Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm

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Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II : A Lagrangean decomposition algorithm. / Yongheng, Jiang; Rodriguez, Maria Analia; Harjunkoski, Iiro; Grossmann, Ignacio E.

In: Computers and Chemical Engineering, Vol. 62, 05.03.2014, p. 211-224.

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@article{5736906517c04d77b244ea4cf23c348c,
title = "Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm",
abstract = "In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2013), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.",
keywords = "Adaptive piecewise linearization, Lagrangean decomposition, Supply chain",
author = "Jiang Yongheng and Rodriguez, {Maria Analia} and Iiro Harjunkoski and Grossmann, {Ignacio E.}",
year = "2014",
month = "3",
day = "5",
doi = "10.1016/j.compchemeng.2013.11.014",
language = "English",
volume = "62",
pages = "211--224",
journal = "Computers and Chemical Engineering",
issn = "0098-1354",
publisher = "Elsevier BV",

}

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TY - JOUR

T1 - Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II

T2 - A Lagrangean decomposition algorithm

AU - Yongheng, Jiang

AU - Rodriguez, Maria Analia

AU - Harjunkoski, Iiro

AU - Grossmann, Ignacio E.

PY - 2014/3/5

Y1 - 2014/3/5

N2 - In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2013), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.

AB - In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2013), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.

KW - Adaptive piecewise linearization

KW - Lagrangean decomposition

KW - Supply chain

UR - http://www.scopus.com/inward/record.url?scp=84894907131&partnerID=8YFLogxK

U2 - 10.1016/j.compchemeng.2013.11.014

DO - 10.1016/j.compchemeng.2013.11.014

M3 - Article

VL - 62

SP - 211

EP - 224

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

SN - 0098-1354

ER -

ID: 6320792