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Manufacturing and Contract Service Networks: Composition, Optimization and Tradeoff Analysis based on a Reusable Repository of Performance Models

Published

Author(s)

Alexander Brodsky, Mohan Krishnamoorthy, Mohomad O. Nachawati, William Z. Bernstein, Daniel A. Menasce

Abstract

In this paper we report on the development of a software framework and system for composition, optimization and trade-off analysis of manufacturing and contract service networks based on a reusable repository of performance models. Performance models formally describe process feasibility constraints and metrics of interest, such as cost, throughput and CO2 emissions, as a function of fixed and control parameters, such as equipment and contract properties and settings. The repository contains the performance models library for (1) unit manufacturing processes, (2) an initial model library of base contract services, and (3) a service network. The proposed framework allows process engineers to (1) hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and (2) perform deterministic optimization based on mathematical programming and Pareto-optimal trade-off analysis. We case study the framework on a service network for a heat sink product which involves contract vendors and manufacturers, unit manufacturing process services including cutting/shearing and CNC machining with milling and drilling steps, quality inspection, finishing and assembly
Conference Dates
December 11-14, 2017
Conference Location
Boston, MA, US
Conference Title
2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics for
Advanced Manufacturing

Keywords

supply chain, performance models, model repository, optimization, decision support, service composition

Citation

Brodsky, A. , Krishnamoorthy, M. , Nachawati, M. , Bernstein, W. and Menasce, D. (2018), Manufacturing and Contract Service Networks: Composition, Optimization and Tradeoff Analysis based on a Reusable Repository of Performance Models, 2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics for Advanced Manufacturing, Boston, MA, US (Accessed April 22, 2024)
Created January 14, 2018, Updated October 12, 2021