Production scheduling for parallel machines
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AlbakiReads Editorial Hybrid optimization for parallel-machine production scheduling
Hybrid optimization for parallel-machine production scheduling
Daniel Pacheco examines an integrated production-planning problem in which jobs must be assigned and sequenced across multiple machines while accounting for sequence-dependent setup requirements. The book compares hybrid solution methods built around a two-level decomposition: genetic algorithms handle machine assignment and job order, while branch-and-cut optimization determines batch sizes and inventory decisions. It also presents a performance comparison that identifies a Teaching-Learning-Based Optimization (TLBO) variant as the strongest of the methods considered, balancing solution quality with computation time. With its focus on modeling and algorithmic solution design, the book centers on the technical links between scheduling, lot sizing, and inventory planning.
Good fit for readers who enjoy
- Operations research students studying scheduling and optimization
- Researchers working on parallel-machine or integrated production-planning models
- Industrial engineering professionals interested in heuristic and exact optimization methods
Themes
Parallel-machine schedulingSequence-dependent setupsGenetic algorithmsBranch-and-cut optimizationLot sizing and inventory planning
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Book Overview This book presents a comparison of hybrid solution methods for integrated multi-machine production sizing and scheduling problems with se...
This book presents a comparison of hybrid solution methods for integrated multi-machine production sizing and scheduling problems with sequence-dependent setups. The proposed methods are based on the decomposition of the problem into 2 levels: In the first level, the assignment of jobs to machines and their sequencing is performed via genetic algorithms, the solution obtained at this level is used to determine the sizing of batches and inventories in the second level in an exact way using the branch and cut method.The results showed the variant based on the Teaching-learning based optimization (TLBO) algorithm as the best in performance with a good solution quality in a reasonable computation time.
Book Details Format: Paperback | Pages: 72 | Language: English | Publisher: OUR KNOWLEDGE PUB | ISBN: 6208376718
FormatPaperback
Pages72
LanguageEnglish
ISBN6208376718
EAN9786208376710
PublisherOUR KNOWLEDGE PUB
Publication Date2024-12-13
AccessoriesNo Accessory
ConditionNew
Product TypeQUALITY PAPERBACK BOOKS
Weight0.26 Pounds
Length9.0 Inches
Width6.0 Inches
Height0.17 Inches
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