Research
Mixed-Integer Constrained Grey-Box Optimization based on Dynamic Surrogate Models and Approximated Interval Analysis
Collaborators: A. Brodsky
10th International Conference on Operations Research and Enterprise Systems (2021)
Abstract
In this paper an algorithmic framework, called GreyOpt, is proposed for the heuristic global optimization of simulations over general constrained mixed-integer sets, where simulations are expressed as a grey-box, i.e. computations using a mix of (1) closed-form analytical expressions, and (2) evaluations of numerical black- box functions that may be non-differentiable and computationally expensive. GreyOpt leverages the partially analytical structure of such problems to dynamically construct differentiable surrogate problems for multiple regions of the search space. These surrogate problems are then used in conjunction with a derivative-based method to locally improve sample points in each region. GreyOpt extends Moore interval arithmetic for approximating the intervals of grey-box objective and constraint functions by fitting quadric surfaces that attempt to roughly underestimate and overestimate embedded black-box functions. This serves as the foundation of a recursive partitioning technique that GreyOpt uses to refine the best points found in each region. An experimental study of GreyOpt’s performance is conducted on a set of grey-box optimization problems derived from MINLPLib, where the ratio of black-box function evaluations to analytical expressions is small. The results of the study show that GreyOpt significantly outperforms three derivative-free optimization algorithms on these problems.
Factory optima: a web-based system for composition and analysis of manufacturing service networks based on a reusable model repository
Collaborators: A. Brodsky, M. Krishnamoorthy, W.Z. Bernstein, and D.A. Menasce
International Journal of Computer Integrated Manufacturing (2019)
Abstract
This paper reports on the development of Factory Optima, a web-based system that allows manufacturing process engineers to compose, optimise and perform 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 performance models representing (1) unit manufacturing processes, (2) base contract services and (3) a composite steady-state service network. The proposed framework allows process engineers to hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and perform deterministic optimisation based on mathematical programming and Pareto-optimal trade-off analysis. Factory Optima is demonstrated using a case study of a service network for a heat sink product which involves contract vendors and manufacturing activities, including cutting, shearing, Computer Numerical Control (CNC) machining with milling and drilling operations, quality inspection, finishing, and assembly.
Unity Decision Guidance Management System: Analytics Engine and Reusable Model Repository
Collaborators: A. Brodsky and J. Luo
19th International Conference on Enterprise Information Systems (2017)
Abstract
Enterprises across all industries increasingly depend on decision guidance systems to facilitate decision-making across all lines of business. Despite significant technological advances, current paradigms for developing decision guidance systems lead to a tight-integration of the analytic models, algorithms and underlying tools that comprise these systems, which inhibits both reusability and interoperability. To address these limitations, this paper focuses on the development of the Unity analytics engine, which enables the construction of decision guidance systems from a repository of reusable analytic models that are expressed in JSONiq. Unity extends JSONiq with support for algebraic modeling using a symbolic computation-based technique and compiles reusable analytic models into lower-level, tool-specific representations for analysis. In this paper, we also propose a conceptual architecture for a Decision Guidance Management System, based on Unity, to support the rapid development of decision guidance systems. Finally, we conduct a preliminary experimental study on the overhead introduced by automatically translating reusable analytic models into tool-specific representations for analysis. Initial results indicate that the execution times of optimization models that are automatically generated by Unity from reusable analytic models are within a small constant factor of that of corresponding, manually-crafted optimization models.
Manufacturing and Contract Service Networks: Composition, Optimization and Tradeoff Analysis based on a Reusable Repository of Performance Models
Collaborators: A. Brodsky, M. Krishnamoorthy, W.Z. Bernstein, and D.A. Menasce
2017 IEEE International Conference on Big Data (Big Data), 2nd Symposium on Data Analytics for Advanced Manufacturing
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 performance models for (1) unit manufacturing processes, (2) base contract services, and (3) a composite steady-state 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 Computer Numerical Control (CNC) machining with milling and drilling steps, quality inspection, finishing and assembly.
A system and architecture for reusable abstractions of manufacturing processes
Collaborators: A. Brodsky, M. Krishnamoorthy, and W.Z. Bernstein
2016 IEEE International Conference on Big Data (Big Data)
Abstract
In this paper we report on the development of a system for managing a repository and conducting analysis and optimization on manufacturing performance models. The repository is designed to contain (1) unit manufacturing process performance models, (2) composite performance models representing production cells, lines, and facilities, (3) domain specific analytical views, and (4) ontologies and taxonomies. Initial implementation includes performance models for milling and drilling as well as a composite performance model for machining. These performance models formally capture (1) the metrics of energy consumption, CO2 emissions, tool wear, and cost as a function of process controls and parameters, and (2) the process feasibility constraints. The initial scope of the system includes (1) an Integrated Development Environment and its interface, and (2) simulation and deterministic optimization of performance models through the use of Unity Decision Guidance Management System.
Decision Guidance Analytics Language (DGAL) and Management System (DGMS): Syntax, Semantics and Model Knowledge Base
Collaborators: A. Brodsky and J. Luo
2016 Technical Report · Department of Computer Science · George Mason University
Abstract
Decision guidance systems are a class of decision support systems that are geared toward producing actionable recommendations, typically based on formal analytical models and techniques. This paper proposes the Decision Guidance Analytics Language (DGAL) for easy iterative development of decision guidance systems. DGAL allows the creation of modular, reusable and composable models that are stored in the analytical knowledge base independently of the tasks and tools that use them. Based on these unified models, DGAL supports declarative queries of (1) data manipulation and computation, (2) what-if prediction analysis, (3) deterministic and stochastic decision optimization, and (4) machine learning, all through formal reduction to specialized models and tools, and in the presence of uncertainty.
Social Sifter: An Agent-Based Recommender System to Mine the Social Web
Collaborators: R. Rabbi, G. Yu, L. Kerschberg, and A. Brodsky
2012 STIDS Conference
Abstract
With the recent growth of the Social Web, an emerging challenge is how we can integrate information from the heterogeneity of current Social Web sites to improve semantic access to the information and knowledge across the entire World Wide Web, the Web. Interoperability across the Social Web sites make the simplest of inferences based on data from different sites challenging. Even if such data were interoperable across multiple Social Web sites, the ability of meaningful inferences of a collective intelligence system depends on both its ability to marshal such semantic data, as well as its ability to accurately understand and precisely respond to queries from its users. This paper presents the architecture for Social Sifter, an agent-based, collective intelligence system for assimilating information and knowledge across the Social Web. A health recommender system prototype was developed using the Social Sifter architecture, which recommends treatments, prevention advice, therapies for ailments, and doctors and hospitals based on shared experiences available on the Social Web.