docto

    Assessing the eco-efficiency benefits of empty container repositioning strategies via dry ports

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    Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry portsโ€”street turns and extended free temporary storageโ€”considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multiparadigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping linesโ€™ collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions

    A Literature Review, Container Shipping Supply Chain: Planning Problems and Research Opportunities

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    This paper provides an overview of the container shipping supply chain (CSSC) by taking a logistics perspective, covering all major value-adding segments in CSSC including freight logistics, container logistics, vessel logistics, port/terminal logistics, and inland transport logistics. The main planning problems and research opportunities in each logistics segment are reviewed and discussed to promote further research. Moreover, the two most important challenges in CSSC, digitalization and decarbonization, are explained and discussed in detail. We raise awareness of the extreme fragmentation of CSSC that causes inefficient operations. A pathway to digitalize container shipping is proposed that requires the applications of digital technologies in various business processes across five logistics segments, and change in behaviors and relationships of stakeholders in the supply chain. We recognize that shipping decarbonization is likely to take diverse pathways with different fuel/energy systems for ships and ports. This gives rise to more research and application opportunities in the highly uncertain and complex CSSC environment.</jats:p

    Uncertainty and the Value of Information in Hinterland Transport Planning

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    Contributions to behavioural freight transport modelling

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    The Design, Planning and Execution of Sustainable Intermodal Port-hinterland Transport Networks

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    Globalization has led to a tremendous growth of international trade over the last century amounting to $18.8 trillion in 2014. Approximately 90% of non-bulk cargo is transported in shipping containers. The dominant mode in container transportation is maritime, in which containers are transported from a seaport to another seaport around the globe. Import containers are discharged in seaport container terminals and are destined to inland locations, a reverse process happens for export containers. The inland terminals can be close or far away from the seaport terminals where the containers were discharged. The container transport between the seaport and the inland locations is called port-hinterland transportation. Given the specific physical characteristics and infrastructure of each area this part of the transportation chain can be performed via trucks, trains or river vessels. The sequential use of multiple transport modes in port-hinterland transport is called combined transport. The main aim of this study is to analyze the port-hinterland transportation process and to develop models that support the design, planning and execution of port-hinterland transportation networks with high capacity modes such as barges and trains

    Strategies and new business models in intermodal hinterland transport

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    Equity research - Hapag-Lloyd, AG

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    Mestrado Bolonha em FinanรงasThe Equity Research report of Hapag-Lloyd, AG was written based on the research report format of ISEGโ€™s Master Final Work also recommended by the CFA Institute, divided into the following 8 parts: Research Snapshot, Business Description, Management and Corporate Governance which also includes Environmental, Social and Governance, Industry Analysis, Investment Summary, Valuation, Financial Analysis, Investment Risks. Hapag-Lloyd is the 5th largest container liner shipping company that operates worldwide with 421 sales offices in 137 counties. The core business of Hapag-Lloyd is container liner shipping, but also includes transport services from door to door. Its leading market position is evidenced by its transport capacity of 1.8m TEU and revenue of โ‚ฌ22.3bn in FY2021, a sharp increase of 74% compared to FY2020. With the closing price at โ‚ฌ177.6 per share on 30th December 2022 and a calculated price target of โ‚ฌ207.84 for 2023YE, Hapag-Lloyd's recommendation is "Hold" given its high exposure to economic cycles and the high-risk profile indicated by its "single" business segment. The price target is estimated based on the DCF model as the primary methodology, supported by other absolute and relative methods, representing an upside potential of 17.03%. Hapag-Lloyd expects to deliver solid financial performance in 2022, because of ultrahigh freight rates resulting from supply chain disruptions and high demand for goods.O relatรณrio de Equity Research da Hapag-Lloyd, AG foi escrito com base no formato de relatรณrio de pesquisa do Trabalho Final de Mestrado do ISEG, recomendado pelo CFA Institute, tendo sido dividido nas seguintes 8 partes: Research Snapshot, Business Description, Management and Corporate Governance que tambรฉm inclui o Environmental, Social and Governance, a Industry Analysis, o Investment Summary, a Valuation, a Financial Analysis, bem como Investment Risks. A Hapag-Lloyd รฉ a quinta maior empresa de transporte marรญtimo de contentores a nรญvel mundial, com 421 escritรณrios distribuรญdos por 137 paรญses. A principal linha de negรณcio da Hapag-Lloyd รฉ o transporte marรญtimo de contentores, porรฉm a mesma tambรฉm presta serviรงos transporte da porta ร  porta. Sua posiรงรฃo de lรญder de mercado รฉ apoiada pela sua capacidade de transporte de 1,8 milhรตes de toneladas e volume de negรณcios de โ‚ฌ22,3 bilhรตes em 2021, um incremento significativo de 74% por comparaรงรฃo a 2020. Com o closing price de โ‚ฌ177,6 por aรงรฃo a 30 de dezembro de 2022 e um price target estimado de โ‚ฌ207,84 para o o final de 2023, a recomendaรงรฃo para a Hapag-Lloyd รฉ "Hold", tendo em conta a sua elevada exposiรงรฃo a ciclos econรณmicos e o elevado perfil de risco do seu modelo de negรณcio, caracterizado por um segmento รบnico. O price target foi determinado com base no modelo DCF como metodologia principal, apoiado por outros mรฉtodos absolutos e relativos, representando deste modo um potencial de valorizaรงรฃo de 17,03%. A empresa espera demonstrar uma performance financeira sรณlida em 2022, devido ร s tarifas de frete extremamente elevadas, resultantes de interrupรงรตes na cadeia de abastecimento e elevada procura agregada de bens.info:eu-repo/semantics/publishedVersio
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