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Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language

Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language

Nathan Egge, Alexander Brodsky, Igor Griva
Copyright: © 2012 |Volume: 4 |Issue: 3 |Pages: 18
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781466611535|DOI: 10.4018/jdsst.2012070103
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MLA

Egge, Nathan, et al. "Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language." IJDSST vol.4, no.3 2012: pp.25-42. http://doi.org/10.4018/jdsst.2012070103

APA

Egge, N., Brodsky, A., & Griva, I. (2012). Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language. International Journal of Decision Support System Technology (IJDSST), 4(3), 25-42. http://doi.org/10.4018/jdsst.2012070103

Chicago

Egge, Nathan, Alexander Brodsky, and Igor Griva. "Distributed Manufacturing Networks: Optimization via Preprocessing in Decision Guidance Query Language," International Journal of Decision Support System Technology (IJDSST) 4, no.3: 25-42. http://doi.org/10.4018/jdsst.2012070103

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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.

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