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Models and Algorithms for Multilevel...

Brunaud, Braulio.

Models and Algorithms for Multilevel Supply Chain Optimization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Models and Algorithms for Multilevel Supply Chain Optimization.
作者:	Brunaud, Braulio.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:	253 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
附註:	Publisher info.: Dissertation/Thesis.
附註:	Advisor: Grossmann, Ignacio E.
Contained By:	Dissertations Abstracts International80-09B.
標題:	Management.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13807688
ISBN:	9780438973770

Models and Algorithms for Multilevel Supply Chain Optimization.
Brunaud, Braulio.

Models and Algorithms for Multilevel Supply Chain Optimization. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 253 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2019.

This item is not available from ProQuest Dissertations & Theses.

A company is group of people organized for a common goal. At the core of every company there is an organizational structure dedicated to make decisions efficiently. This organization can be represented in modular modeling approach through a decision network, a collection of models joined by linking constraints. In order to optimize the decisions made for the entire network, the best model for each node must be identified, and efficient coordination algorithms must be designed. This is the approach taken in this thesis, organized in three parts: 1) single level modeling improvements, 2) integration of more than one decision level, and 3) algorithmic developments. In the first part, batch scheduling models are extended to include quality-based changeovers (QBC) are developed. QBC is a frequent operation in the chemical industry, in which cleaning operations can be avoided taking into consideration the quality of the produced materials. Three of the main modeling frameworks for batch scheduling, STN, RTN, and UOPSS, were compared and extended to include this feature. In the tactical level, planning models that include inventory policies and safety stock are proposed. These models prescribe decisions that are easier to implement for inventory managers. In the second part, the integration of warehouse location with planning under discrete transportation costs is considered. Other modeling features addressed are safety stock with risk-pooling effect, and warehouse contracting constraints. To solve large-scale instances, simplified formulations and multistage heuristics are developed. The integration of planning and scheduling is also addressed. Integrated models with shorter scheduling time horizons are proposed. Models in which planners and schedulers communicate through inventory policies, are also proposed. They are evaluated in a tailored simulation framework. The simulation results that there are potential benefits in using the novel approaches proposed. Finally, algorithmic improvements are also developed. A Lagrangean decomposition for supply chain planning based in products is proposed, together with an alternative subgradient method, the probing subgradient method. Additionally, PlasmoAlgorithms is presented. A Julia package to facilitate the implementation of decomposition algorithms. The algorithms included are Lagrangean decomposition, multilevel Benders decomposition, and multilevel Cross decomposition. The latter had only been developed for two-stage stochastic programming. The package can accelerate research by enabling a faster implementation and extension of algorithms for optimization of large-scale problems.

ISBN: 9780438973770Subjects--Topical Terms:

180005
Management.

Models and Algorithms for Multilevel Supply Chain Optimization.
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520 $a A company is group of people organized for a common goal. At the core of every company there is an organizational structure dedicated to make decisions efficiently. This organization can be represented in modular modeling approach through a decision network, a collection of models joined by linking constraints. In order to optimize the decisions made for the entire network, the best model for each node must be identified, and efficient coordination algorithms must be designed. This is the approach taken in this thesis, organized in three parts: 1) single level modeling improvements, 2) integration of more than one decision level, and 3) algorithmic developments. In the first part, batch scheduling models are extended to include quality-based changeovers (QBC) are developed. QBC is a frequent operation in the chemical industry, in which cleaning operations can be avoided taking into consideration the quality of the produced materials. Three of the main modeling frameworks for batch scheduling, STN, RTN, and UOPSS, were compared and extended to include this feature. In the tactical level, planning models that include inventory policies and safety stock are proposed. These models prescribe decisions that are easier to implement for inventory managers. In the second part, the integration of warehouse location with planning under discrete transportation costs is considered. Other modeling features addressed are safety stock with risk-pooling effect, and warehouse contracting constraints. To solve large-scale instances, simplified formulations and multistage heuristics are developed. The integration of planning and scheduling is also addressed. Integrated models with shorter scheduling time horizons are proposed. Models in which planners and schedulers communicate through inventory policies, are also proposed. They are evaluated in a tailored simulation framework. The simulation results that there are potential benefits in using the novel approaches proposed. Finally, algorithmic improvements are also developed. A Lagrangean decomposition for supply chain planning based in products is proposed, together with an alternative subgradient method, the probing subgradient method. Additionally, PlasmoAlgorithms is presented. A Julia package to facilitate the implementation of decomposition algorithms. The algorithms included are Lagrangean decomposition, multilevel Benders decomposition, and multilevel Cross decomposition. The latter had only been developed for two-stage stochastic programming. The package can accelerate research by enabling a faster implementation and extension of algorithms for optimization of large-scale problems.
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