Title
Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language
Abstract
The authors consider optimization problems expressed in Decision Guidance Query Language that may involve linear arithmetic constraints, as well as finite domain and binary variables. They focus on Distributed Manufacturing Network optimization problems in which only a part of the problem is dynamic, i.e., the demand for the output products in a manufacturing network, whereas the rest of the problem is static, i.e., the connectivity graph of the assembly processes and the cost functions of machines. The authors propose the Online Decomposition Algorithm based on offline preprocessing that optimizes each static problem component for discretized values of shared constraint variables, and approximate the optimal aggregated utility functions. The Online Decomposition Algorithm uses the pre-processed approximated aggregated cost functions to decompose the original problem into smaller problems, and utilizes search heuristics for the combinatorial part of the problem based on the pre-processed look-up tables. They also conduct an initial experimental evaluation which shows that the Online Decomposition Algorithm, as compared with Mixed Integer Linear Programming, provides an order of magnitude improvement in terms of both computational time and the quality of found solutions for a class of problems for which pre-processing is possible.
Year
DOI
Venue
2012
10.4018/jdsst.2012070103
IJDSST
Keywords
Field
DocType
smaller problem,static problem component,optimal aggregated utility function,optimization problem,original problem,cost function,manufacturing network optimization problem,combinatorial part,online decomposition algorithm,decision guidance query language,pre-processed approximated aggregated cost,manufacturing networks
Data mining,Discretization,Query language,Mathematical optimization,Computer science,Decision support system,Distributed manufacturing,Theoretical computer science,Integer programming,Preprocessor,Heuristics,Optimization problem
Journal
Volume
Issue
ISSN
4
3
1941-6296
Citations 
PageRank 
References 
1
0.39
12
Authors
3
Name
Order
Citations
PageRank
Nathan Egge1326.85
Alexander Brodsky251092.99
Igor Griva3445.13