Mathematical Programing 4.0 for Industry 4.0 Cyber-Physical Systems. (Book and Advanced Course)
Jesus Velasquez-Bermudez
Decision-Making Artificial Intelligence Entrepreneur & Researcher - Chief Scientific Officer
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2. Book & Advanced Course
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ARTIFICIAL HYPOTHALAMUS & MATHEMATICAL PROGRAMING 4.0 FOR INDUSTRY 4.0 CYBER-PHYSICAL SYSTEMS.?
Topics (Draft):
The book presents the results of research and technological development carried out by the Eng. Jesús Maria Velasquez-Bermudez, Ph. D., during 48 years of practice of the profession of Math Programmer, starting from the year 1973; user of the theories of large-scale optimization since 1978, primarily the theory of J. F. Benders. The collection of written material has been funded entirely by DecisionWare (DW), company with about twenty-five years (since 1995) in the market of the Mathematical Programming that was adquired by RCADT in 2021. The book collects experience and knowledge generated in projects carried out since 1978.
All this knowledge is preserved at the level of the state-of-the-art of Mathematical Programming and the Computer Science by the cognitive robots supported by RCADT (OPTEX and OPTEX-SAAM), as the way to keep updated models that have worked and evolved since 1991. At the present time, the software is being updated in accordance with the new concepts of cyber physical spaces that support the new industrial revolution 4.0. that connect the industrial growth with the assessment of the knowledge and with the ability to produce new knowledge from past knowledge and new information which generates continually big data, giving origin to the knowledge-intensive industries.
The book and the Course are aimed at professionals, researchers, teachers, advanced students who are interested in generating new knowledge through mathematics methodologies (algorithms) for the markets of Advanced Analytics solutions (predictive and prescriptive). and the generation of software according to: i) parallel computing (multi-CPU with multi-cores, multi-GPUs and multi-tensor processors), and ii) distributed optimization (multiple agents operating simultaneously in real time).
The book is in phase of editing and review. All the material is already written (95%), but it is in the phase of integration. The "final" version of the book will be available in July 2022.
STRUCTURE - PARTS
The Book has four parts
Whose aim is to present the Mathematical Foundations for the solution of the constrained optimization problems. It includes the economic interpretation of optimization models and the MPEC (Mathematical Programming with Equilibrium Constraints) problems. The different formats of the problems of optimization and algorithms to solve them are also presented briefly.
Oriented to the presentation of the theoretical foundations of large-scale optimization, with emphasis on the atomization of the problems to solve them in parallel computers and/or in grid of computers. The goal is to present the integrated vision of the Real-Time Distributed Optimization (RTDO) as the mathematical way to modeling the cyber-physical systems of the Industry 4.0; that is characterized by integration of multiple algorithms which continuously cooperate to solve the problems of the multi-agent system, using independently the multiple computers of the agents and sharing information to achieve an optimal solution. This theory is built using the basic theories of partition and decomposition of problems (Benders, Lagrange, Dantzig-Wolfe,...).
It presents a new view of the Mathematical Programming (Mathematical Programming 4.0) in order to:
i)?Structure Mathematical Modeling Process in order to standardize the formulation of models?mathematical models to make independent the algebraic formulation from de optimization technologies; this will facilitate the portability of mathematical models among multiple optimization technologies, which benefits users expanding the market of mathematical programming.
ii)?Robotization making use of the concepts of artificial intelligence to capitalize on expert systems the lessons learned as a form to facilitate the construction of the mega-models of the future.
The two points socialize the mathematical programming.
The concepts presented are based on the experience of the author (since 1991) in the development of solutions of problems through the development and use of the cognitive robot OPTEX Optimization Expert System.
The chapters of this part contain cases of real-fife application of the concepts presented based on engineering experiences of the author. Each chapter includes the algebraic formulation and the design of the computational implementation of the models; several of the chapters will include programming code in multiple optimization technologies: GAMS, C, AMPL, IBM OPL, MOSEl, PYTHON-PYOMO, , ...; These codes correspond to real cases and includes demo data.
CHAPTERS & CLASSES
● PART I – MATHEMATICAL PROGRAMMING & CYBER-PHYSICAL SYSTEMS
1.?????????The Future: Artificial Hypothalamus & Mathematical Programming 4.0
2.?????????Optimization Fundamentals
3.?????????Economics: Fundamentals
● PART II – LARGE SCALE OPTIMIZATION METHODOLOGIES
1.?????????Partitioning and Decomposition of Large-Scale Models
2.?????????J. F. Benders: Theory, Variations and Enhancements
3.?????????Stochastic Programing and Risk Management: Fundamentals
4.?????????Dynamic & Stochastic Benders Theory
5.?????????GDDP/G-SDDP – Implementation and Electric Sector Applications
6.?????????Lagrangean & Dantzig-Wolfe Relaxation & Decomposition
7.?????????Stochastic & Dynamic Cross Decomposition.
8.?????????Other Methodologies for Solving Complex Problems
领英推荐
?????????o???Generalized Disjunctive Programming
?????????o???Bilevel Optimization
?????????o???Surrogate Programming. Primal-Dual Surrogate Algorithm
?????????o???Mathematical Programming with Equilibrium Constraints (MPEC)
9.?????????Asynchronous Parallel Optimization
10.???????Real-Time Distributed?Optimization?in Cyber-Physical Systems
11.???????Parallel Optimization as an Artificial Neural Network
● PART III – A NEW PARADING: MAKING REAL–LIFE DSS USING ARTIFICIAL INTELLIGENCE
1.?????????Relational Databases and Mathematical Programing
2.?????????Structured Mathematical Modeling
3.?????????Tutorial: An example of Structured Mathematical Modeling
4.?????????Optimization Expert Systems
5.?????????Cognitive Robots: Smart Algorithms that Make Advanced Analytical Algorithms
6.?????????OPTEX Expert Optimization System: A Cognitive Robot for Mathematical Programming
7.?????????Machine Learning (Predictive Advanced Analytics) using Mathematical Programming
● PART IV – REAL-LIFE APPLICATIONS USING MATHEMATICAL PROGRAMMING 4.0 – GROUP 1
1.?????????Tutorial: Vehicle Routing Problem?(VRP) using MP 4.0 and OPTEX-GAMS
2.?????????Enterprise Wide Optimization (S&OP & IBP Modeling)
3.?????????Scheduling Industrial Systems using Continuous Time Modeling
4.?????????Multi-Echelon Dynamic Inventory using Stochastic Optimization
5.?????????Blending in Industrial Processes. Case: Oil Production, Distribution and Marketing
6.?????????Modeling Chemical Process. Case: Advanced Optimization in Cement Plants
7.?????????Oil Pipelines Real-Time Optimization using Non-Convex Hull Approximations
8.?????????Predictive Advanced Analytics I: Market Share Modeling using Syndicated Databases
9.?????????A Decision Support System for Integrated Logistic Regional Planning
10.???????Smart Grids Real Time Distributed Optimization
● PART V – REAL-LIFE APPLICATIONS USING MATHEMATICAL PROGRAMMING 4.0 – GROUP 2
1.?????????Transport Revenue Management Using Machine Learning & Optimization
2.?????????Continuous Time-Tabling Optimization – Case: Port Operations
3.?????????Discrete Time-Tabling Optimization – Case: University/College Scheduling
4.?????????Sourcing Optimization
5.?????????Modeling Energy Markets
6.?????????Industrial Assets (Plant Maintenance & Machines/Vehicles) Maintenance Optimization
7.?????????Predictive Advanced Analytics II: Fundamentals of Revenue Management
8.?????????Modeling Bio-Industrial Supply Chains
9.?????????Resilient Logistics Networks Design. Using Stochastic Programing & Risk Management. A Case in the Beverage Industry
10.???????Financial Modeling. Fundamentals?of Assets Liabilities Management (ALM)
REVIEWERS & PROFESSORS
During the book review process, reviewers of the final versions of the chapters are required. Two type of reviewers are considered:
1.???????Mathematical Programming Professors: Professors that teach and/or research in Mathematical Programming or Applications of Mathematical Programming.
2.???????Operations Research Professionals: Professional interested in the book and to help during the edition process.
If you are interested please send an email to jesus.velasquez@decisionware,net
Lecturer/Higher Education
5 年Good work Prof. I want to get a copy of the book!
Jesús, congratulations and thanks for sharing!! Let us have a coffee to explore new opportunities. Regards!!
Decision-Making Artificial Intelligence Entrepreneur & Researcher - Chief Scientific Officer
5 年For economic topics download PDF Book Information: https://www.doanalytics.net/Documents/DW-Mathematical-Programing-Industry-Cyber-Physical-Systems.pdf? Regards
Energy Quant | Energy Modeller | Data Analytics | Forecasting | Decision Making Under Uncertainty #RWRI18
5 年I'm interested in buying this. Where could I buy it??
Business/Consulting Partner at Tompkins Ventures
5 年Wow!