Optimization of large‐scale water transfer networks: Conic integer programming model and distributed parallel algorithms

The optimization of a multi‐echelon water transfer network (WTN) and the associate transportation and inventory systems with demand uncertainty is addressed in article. Optimal network structure, facility locations, operation capacities, as well as the inventory and transportation decisions can be s...

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Bibliographic Details
Published in:AIChE journal Vol. 63; no. 5; pp. 1566 - 1581
Main Authors: Li, Li‐Juan, Zhou, Rui‐Jie
Format: Journal Article
Language:English
Published: New York American Institute of Chemical Engineers 01.05.2017
Subjects:
Algorithms
Chemical engineering
Conics
distributed parallel optimization
Integer programming
Lagrangean relaxation
message passing interface
mixed integer conic quadratic programming
Nonlinear programming
Optimization
Searching
Transportation
Transportation networks
Water distribution
Water transfer
water transfer networks
China, People's Rep
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary:The optimization of a multi‐echelon water transfer network (WTN) and the associate transportation and inventory systems with demand uncertainty is addressed in article. Optimal network structure, facility locations, operation capacities, as well as the inventory and transportation decisions can be simultaneously determined by the mixed integer nonlinear programming (MINLP) model which includes bilinear, square root and nonlinear fractional terms. By exploiting the properties of this model, we reformulate the MINLP problem as a conic integer optimization model. To overcome the memory and computing bandwidth limitations caused by the huge number of active nodes in the branch‐and‐bound search tree, novel distributed parallel optimization algorithms based on Lagrangean relaxation and message passing interface as well as their serial versions are proposed to solve the resulting conic integer programming model. A regional WTN in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithms. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1566–1581, 2017
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15505