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＜電子ブック＞
Demand Flexibility in Supply Chain Planning / by Joseph Geunes
(SpringerBriefs in Optimization. ISSN:2191575X)

版	1st ed. 2012.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2012
本文言語	英語
大きさ	XIII, 90 p : online resource
著者標目	*Geunes, Joseph author SpringerLink (Online service)
件　名	LCSH:Operations research LCSH:Management science LCSH:Industrial Management LCSH:Mathematical optimization LCSH:Production management FREE:Operations Research, Management Science FREE:Industrial Management FREE:Optimization FREE:Operations Management
一般注記	1. Scope of Problem Coverage and Introduction -- 2. Production and Inventory Planning Models with Demand Shaping -- 3. EOQ-Type Models with Demand Selection -- 4. Single-Period Stochastic Inventory Planning with Demand Selection -- 5. Dynamic Lot Sizing with Demand Selection and the Pricing Analog -- 6. Dynamic Lot Sizing with Market Selection -- 7. Assignment and Location Problems in Supply Chains -- 8. Branch and Price Decomposition for Assignment and Location Problems -- 9. Research Challenges in Supply Chain Planning with Flexible Demand This work encapsulates the essential developments in this field into a single resource, as well as to set an agenda for further development in the field. This brief focuses on the demand flexibility in supply chains with fragmented results distributed throughout the literature. These results have strong implications for managing real-world complex operations planning problems. This book exploits dimensions of demand flexibility in supply chains and characterizes the best fit between demand properties and operations capabilities and constraints. The origins and seminal works are traced in integrated demand and operations planning and an in-depth documentation is provided for the current state of the art. Systems with inherent costs and constraints that must respond to some set of demands at a minimum cost are examined.  Crucial unanswered questions are explored and the high-value research directions  are highlighted for both practice and for the development of new and interesting optimization models and algorithms HTTP:URL=https://doi.org/10.1007/978-1-4419-9347-2

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