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Planning demand-driven disassembly for remanufacturing

Author: Ian M Langella
Publisher: Wiesbaden : Deutscher Universität-Verlag : GWV Fachverlage, 2007.
Series: Gabler Edition Wissenschaft., Produktion und Logistik.
Edition/Format:   eBook : Document : English : 1. AuflView all editions and formats
Summary:
Remanufacturing has received increasing attention in the recent past as more companies engage in product recovery management. Ian M. Langella examines the planning of disassembly for remanufacturing of used products, yielding components which are reassembled into "as good as new" items. Through a thorough analysis of the underlying planning problem, fundamental insights are attained and heuristic solution methods  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Langella, Ian M.
Planning Demand-Driven Disassembly for Remanufacturing.
Wiesbaden : Deutscher Universitats-Verlag : GWV Fachverlage, 2007
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ian M Langella
ISBN: 9783835095953 3835095951 9783835007758 3835007750
OCLC Number: 213080348
Description: 1 online resource.
Contents: Preliminary; Introduction; Fundamentals; Planning disassembly with deterministic yields; Planning disassembly with stochastic yields; Conclusion and outlook; Back matter.
Series Title: Gabler Edition Wissenschaft., Produktion und Logistik.
Responsibility: Ian M. Langella ; with a foreword by Karl Inderfurth.

Abstract:

Remanufacturing has received increasing attention in the recent past as more companies engage in product recovery management. Ian M. Langella examines the planning of disassembly for remanufacturing of used products, yielding components which are reassembled into "as good as new" items. Through a thorough analysis of the underlying planning problem, fundamental insights are attained and heuristic solution methods are developed and tested. Although the heuristics exhibit good performance, they remain simple enough to be applied to industrial-sized problems. The author considers both settings where yields are deterministic and stochastic and where the amount of returned products is constrained.

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