Title:	INDUSTRIAL MODELING AND PROGRAMMING LANGUAGE (IMPL) FOR COMPLEX DATA ANALYTICS AND DECISION-MAKING PROBLEMS
Author(s):	Brenno Menezes, Jeffrey Kelly, Munier Elsherif and Robert Franzoi
ISBN:	978-989-8704-42-9
Editors:	Yingcai Xiao, Ajith Abraham, Guo Chao Peng and Jörg Roth
Year:	2022
Edition:	Single
Keywords:	IMPL, Programming Languages, Data Analytics, Decision-Making, Structured and Semantic Programming
Type:	Full Paper
First Page:	168
Last Page:	175
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	The Industrial Modeling and Programming Language (IMPL) is a sophisticated computational system for tackling large-scale and complex-scope data analytics and decision-making problems in the engineering and operations research fields. Although the software provides both scalar-based (e.g., MATLAB) and set-based modeling (GAMS, AIMMS, AMPL, MOSEL, OPL) as found in most of the modeling programming languages (MPL), IMPL is mainly built as ad hoc and specialized proprietary industrial software platform (closed-source). This is made by built-in structured- and semantic-based modeling that rely on the Unit-Operation-Port-State Superstructure (UOPSS) constructs (for the structure) and Quantity-Logic-Quality Phenomena (QLQP) concepts (for the semantics). Besides the data analytics package included in the IMPL-DATA version, this mathematical programming language enables the modeling and solving of industrial-scale discrete, nonlinear, and dynamic optimization, estimation, and simulation problems found in both the batch and continuous process industries. IMPL's data analytics (DA) includes several functions to concatenate, substitute, sort, cluster, regress, etc., datasets in order to categorize, reduce, track, etc., the given data. IMPL's decision-making (DM) is suitable to support the modeling and solving of design, planning, scheduling, coordinating production, process, operations, optimization, and control problems as well as parameter identification, estimation, and data reconciliation problems. IMPL includes links to various community and commercial LP, QP, MILP and NLP solvers. To summarize, IMPL may be considered as a confluence with the scientific disciplines of applied engineering, management, information and computer science, statistical and data science.
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