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Titlebook: Supply Chain Disruption Management; Using Stochastic Mix Tadeusz Sawik Book 2020Latest edition The Editor(s) (if applicable) and The Author

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發(fā)表于 2025-3-21 16:21:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Supply Chain Disruption Management
副標題Using Stochastic Mix
編輯Tadeusz Sawik
視頻videohttp://file.papertrans.cn/883/882021/882021.mp4
概述Presents practical applications of stochastic mixed integer programming for integrated decision-making in supply chain disruption management.Uses computational examples for commercially available soft
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Supply Chain Disruption Management; Using Stochastic Mix Tadeusz Sawik Book 2020Latest edition The Editor(s) (if applicable) and The Author
描述.This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. .Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX
出版日期Book 2020Latest edition
關鍵詞Supply Chain Management; Supply Chain Risk; Stochastic Mixed Integer Programming; Supply Chain Disrupti
版次2
doihttps://doi.org/10.1007/978-3-030-44814-1
isbn_softcover978-3-030-44816-5
isbn_ebook978-3-030-44814-1Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
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Introduction,e.g., Simchi-Levi et al. .) consists of over 50 manufacturing plants, ten tiers of suppliers, including 1400 tier 1 supplier companies with 4400 manufacturing sites in over 60 countries. Six million vehicles produced annually, require 55,000 different part types with a complex bill of materials.
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Introduction, production plants and distribution centers, where supplied parts are assembled into finished products and next distributed to customers. Figure 1.1 shows a schematic diagram of a multi-tier supply chain network,where each vertical level (suppliers, producers, distribution centers, customers) is cal
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Selection of Static Supply Portfolio a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. The selection of suppliers and allocation of orders is based on price and quali
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發(fā)表于 2025-3-22 23:50:56 | 只看該作者
Selection of Dynamic Supply Portfolioy risks is considered. Given a set of customer orders for finished products, the decision maker needs to decide from which supplier and when to purchase product-specific parts required for each customer order to meet customer requested due date at a low cost and to mitigate the impact of supply chai
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Integrated Selection of Supply Portfolio and Scheduling of Productionion risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographic regions: in the producer’s region (domestic suppliers) and outside the producer’s region (foreign suppliers). The supplies are subject to independent random local disruptions that a
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