Publications

For the full list please visit Google Scholar. Abstracts for the following publications are below.

  1. Temporal Manufacturing Query Language (tMQL) for Domain Specific Composition, What-if Analysis, and Optimization of Manufacturing Processes With Inventories (with A. Brodsky, D. Menascé). Technical Report. Department of Computer Science, George Mason University, Fairfax, VA, 22030, Tech. Rep. GMU-CS-TR-2014-3, 2014. [Online]. Also presented at INFORMS Computing Society Conf., Richmond, VA, January 11-13, 2015.
  2. Optimizing Stochastic Temporal Manufacturing Processes with Inventories: An Efficient Heuristic Algorithm Based on Deterministic Approximations (with A. Brodsky, D. Menascé) at Operations Research and Computing: Algorithms and Software for Analytics, Proc. INFORMS Computing Society Conf., Richmond, VA, January 11-13, 2015. PDF
  3. Toward Smart Manufacturing Using Decision Analytics (with A. Brodsky, D. Menascé, G. Shao, S. Rachuri) at IEEE International Conference on Big Data (Big Data), 27-30 Oct. 2014. PDF
  4. Autonomic Smart Manufacturing (with D. Menascé, A. Brodsky), eds. I. Linden, J. Linden, S. Liu.) in the Journal of Decision Systems, Special Issue on Integrated Decision Support Systems, June 2015. PDF
  5. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models (with A. Brodsky, G. Shao, A. Narayanan, D Menascé, R. Ak) at the IEEE International Conference on Big Data (Big Data) 2015, Santa Clara, CA, 29 Oct.-1 Nov. 2015. PDF
  6. Modular Modeling & Optimization of Temporal Manufacturing Processes with Inventories (with A. Brodsky, D. Menascé). at the Hawaii International Conference on System Sciences (HICSS-49) 2016 proceedings, Kauai, HI. 5-8 Jan. 2016. PDF
  7. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models (with A. Brodsky, G. Shao, A. Narayanan, D Menascé, R. Ak) in the International Journal of Advanced Manufacturing Technology, April 2016. PDF
  8. A System and Architecture for Reusable Abstractions of Manufacturing Processes (with A. Brodsky, W. Z. Bernstein, M. O. Nachawati) submitted to the IEEE International Conference on Big Data (Big Data) 2016, Washington DC, 5-8 Dec. 2016
  9. Tree pruner: An efficient tool for selecting data from a biased genetic database (with P.Patel, M. Dimitrijevic, J. Dietrich, M. Green, C. Macken) in BMC bioinformatics Journal, January 2011. PDF

 

Publication Abstracts

  1. Temporal Manufacturing Query Language (tMQL) for Domain Specific Composition, What-if Analysis, and Optimization of Manufacturing Processes With Inventories: Smart manufacturing requires streamlining operations and optimizing processes at a global and local level. This paper considers manufacturing processes that involve physical or virtual inventories of products, parts and materials, that move from machine to machine. The inventory levels vary with time and are a function of the configuration settings of the machines involved in the process. These environments require analysis, e.g., answering what-if questions, and optimization to determine optimal operating settings for the entire process. The modeling complexities in performing these tasks are not always within the grasp of production engineers. To address this problem, the paper proposes the temporal Manufacturing Query Language (tMQL) that allows the composition of modular process models for what-if analysis and decision optimization queries. tQML supports an extensible and reusable model knowledge base against which declarative queries can be posed. Additionally, the paper describes the steps to translate the components of a tMQL model to input data files used by a commercial optimization solver.
  2. Optimizing Stochastic Temporal Manufacturing Processes with Inventories: An Efficient Heuristic Algorithm Based on Deterministic Approximations: This paper deals with stochastic temporal manufacturing processes with work-in-process inventories in which multiple products are produced from raw materials and parts. The processes may be composed of subprocesses, which, in turn may be either composite or atomic, i.e., a machine on a manufacturing floor. We assume that machines’ throughput is stochastic and so are work-in-process inventories and costs. We consider the problem of optimizing the process, that is, finding throughput expectation setting for each machine at each time point over the time horizon as to minimize the total cost of production subject to satisfying the production demand with a requested probability. To address this problem, we propose an efficient iterative heuristic algorithms that is based on (1) producing high quality candidate machine settings based on a deterministic approximation of the stochastic problem, and (2) running stochastic simulations to find the best machine setting out of the produced candidates using optimal simulation budget allocation methods. We conduct an experimental study that shows that our algorithm significantly outperforms four popular simulation-based optimization algorithms.
  3. Toward Smart Manufacturing Using Decision Analytics: This paper is focused on decision analytics for smart manufacturing. We consider temporal manufacturing processes with stochastic throughput and inventories. We demonstrate the use of the recently proposed concept of the decision guidance analytics language to perform monitoring, analysis, planning, and execution tasks. To support these tasks we define the structure of and develop modular reusable process component models, which represent data, decision/control variables, computation of functions, constraints, and uncertainty. The tasks are then implemented by posing declarative queries of the decision guidance analytics language for data manipulation, what-if prediction analysis, decision optimization, and machine learning.
  4. Autonomic Smart Manufacturing: Smart Manufacturing (SM) systems have to optimize manufacturing activities at the machine, unit or entire manufacturing facility level as well as adapting the manufacturing process on-the-fly as required by a variety of conditions (e.g., machine breakdowns and/or slowdowns) and unexpected variations in demands. This paper provides a framework for autonomic smart manufacturing (ASM) that relies on a variety of models for its support: (a) a process model to represent machines, part inventories, and the flow of parts through machines in a discrete manufacturing floor; (b) a predictive queuing network model to support the analysis and planning phases of ASM; and (c) optimization models to support the planning phase of ASM. In essence, ASM is an integrated decision support system for smart manufacturing and combines models of different nature in a seamless manner. As shown here, these models can be used to predict manufacturing time and the energy consumed by the manufacturing process, as well as finding the machine settings that minimize the energy consumed or the manufacturing time subject to a variety of constraints using non-linear optimization models. The diversity of models used affords different levels of integration and granularity in the decision making process. An example of a car manufacturing process is used throughout the paper to explain the concepts and models introduced here.
  5. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models (in IEEE-BD 2015 proceedings): In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tasks on a composite performance model.
  6. Modular Modeling & Optimization of Temporal Manufacturing Processes with Inventories: Smart manufacturing requires streamlining operations and optimizing processes at a global and local level. This paper considers temporal manufacturing processes that involve physical or virtual inventories of products, parts and materials that move through a network of subprocesses. The inventory levels vary with time and are a function of the configuration settings of the machines involved in the process. These environments require analysis, e.g., answering what-if questions, and optimization to determine optimal operating settings for the entire process. To address this problem, the paper proposes modular process components that can represent these manufacturing environments at various levels of granularity for performing what-if analysis and decision optimization queries. These components are extensible and reusable against which optimization and what-if questions can be posed. Additionally, the paper describes the steps to translate these complex components and optimization queries into a formal mathematical programming model, which is then solved by a commercial optimization solver.
  7. Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models (Article in the IJAMT): In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable knowledge base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.
  8. A System and Architecture for Reusable Abstractions of Manufacturing Processes: In this paper we report on the development of a Reusable Abstractions of Manufacturing Processes, a prototype system for managing a repository and conduct- ing 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 tear, and cost as a function of process controls and parameters, and (2) the process feasibility constraints. The initial scope of RAMP 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.
  9. Tree pruner: An efficient tool for selecting data from a biased genetic database: Background: Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses. Results: We developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner session, the editing actions are implemented in the dataset. Currently, Tree Pruner is implemented on the Influenza Research Database (IRD). The data management capabilities of the IRD allow the user to store a pruned dataset for additional pruning or for subsequent analysis. Tree Pruner can be easily adapted for use with other organisms. Conclusions: Tree Pruner is an efficient, manual tool for selecting a high-quality dataset with desired evolutionary properties from a biased database of genetic sequences. It offers an important alternative to automated approaches to the same goal, by providing the user with a dynamic, visual guide to the ongoing selection process and ultimate control over the contents (and therefore quality) of the dataset.