LEADER 04511nam 22006735 450 001 9910298490303321 005 20200919111606.0 010 $a3-662-47250-3 024 7 $a10.1007/978-3-662-47250-7 035 $a(CKB)3710000000416736 035 $a(EBL)2094860 035 $a(SSID)ssj0001500762 035 $a(PQKBManifestationID)11848355 035 $a(PQKBTitleCode)TC0001500762 035 $a(PQKBWorkID)11520248 035 $a(PQKB)11153681 035 $a(DE-He213)978-3-662-47250-7 035 $a(MiAaPQ)EBC2094860 035 $a(iGPub)SPNA0040591 035 $a(PPN)186027001 035 $a(EXLCZ)993710000000416736 100 $a20150527d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimization of Integrated Supply Chain Planning under Multiple Uncertainty /$fby Juping Shao, Yanan Sun, Bernd Noche 205 $a1st ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (197 p.) 300 $aDescription based upon print version of record. 311 $a3-662-47249-X 320 $aIncludes bibliographical references. 327 $aPreface -- List of Main Symbols -- Introduction -- Literature Overview -- Customers demand forecasting dynamic equation models and weight distribution method of combination prediction -- Strategic alliance model for supply chain with parameters -- Model and algorithm of decentralized control supply chain logistics planning under uncertain environment -- Optimization of a hybrid supply chain under uncertain situation -- Conclusion and Future Work. 330 $aThe 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. 606 $aBusiness logistics 606 $aDevelopment economics 606 $aMarket research 606 $aSupply Chain Management$3https://scigraph.springernature.com/ontologies/product-market-codes/519030 606 $aDevelopment Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W42000 606 $aMarket Research/Competitive Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/513030 615 0$aBusiness logistics. 615 0$aDevelopment economics. 615 0$aMarket research. 615 14$aSupply Chain Management. 615 24$aDevelopment Economics. 615 24$aMarket Research/Competitive Intelligence. 676 $a330 676 $a338.9 676 $a658.5 676 $a658.83 700 $aShao$b Juping$4aut$4http://id.loc.gov/vocabulary/relators/aut$01061743 702 $aSun$b Yanan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aNoche$b Bernd$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298490303321 996 $aOptimization of Integrated Supply Chain Planning under Multiple Uncertainty$92519950 997 $aUNINA