Könyv Optimization of Integrated Supply Chain Planning under Multiple Uncertainty Juping Shao

Optimization of Integrated Supply Chain Planning under Multiple Uncertainty

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
19 797 Ft
The subject of this book is supply chain logistics planning optimization under multiple uncertaintie...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2016
oldal
188
EAN
9783662516171
ISBN
3662516179
Enbook ID
15201933
Súly
3226
Méretek
155 x 235 x 11

Teljes leírás

The subject of this book is supply chain logistics planning optimization under multiple uncertainties, the key issue in supply chain management. Focusing on the strategic-alliance three-level supply chain, the model of supply chain logistics planning was established in terms of the market prices and the market requirements as random variables of manufactured goods with random expected value programming theory, and the hybrid intelligence algorithm solution model was designed. Aiming at the decentralized control supply chain, in which the nodes were unlimited expansion, the chance-constrained stochastic programming model was created in order to obtain optimal decision-making at a certain confidence level. In addition, the hybrid intelligence algorithm model was designed to solve the problem of supply chain logistics planning with the prices of the raw-materials supply market of the upstream enterprises and the prices of market demand for products of the downstream enterprises as random variables in the supply chain unit. Aimed at the three-stage mixed control supply chain, a logistics planning model was designed using fuzzy random programming theory with customer demand as fuzzy random variables and a hybrid intelligence algorithm solution was created. The research has significance both in theory and practice. Its theoretical significance is that the research can complement and perfect existing supply chain planning in terms of quantification. Its practical significance is that the results will guide companies in supply chain logistics planning in the uncertain environment.

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