The document introduces the Capital Investment & Facilities Location, Industrial Modeling Framework (CIFL-IMF) which models capital investment and facilities location problems. It describes a flowsheet example involving multiple unit operations producing and storing products. The framework uses a Unit-Operation-Port-State Superstructure (UOPSS) and Quantity-Logic-Quality Phenomena (QLQP) modeling approach to represent complex industrial systems. It formulates the problem as a mixed-integer linear program to maximize net present value over time considering capital costs, production costs, and budget constraints.
Presented in this short document is a description of what we call "Phasing" and "Planuling". Phasing is a variation of the sequence-dependent changeover problem (Kelly and Zyngier, 2007, Balas et. al., 2008) except that the sequencing, cycling or phasing is fixed as opposed to being variable or free. Planuling is a portmanteau of planning and scheduling where we "schedule" slow processes and we "plan" fast processes together inside the same time-horizon and can also be considered as "hybrid" planning and scheduling.
Presented in this short document is a description of modeling and solving partial differential equations (PDE’s) in both the temporal and spatial dimensions using IMPL. The sample PDE problem is taken from Cutlip and Shacham (1999 and 2014) and models the process of unsteady-state heat transfer or conduction in a one dimensional (1D) slab with one face insulated and constant thermal conductivity as discussed by Geankoplis (1993).
Advanced Production Accounting of an Olefins Plant Industrial Modeling Framew...Alkis Vazacopoulos
Presented in this short document is a description of what we call "Advanced" Production Accounting (APA) applied to a small Olefins Plant found in Sanchez and Romagnoli (1996). APA is the term given to the technique of vetting, screening or cleaning the past production data using statistical data reconciliation and regression (DRR) when continuous-processes are assumed to be at steady-state (Kelly and Hedengren, 2013) i.e., there is no significant material accumulation. For this case, the model and data define a simultaneous mass or volume linear DRR problem. Figure 1a shows the Olefins Plant using simple number indices for both the nodes and streams where Figure 1b depicts the same problem configured in our unit-operation-port-state superstructure (UOPSS) (Kelly, 2004, 2005; Zyngier and Kelly, 2012).
Generalized capital investment planning of oil-refineries using CPLEX-MILP an...Alkis Vazacopoulos
Performing capital investment planning (CIP) is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for industrial expenditure optimizations. We propose a more tractable approach using mixed-integer linear (MILP) model and input-output (Leontief) models whereby the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIP's known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with the scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly. Similar applications can be applied to process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning are highlighted.
Presented in this short document is a description of what is called the (classic) “Pooling Optimization Problem” and was first described in Haverly (1978) where he modeled a small distillate blending problem with three component materials (A, B, C), one pool for mixing or blending of only two components, two products (P1, P2) and one property (sulfur, S) as well as only one time-period. The GAMS file of this exact same problem is found in Appendix A which describes all of the sets, lists, parameters, variables and constraints required to represent this problem. Related types of NLP sub-models can also be found in Kelly and Zyngier (2015) where they formulate other sub-types of continuous-processes such as blenders, splitters, separators, reactors, fractionators and black-boxes for adhoc or custom sub-models.
Presented in this short document is a description of what we call "Phasing" and "Planuling". Phasing is a variation of the sequence-dependent changeover problem (Kelly and Zyngier, 2007, Balas et. al., 2008) except that the sequencing, cycling or phasing is fixed as opposed to being variable or free. Planuling is a portmanteau of planning and scheduling where we "schedule" slow processes and we "plan" fast processes together inside the same time-horizon and can also be considered as "hybrid" planning and scheduling.
Presented in this short document is a description of modeling and solving partial differential equations (PDE’s) in both the temporal and spatial dimensions using IMPL. The sample PDE problem is taken from Cutlip and Shacham (1999 and 2014) and models the process of unsteady-state heat transfer or conduction in a one dimensional (1D) slab with one face insulated and constant thermal conductivity as discussed by Geankoplis (1993).
Advanced Production Accounting of an Olefins Plant Industrial Modeling Framew...Alkis Vazacopoulos
Presented in this short document is a description of what we call "Advanced" Production Accounting (APA) applied to a small Olefins Plant found in Sanchez and Romagnoli (1996). APA is the term given to the technique of vetting, screening or cleaning the past production data using statistical data reconciliation and regression (DRR) when continuous-processes are assumed to be at steady-state (Kelly and Hedengren, 2013) i.e., there is no significant material accumulation. For this case, the model and data define a simultaneous mass or volume linear DRR problem. Figure 1a shows the Olefins Plant using simple number indices for both the nodes and streams where Figure 1b depicts the same problem configured in our unit-operation-port-state superstructure (UOPSS) (Kelly, 2004, 2005; Zyngier and Kelly, 2012).
Generalized capital investment planning of oil-refineries using CPLEX-MILP an...Alkis Vazacopoulos
Performing capital investment planning (CIP) is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for industrial expenditure optimizations. We propose a more tractable approach using mixed-integer linear (MILP) model and input-output (Leontief) models whereby the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIP's known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with the scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly. Similar applications can be applied to process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning are highlighted.
Presented in this short document is a description of what is called the (classic) “Pooling Optimization Problem” and was first described in Haverly (1978) where he modeled a small distillate blending problem with three component materials (A, B, C), one pool for mixing or blending of only two components, two products (P1, P2) and one property (sulfur, S) as well as only one time-period. The GAMS file of this exact same problem is found in Appendix A which describes all of the sets, lists, parameters, variables and constraints required to represent this problem. Related types of NLP sub-models can also be found in Kelly and Zyngier (2015) where they formulate other sub-types of continuous-processes such as blenders, splitters, separators, reactors, fractionators and black-boxes for adhoc or custom sub-models.
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...Alkis Vazacopoulos
Presented in this short document is a description of how to model and solve advanced parameter estimation (APE) problems in IMPL. APE is the term given to the application of estimating, fitting or calibrating parameters in models involving a network, topology, superstructure or flowsheet. When estimating parameters with multiple linear regression (MLR), ordinary least squares (OLS), ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) there is no explicit model but simply an X-block and Y-block of data. Hence, these methods are referred to as “non-parametric” or “data-based” methods as opposed to the “parametric” or “model-based” method used here. To solve these types of problems we use what is commonly referred to as “error-in-variables” (EIV) regression which is conveniently implemented as nonlinear data reconciliation and regression (NDRR) using the technology found in Kelly (1998a; 1998b; 1999) and Kelly and Zyngier (2008a). The primary benefit of using EIV (NDRR) over the other regression methods is that we can easily handle the inclusion of conservation laws and constitutive relations, explicitly, a must for any industrial estimation problem (IEP).
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate a deterministic and stochastic time-series transfer function models in IMPL using IBM’s CPLEX applied to industrial gas furnace data. The methodology of time-series analysis involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment, auto-, cross- and partial-correlation, etc. to establish the transfer function polynomial degrees. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s nonlinear programming algorithm SLPQPE which uses CPLEX 12.6 as the QP sub-solver.
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Alkis Vazacopoulos
Presented in this document is a description of how to develop and deploy industrial applications in a timely fashion using Excel/VBA as the user-interface (UI) and systems-integration (SI) system, IMPL as the industrial modeller and CPLEX as the commercial solver. A small jobshop scheduling example is overviewed to help describe to some extent, the details of this advanced decision-making application where this type of problem can be found in both the manufacturing and process industries.
The purpose of developing and deploying quickly is to acquire feedback from the end-users, to assess the difficulty and tractability of the problem, to ascertain the expected costs and benefits of the application and to address any other issues and requirements regarding the project as a whole as soon as possible. For some projects, proof-of-concepts, prototypes and/or pilots are also useful and these should also be performed ASAP as well using the same approach highlighted here. Ultimately, once a business problem solution has been achieved and full or partial benefits have been captured, then a more robust and sophisticated end-user experience and system architecture can be implemented in the operating system and computer programming environment of choice which will hopefully enhance and maintain the solution over its expected life-cycle.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
PHP modernization approach generating KDM models from PHP legacy codejournalBEEI
With the rise of new web technologies such as web 2.0, Jquery, Bootstrap. Modernizing legacy web systems to benefit from the advantages of the new technologies is more and more relevant. The migration of a system from an environment to another is a time and effort consuming process, it involves a complete rewrite of the application adapted to the target platform. To realize this migration in an automated and standardized way, many approaches have tried to define standardized engineering processes. Architecture Driven Modernization (ADM) defines an approach to standardize and automate the reengineering process. We defined an ADM approach to represent PHP web applications in the highest level of abstraction models. To do this, we have used software artifacts as a entry point . This paper describes the extraction process, which permits discovering and understanding of the legacy system. And generate models to represent the system in an abstract way.
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate deterministic and stochastic non-parametric finite impulse response (FIR) models in IMPL applied to industrial gas furnace data identical to that found in TSE-GFD-IMF using parametric transfer-functions. The methodology of time-series analysis or system identification involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment/adjustment, auto-, cross- and partial-correlation, etc. to establish the parametric transfer function polynomial degrees especially when we are using non-parametric FIR estimation. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s Sequential Equality-Constrained QP Engine (SECQPE) and Supplemental Observability, Redundancy and Variability Estimator (SORVE). Other types of non-parametric identification known as Subspace Identification (Qin, 2006) and can used to estimate state-space models.
CS266 Software Reverse Engineering (SRE)
Reengineering and Reuse of Legacy Software
Teodoro (Ted) Cipresso, teodoro.cipresso@sjsu.edu
Department of Computer Science
San José State University
Spring 2015
MODEL DRIVEN ARCHITECTURE, CONTROL SYSTEMS AND ECLIPSEAnže Vodovnik
This paper describes the use of model driven architecture and its application in control system development. It also presents a prototype solution based on the Eclipse framework implemented by the author.
Anže Vodovnik, Klemen Žagar, Cosylab, Ljubljana, Slovenija
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Using Model-Driven Engineering for Decision Support Systems Modelling, Implem...CSCJournals
Following the principle of everything is object, software development engineering has moved towards the principle of everything is model, through Model Driven Engineering (MDE). Its implementation is based on models and their successive transformations, which allow starting from the requirements specification to the code’s implementation. This engineering is used in the development of information systems, including Decision-Support Systems (DSS). Here we use MDE to propose an DSS development approach, using the Multidimensional Canonical Partitioning (MCP) design approach and a design pattern. We also use model’s transformation in order to obtain not only implementation codes, but also data warehouse feeds.
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...Alkis Vazacopoulos
Presented in this short document is a description of how to model and solve advanced parameter estimation (APE) problems in IMPL. APE is the term given to the application of estimating, fitting or calibrating parameters in models involving a network, topology, superstructure or flowsheet. When estimating parameters with multiple linear regression (MLR), ordinary least squares (OLS), ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) there is no explicit model but simply an X-block and Y-block of data. Hence, these methods are referred to as “non-parametric” or “data-based” methods as opposed to the “parametric” or “model-based” method used here. To solve these types of problems we use what is commonly referred to as “error-in-variables” (EIV) regression which is conveniently implemented as nonlinear data reconciliation and regression (NDRR) using the technology found in Kelly (1998a; 1998b; 1999) and Kelly and Zyngier (2008a). The primary benefit of using EIV (NDRR) over the other regression methods is that we can easily handle the inclusion of conservation laws and constitutive relations, explicitly, a must for any industrial estimation problem (IEP).
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate a deterministic and stochastic time-series transfer function models in IMPL using IBM’s CPLEX applied to industrial gas furnace data. The methodology of time-series analysis involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment, auto-, cross- and partial-correlation, etc. to establish the transfer function polynomial degrees. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s nonlinear programming algorithm SLPQPE which uses CPLEX 12.6 as the QP sub-solver.
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Alkis Vazacopoulos
Presented in this document is a description of how to develop and deploy industrial applications in a timely fashion using Excel/VBA as the user-interface (UI) and systems-integration (SI) system, IMPL as the industrial modeller and CPLEX as the commercial solver. A small jobshop scheduling example is overviewed to help describe to some extent, the details of this advanced decision-making application where this type of problem can be found in both the manufacturing and process industries.
The purpose of developing and deploying quickly is to acquire feedback from the end-users, to assess the difficulty and tractability of the problem, to ascertain the expected costs and benefits of the application and to address any other issues and requirements regarding the project as a whole as soon as possible. For some projects, proof-of-concepts, prototypes and/or pilots are also useful and these should also be performed ASAP as well using the same approach highlighted here. Ultimately, once a business problem solution has been achieved and full or partial benefits have been captured, then a more robust and sophisticated end-user experience and system architecture can be implemented in the operating system and computer programming environment of choice which will hopefully enhance and maintain the solution over its expected life-cycle.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
PHP modernization approach generating KDM models from PHP legacy codejournalBEEI
With the rise of new web technologies such as web 2.0, Jquery, Bootstrap. Modernizing legacy web systems to benefit from the advantages of the new technologies is more and more relevant. The migration of a system from an environment to another is a time and effort consuming process, it involves a complete rewrite of the application adapted to the target platform. To realize this migration in an automated and standardized way, many approaches have tried to define standardized engineering processes. Architecture Driven Modernization (ADM) defines an approach to standardize and automate the reengineering process. We defined an ADM approach to represent PHP web applications in the highest level of abstraction models. To do this, we have used software artifacts as a entry point . This paper describes the extraction process, which permits discovering and understanding of the legacy system. And generate models to represent the system in an abstract way.
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate deterministic and stochastic non-parametric finite impulse response (FIR) models in IMPL applied to industrial gas furnace data identical to that found in TSE-GFD-IMF using parametric transfer-functions. The methodology of time-series analysis or system identification involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment/adjustment, auto-, cross- and partial-correlation, etc. to establish the parametric transfer function polynomial degrees especially when we are using non-parametric FIR estimation. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s Sequential Equality-Constrained QP Engine (SECQPE) and Supplemental Observability, Redundancy and Variability Estimator (SORVE). Other types of non-parametric identification known as Subspace Identification (Qin, 2006) and can used to estimate state-space models.
CS266 Software Reverse Engineering (SRE)
Reengineering and Reuse of Legacy Software
Teodoro (Ted) Cipresso, teodoro.cipresso@sjsu.edu
Department of Computer Science
San José State University
Spring 2015
MODEL DRIVEN ARCHITECTURE, CONTROL SYSTEMS AND ECLIPSEAnže Vodovnik
This paper describes the use of model driven architecture and its application in control system development. It also presents a prototype solution based on the Eclipse framework implemented by the author.
Anže Vodovnik, Klemen Žagar, Cosylab, Ljubljana, Slovenija
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Using Model-Driven Engineering for Decision Support Systems Modelling, Implem...CSCJournals
Following the principle of everything is object, software development engineering has moved towards the principle of everything is model, through Model Driven Engineering (MDE). Its implementation is based on models and their successive transformations, which allow starting from the requirements specification to the code’s implementation. This engineering is used in the development of information systems, including Decision-Support Systems (DSS). Here we use MDE to propose an DSS development approach, using the Multidimensional Canonical Partitioning (MCP) design approach and a design pattern. We also use model’s transformation in order to obtain not only implementation codes, but also data warehouse feeds.
WHAT IS BIG DATA? AND HOW IT APPLIED IN MODERN MARKETINGAndzhey Arshavskiy
Что такое Большие Данные? Где лежит та грань, что отделяет большие данные от обычных? Является ли размер данных, скорость их поступления или разнообразие форматов критерием их дифференцирующим? Как применяются технологии Больших Данных в современном маркетинге?
Women Executives Correlates to Startup Successgordonkaren
This Dow Jones research study shows female executives positively
helps VC-backed companies. Particularly at VP and director levels, the participation from female executives makes a significant difference in pushing a company to its success.
Presented in this short document is a description of what we call "Advanced" Property Tracking or Tracing (APT). APT is the term given to the technique of predicting, simulating, calculating or estimating the properties (i.e., densities, compositions, conditions, qualities, etc.) in a network or superstructure with significant inventory using statistical data reconciliation and regression (DRR)
Generalized Capital Investment Planning w/ Sequence-Dependent Setups Industri...Alkis Vazacopoulos
Presented in this short document is a description of what we call the “Generalized” Capital Investment Planning (GCIP) problem where conventional capital investment planning (CIP), and specifically for the “retrofit” problem, is discussed in Sahinidis and Grossmann (1989) and Liu and Sahinidis (1996). CIP is the optimization problem where it is desired to expand the capacity and/or extend the capability (conversion) of either the “expansion” of an existing unit or the “installation” of a new unit (Jackson and Grossmann, 2002).
Figure 1 shows the three types of CIP problems as defined in Vazacopoulos et. al. (2014) and Menezes (2014) with its capital cost and time scales.
Presented in this short document is a description of what is called Advanced Process Monitoring (APM) as described by Hedengren (2013). APM is the term given to the technique of estimating unmeasured but observable variables or "states" using statistical data reconciliation and regression (DRR) in an off-line or real-time environment and is also referred to as Moving Horizon Estimation (MHE) (Robertson et. al., 1996). Essentially, the model and data define a simultaneous nonlinear and dynamic DRR problem where the model is either engineering-based (first-principles, fundamental, mechanistic, causal, rigorous) or empirical-based (correlation, statistical data-based, observational, regressed) or some combination of both (hybrid).
Presented in this short document is a description of what we call "Partitioning" and "Positioning". Partitioning is the notion of decomposing the problem into smaller sub-problems along its “hierarchical” (Kelly and Zyngier, 2008), “structural” (Kelly and Mann, 2004), “operational” (Kelly, 2006), “temporal” (Kelly, 2002) and now “phenomenological” (Kelly, 2003, Kelly and Mann, 2003, Kelly and Zyngier, 2014 and Menezes, 2014) dimensions. Positioning is the ability to configure the lower and upper hard bounds and target soft bounds for any time-period over the future time-horizon within the problem or sub-problem and is especially useful to fix variables (i.e., its lower and upper bounds are set equal) which will ultimately remove or exclude these variables from the solver’s model or matrix.
The IML file is our user readable import or input file to the IMPL modeling and solving platform. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. The IML file allows the user to configure the necessary data to model and solve large-scale and complex industrial optimization problems (IOP's) such as planning, scheduling, control and data reconciliation and regression in either off or on-line environments.
The data configurable in the IML file are broken-down into several categories or classes where these data categories are used as further sections in this basic reference manual. This reference manual is specific only to the quantity dimension of what we refer to as the Quantity-Logic-Quality Phenomena (QLQP). The QLQP provides a useful phenomenological break-down of the problem complexity where the quantity dimension details quantities such as flows, rates, holdups and yields where the quantities can be related to any stock or signal including time. The other two dimensions are not the focus of this documentation but for completeness of the description, logic data have setups, startups, switchovers-to-itself, shutdowns and switchover-to-others (sequence-dependent transitions) and quality data have densities, components, properties and conditions. In addition to the QLQP , we also have what we call the Unit-Operation-Port-State Superstructure (UOPSS). This provides the flowsheet or topology of the IOP in terms of the various shapes, constructs or objects necessary to configure it. The UOPSS is more than a single network given that it is comprised of two networks we call the "physical" network and the "procedural" network. The physical network involves the units and ports (equipment, structural) and the procedural network involves the operations and states (activities, functional). The combination or cross-product of the two derives the "projectional" superstructure and it is these superstructure constructs or UOPSS keys that we apply, attach or associate specific QLQP attributes where projections are also known as hypothetical, logical or virtual constructs. Ultimately, when we augment the superstructure with the time or temporal dimension as well as including multiple sites or echelons i.e., sub-superstructures, we essentially are configuring what is known as a "hyperstructure".
Graph-Based Algorithm for a User-Aware SaaS Approach: Computing Optimal Distr...IJERA Editor
As a tool to exploit economies of scale, Software as a Service cloud models promote Multi-Tenancy which is the notion of sharing instances among a large group of tenants. However, Multi-Tenancy only satisfies requirements that are common to all tenants as well as the fact that tenants themselves hesitate about sharing. In a try to solve this problem, the present paper propose a User-Aware approach for Software as a Service models using Rich-Variant Components. The main contribution of this approach is a framework summarized in a graphbased algorithm enabling deduction of an optimal distribution of instances on application's tenants. To illustrate and evaluate the framework, the approach is applied on a Software as a Service Application for private school management
GENERATIVE SCHEDULING OF EFFECTIVE MULTITASKING WORKLOADS FOR BIG-DATA ANALYT...IAEME Publication
Scheduling of dynamic and multitasking workloads for big-data analytics is a challenging issue, as it requires a significant amount of parameter sweeping and iterations. Therefore, real-time scheduling becomes essential to increase the throughput of many-task computing. The difficulty lies in obtaining a series of optimal yet responsive schedules. In dynamic scenarios, such as virtual clusters in cloud, scheduling must be processed fast enough to keep pace with the unpredictable fluctuations in the workloads to optimize the overall system performance. In this paper, ordinal optimization using rough models and fast simulation is introduced to obtain suboptimal solutions in a much shorter timeframe.
AN AI PLANNING APPROACH FOR GENERATING BIG DATA WORKFLOWSgerogepatton
The scale of big data causes the compositions of extract-transform-load (ETL) workflows to grow increasingly complex. With the turnaround time for delivering solutions becoming a greater emphasis, stakeholders cannot continue to afford to wait the hundreds of hours it takes for domain experts to manually compose a workflow solution. This paper describes a novel AI planning approach that facilitates rapid composition and maintenance of ETL workflows. The workflow engine is evaluated on real-world scenarios from an industrial partner and results gathered from a prototype are reported to demonstrate the validity of the approach.
The scale of big data causes the compositions of extract-transform-load (ETL) workflows to grow increasingly complex. With the turnaround time for delivering solutions becoming a greater emphasis,
stakeholders cannot continue to afford to wait the hundreds of hours it takes for domain experts to manually compose a workflow solution. This paper describes a novel AI planning approach that facilitates rapid composition and maintenance of ETL workflows. The workflow engine is evaluated on real-world
scenarios from an industrial partner and results gathered from a prototype are reported to demonstrate the validity of the approach.
DESIGN AND DEVELOPMENT OF BUSINESS RULES MANAGEMENT SYSTEM (BRMS) USING ATLAN...ijcsit
Nowadays, in the world of industry end-users of business rules inside huge or small companies claims that
it’s so hard to understand the rules either because they are hand written by a specific structural or
procedural languages used only inside their organizations or because they require a certain understanding
of the back-end process. As a result, a high need for a better management system that is easy to use, easy to
maintain during the evolution process has increased. In this paper, the emphasis is put on building a
business rule management system (BRMS) as a graphical editor for editing the models in a flexible agile
manner with the assistant of ATL and Sirius frameworks within Eclipse platform. Thus, the proposed
solution, on one hand, solves the problem of wasting resources dedicated for updating the rules and on the
other hand it guarantees a great visibility and reusability of the rules.
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...Alkis Vazacopoulos
Presented in this short document is a description of what is called “Advanced” Process Monitoring as described by Hedengren (2013) but related to Startups, Shutdowns and Switchovers-to-Others (APM-SUSDSO). APM is the term given to the technique of estimating or fitting unmeasured but observable variables or "states" using statistical data reconciliation and regression (DRR) in an off-line or real-time environment. It is also referred to as Moving Horizon Estimation (MHE) (Robertson et. al., 1996) in Advanced Process Control (APC) which goes beyond simply updating a bias to implement some form of measurement or parameter feedback (Kelly and Zyngier, 2008b). Essentially, the model and data define a simultaneous nonlinear and dynamic DRR problem where the model is either engineering-based (first-principles, fundamental, mechanistic, causal, rigorous) or empirical-based (correlation, statistical data-based, observational, regressed) or some combination of both (hybrid) (Pantelides and Renfro, 2012).
Presented in this short document is a description of what is called a “Pipeline Scheduling Optimization Problem” and was first described in Rejowski and Pinto (2003) where they modeled the first-in-first-out (FIFO) and multi-product nature of the segregated pipeline using both discretized space (multi-batches, packs or pipes) and time (multi-intervals, slots or periods). The same MILP model can also be found in Zyngier and Kelly (2009) along with other related production/process objects.
Dynamic Component Deployment and (Re) Configuration Using a Unified FrameworkMadjid KETFI
Dynamic Component Deployment and (Re) Configuration Using a Unified Framework
M. Ketfi and N. Belkhatir
Proceedings of the ISCA 18th International Conference on Computer Applications in Industry and Engineering, CAINE 2005, November 9-11, 2005, Honolulu, Hawaii, USA. ISBN 1-880843-57-9 (pages 85-90).
We tested ODH|CPLEX 4.24 on Miplib Open-v7 Models, a public collection of 286 models to which and optimal solution has not been proven. 257 of these are known to have a feasible solution.
ODH|CPLEX proved optimality on 6 models and found better solutions in 2 hours, to 40% of the models with 12 threads and 35% with 8 threads. ODH|CPLEX matched on 21% of the models.
EX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment. CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to: Optimize business decisions with high-performance optimization engines. Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios. Create real-world applications that can significantly improve business outcomes. Optimization Direct has partnered with and entered into a technology licensing and distribution agreement with IBM. By combining the founders' industry and software experience and IBM’s CPLEX Optimization Studio product with the arsenal of Optimization modeling and solving tools from IBM provides customers the most powerful capabilities in the industry.
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.
Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.
This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992).
The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.
Presented in this short document is a description of how to model and solve multi-utility scheduling optimization (MUSO) problems in IMPL. Multi-utility systems (co/tri-generation) are typically found in petroleum refineries and petrochemical plants (multi-commodity systems) especially when fuel-gas (i.e., off-gases of methane and ethane) is a co- or by-product of the production from which multi-pressure heating-, motive- and process-steam are generated on-site. Other utilities include hydrogen, electricity, water, cooling media, air, nitrogen, chemicals, etc. where a multi-utility system is shown in Figure 1 with an intermediate or integrated utility (both produced and consumed) such as fuel-gas, steam or electricity. Itemized benefit areas just for better management of an integrated steam network can be found in Pelham (2013) where his sample multi-pressure steam utility flowsheet is found in Figure 2.
Presented in this short document is a description of what is well-known as Advanced Process Control (APC) applied to a small linear three (3) manipulated variable (MV) by two (2) controlled variable (CV) problem. These problems are also known as Model Predictive Control (MPC) (Grimm et. al., 1989) and Moving Horizon Control (MHC). Figure 1 shows the 3 x 2 APC problem configured in our unit-operation-port-state superstructure (UOPSS) (Kelly, 2004, 2005; Zyngier and Kelly, 2012) as an Advanced Planning and Scheduling (APS) problem as opposed to a traditional APC problem.
Although there is a tremendous amount of stability, performance and robustness theory associated with APC which can be directly assumed to APS problems (Mastragostino et. al., 2014), our approach is to show that APC can equally be set into an APS framework except that APS has far less sensitivity technology due to its inherent discrete and nonlinear modeling complexities i.e., especially non-convexities. In order to eliminate the steady-state offset between the actual value and its target, it is well-known to apply bias-updating though other forms of “parameter-feedback” is possible. Typically, APS applications only employ “variable-feedback” i.e., opening or initial inventories, properties, etc. but this alone will not alleviate the steady-state offset as demonstrated by Kelly and Zyngier (2008).
Presented in this short document is a description of our three separate techniques to analyze the data by checking, clustering and componentizing it before it is used by other IMPL’s routines especially in on-line/real-time decision-making applications. We also have other data consistency or analysis techniques which have been described in other IMPL documents and these relate to the application of data reconciliation and regression with diagnostics but require an explicit model (model-based) whereas the techniques below do not i.e., they are data-based techniques.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Capital Investment Industrial Modeling Framework - IMPRESS
1. Capital Investment & Facilities Location, Industrial Modeling Framework
(CIFL-IMF)
J.D. Kelly1 & A. Vazacopoulos2
i n d u s t r IAL g o r i t h m s
January, 2013
Introduction to Capital Investment & Facilities Location, UOPSS and QLQP
Presented in this short document is a description of what is typically known as a capital
investment or facilities location problem involving fixed-charge and economies-of-scale details
(Williams, 1993 and Winston, 1994). It is also known as a long-range strategic capacity
expansion problem (Sahinidis et. al. 1989, Lui and Sahinidis, 1995, 1996 and Lui et. al., 1996).
Figure 1 below depicts two processing unit-operations (batch and continuous) producing
product stocks D and E from feed stocks A, B and C. Each feed and product has dedicated
storage where product E is only produced if capital is expended to install a new continuous-
process unit either consuming A or B but not both. The capacity of the batch-process unit can
also be expanded or extended where D is an intermediate product. Cash-flows are also shown
constrained by budgetary restrictions which represent both a fixed-charge amount and a
variable-charge amount scaled by the throughput of the units i.e., batch, charge or lot-sizes.
Figure 1. Capital Investment & Facilities Location Flowsheet Example.
1
jdkelly@industrialgorithms.ca
2
alkis@industrialgorithms.com
2. A full description of the objects found in Figure 1 (as well as other objects not shown) can be
found in Kelly (2004b) and Zyngier and Kelly (2009) and is based on our Unit-Operation-Port-
State Superstructure (UOPSS) and our Quantity-Logic-Quality Phenomena (QLQP) (Kelly,
2005). In UOPSS, the units represent physical equipment which can have one or more
procedural operations assigned, attached or associated with it. The cross-product of a unit with
an operation creates a projectional unit-operation which is sometimes referred to as a virtual,
logical or hypothetical object. We impose symmetry with the projectional port-state where the
port is physical and the state is procedural where the state characterizes the type of substance
passing through the port-state. Connectivity is modeled as paths between unit-operations and
port-states and represents the flow of something. The key idea of UOPSS is its ability to
explicitly manage the fact that a single unit can have multiple operations each with a different
configuration of port-states (e.g., BATCH and CONTINUOUS in Figure 1) and allows for very
complex flowsheets to be depicted graphically. An important notion that we exploit with respect
to the QLQP is our novel phenomenological decomposition3 of logistics and quality. Logistics is
the combination of quantity and logic where quantities are flows, holdups, yields and rates and
the logic aspects are related to the setup, startup, switchover, shutdown, status, etc. (Kelly and
Zyngier, 2007) of unit-operations and is solved using mixed-integer linear programming (MILP),
meta-heuristics (Genetic Algorithms, Simulated Annealing, etc.) and/or constraint programming
(CP).
The industrial shipping model presented above is MILP4 based but most process industry
production or manufacturing problems also contain a quantity times quality (sub-)problem due to
intensive variables such as densities, components, properties and conditions multiplied by
extensive quantities such as flows and holdups and is solved using nonlinear programming
(NLP). Furthermore, our modeling framework is based on a discrete-time time-indexed
formulation which requires each time-period to have the same time duration. Other time-
indexed formulations classed as continuous-time models are available and have several
variations based on whether the asynchronous time-periods are defined for a global/common
time grid or local/specific to each unit. However, for our industrial planning and scheduling
problems we have found discrete-time to be not only computationally effective (Maravelias,
2012) but also appropriate when dealing with the many nuances of the problem specification
especially handling partially specified plans or schedules in the future i.e., manually locking or
fixing certain future activities and solving around or between them. This is very important for
industrial decision-making problems where some level of transparency for the user, modeler or
analyst is required in terms of how the planning or scheduling solution is computed from
essentially black-box solvers5.
Industrial Modeling Framework (IMF), IMPRESS and SIIMPLE
To implement the mathematical model of this and other systems, Industrial Algorithms offers a
unique approach and is incorporated into our Industrial Modeling and Pre-Solving System we
call IMPRESS. IMPRESS has its own modeling language called IML (short for Industrial
Modeling Language) which is a flat or text-file interface as well as a set of API's which can be
called from any computer programming language such as C, C++, Fortran, Java, C# or Python
3
Other decompositions are well known such as hierarchical, structural, spatial and temporal but the concept
of phenomenological decomposition is new at least in name for advanced planning and scheduling problems.
4
Although MH and CP as well as local search (LS) solvers can be integrated, at present they are not.
5
Most industrial scheduling applications are still simulation-based where the schedules are built manually and
incrementally one decision at a time so feedback in terms of cause and effect is important to the user.
3. called IPL (short for Industrial Programming Language) to both build the model and to view the
solution. Models can be a mix of linear, mixed-integer and nonlinear variables and constraints
and are solved using a combination of LP, QP, MILP and NLP solvers such as COINMP, GLPK,
LPSOLVE, SCIP, CPLEX, GUROBI, LINDO, XPRESS, CONOPT, IPOPT and KNITRO as well
as our own implementation of SLP called SLPQPE (successive linear & quadratic programming
engine) which is a very competitive alternative to the other nonlinear solvers.
The underlying system architecture of IMPRESS is called SIIMPLE (we hope literally) which is
short for Server, Interacter (IPL), Interfacer (IML), Modeler, Presolver Libraries and Executable.
The Server, Presolver and Executable are primarily model or problem-independent whereas the
Interacter, Interfacer and Modeler are typically domain-specific i.e., model or problem-
dependent. Fortunately, for most industrial planning, scheduling, optimization and control
problems found in the process industries, IMPRESS's standard Interacter, Interfacer and
Modeler are well-suited and comprehensive to model the most difficult of production and
process complexities allowing for the formulations of ubiquitous conservation laws, rigorous
constitutive relations, empirical correlative expressions and other necessary side constraints.
User or adhoc constraints can be augmented or appended to IMPRESS when necessary in
several ways. For MILP or logistics problems we offer user-defined constraints configurable
from the IML file or the IPL code where the variables and constraints are referenced using unit-
operation-port-state names and the quantity-logic variable types. It is also possible to import a
foreign LP file (row-based MPS file) which can be generated by any algebraic modeling
language or matrix generator. This file is read just prior to generating the matrix and before
exporting to the LP, QP or MILP solver. For NLP or quality problems we offer user-defined
formula configuration in the IML file and single-value and multi-value function blocks writable in
C, C++ or Fortran. The nonlinear formulas may include intrinsic functions such as EXP, LN,
LOG, SIN, COS, TAN, MIN, MAX, IF, LE, GE and KIP, LIP, SIP (constant, linear and monotonic
spline interpolation) as well as user-written extrinsic functions.
Industrial modeling frameworks or IMF's are intended to provide a jump-start to an industrial
project implementation i.e., a pre-project if you will, whereby pre-configured IML files and/or IPL
code are available specific to your problem at hand. The IML files and/or IPL code can be
easily enhanced, extended, customized, modified, etc. to meet the diverse needs of your project
and as it evolves over time and use. IMF's also provide graphical user interface prototypes for
drawing the flowsheet as in Figure 1 and typical Gantt charts and trend plots to view the solution
of quantity, logic and quality time-profiles. Current developments use Python 2.3 and 2.7
integrated with open-source Dia and Matplotlib modules respectively but other prototypes
embedded within Microsoft Excel/VBA for example can be created in a straightforward manner.
However, the primary purpose of the IMF's is to provide a timely, cost-effective, manageable
and maintainable deployment of IMPRESS to formulate and optimize complex industrial
manufacturing systems in either off-line or on-line environments. Using IMPRESS alone would
be somewhat similar (but not as bad) to learning the syntax and semantics of an AML as well as
having to code all of the necessary mathematical representations of the problem including the
details of digitizing your data into time-points and periods, demarcating past, present and future
time-horizons, defining sets, index-sets, compound-sets to traverse the network or topology,
calculating independent and dependent parameters to be used as coefficients and bounds and
finally creating all of the necessary variables and constraints to model the complex details of
logistics and quality industrial optimization problems. Instead, IMF's and IMPRESS provide, in
our opinion, a more elegant and structured approach to industrial modeling and solving so that
you can capture the benefits of advanced decision-making faster, better and cheaper.
4. CIFL-IMF Modeling Details
At this point it is prudent to elucidate more of the modeling details found in Figure 1. The
production facility or plant is represented by a supply of raw materials A, B and C which can be
used to produce finished product D in an existing batch-process unit-operation labeled BATCH,
EXISTING. Batch-processes exhibit a distinct "fill-hold-draw" holdup or inventory profile over
time (Zyngier and Kelly, 2009) where the feeds can be filled or loaded into the batch vessel
either continuously or intermittently over the duration of the batch known as its cycle or
processing-time. Finished product E is produced in a (future) continuous-process unit named
CONTINUOUS requiring D and A for operation INSTALLATION1 and D and B for
INSTALLATION2. Continuous-processes exhibit no or negligible holdup during the processing
and as such simultaneously produce E the instant D and A or B is available where the fill-hold-
draw profile collapses to a concurrent fill-draw with no hold of course. The non-material or non-
stock flow called CAPITAL is a pecuniary or currency resource and is located on what we call a
port-state.
Port-states allow flow into and out of a unit-operation and can be considered as flow-interfaces
similar to ports on a computer i.e., nozzles, spouts, spigots. Port-states also provide an
unambiguous description of the flowsheet or superstructure in terms of specifically what type of
materials or resources are being consumed and produced by the unit-operation. Port-states
can also represent utilities (steam, power), utensils (operators, tools) as well as signals such as
data, time, tasks, etc. Each of the two products D and E have tanks available for storage and is
a requirement when balancing the production-side supply with the transportation-side demand
of the value-chain. Finally, the lines or arcs between the unit-operations and port-states and
across an upstream unit-operation-port-state to a downstream unit-operation-port-state
correspond to flows as one would except given that the superstructure is ultimately composed of
a network or graph of nodes/vertices and arcs/edges (directed).
We now feature the capital investment or facilities location details of the problem where the
objective function of the problem is to maximize the net-present-value6 (NPV) of the profit
(revenue minus feed costs and capital costs) over time. The time-horizon is usually modelled
over several years where the time-periods are in years or sub-years such as quarters. These
time-periods can also be non-uniform in the sense that their durations can be variable but
exogenously defined (known a priori). These types of problems may have different aggregated
or disaggregated formulations such as lot-sizing reformulations (Liu and Sahinidis, 1996) which
are not applied explicitly in this model7.
The unit BATCH is existing but its capacity can also be expanded at a cost. This cost is well
known to have essentially two parts, a fixed and a variable cost where the fixed cost is applied
to the binary or logic variable determining the existence of the expansion (i.e., its setup or
startup logic) and the variable cost is applied to its throughput or batch-size. When known
power-law relationships of capital cost versus throughput are available such as found in Gary
and Handwerk (1994), Johnson (1996) and Kaiser and Gary (2007), simple linear regression
can be applied to convert these to approximated fixed+variable coefficients (Liu et. al. 1996 and
Kelly 2004a) that can be easily used inside MILP formulations such as that presented here.
6
NPV simply discounts the objective function coefficients according to an inflation or deflation rate parameter
which can also be time-varying or time-dependent.
7
These types of reformulations can be generated automatically for each unit or unit-operation that is
identified as having a lot-sizing structure for example.
5. Each of the expansion and installation unit-operations shown i.e., EXPANSION1, etc. have a
CAPITAL port-state which carries the NPV cash-flow to the unit-operation named BUDGET
(diamond shaped). The inlet port-state of this unit-operation will have a time-varying NPV cash-
flow lower and upper bound to constrain the expansions and/or installations according to the
expected cash-flow profiles in the future. An additional restriction required, sometimes referred
to as a side-constraint, is the fact that if an expansion/installation unit-operation is selected in
some future time-period then it must be setup for the rest of the time-horizon. This can be
modeled using our up-time8 logic constraint where a lower or minimum up-time is configured as
the time-horizon length of the problem (Wolsey, 1998, Kelly and Zyngier, 2007 and Zyngier and
Kelly, 2009).
The unit CONTINUOUS is non-existing and requires a completely grass-roots or green-field
installation or construction. If selected, based on its economic viability with respect to its
expected installed cost and projected revenue of E, it will use D and either A or B as its co-feeds
and will produce final product E which also requires its own newly installed storage vessel. This
unit is also strictly conditional on either of the BATCH unit's expansion operations being
selected. That is, the CONTINUOUS unit will not be installed unless either EXPANSION1 or
EXPANSION2 operation is chosen. This is known as an implication or sequence-dependency
type of constraint and is managed by the fact that if there is no flow of D from the BATCH unit to
the CONTINUOUS unit then the CONTINUOUS unit cannot be installed or constructed given
that an infeasibility will occur during the branch-and-bound search of the MILP solution.
CIFL-IMF Solving Details
Once the flowsheet has been configured as in Figure 1, a *.UPS file (short for UOPSS) is
constructed using the UOPSS object names via a Python 2.3 macro (IALconstructer.py)
embedded in the Dia drawing package and is shown in Appendix A. This file can then be
included into the IML file or the IPL code and will define the necessary named keys or index-
sets for the various capacity data necessary to create the mathematical model. A useful facet of
the UPS file is the application of "aliases". Aliases allow the capacity configuration of many
UOPSS objects simultaneously - see ALLPARTS, ALLINPORTS, ALLOUTPORTS and
ALLPATHS.
TBD
References
Sahinidis, N.V., Grossmann, I.E., Fornari, R.E., Chathrathi, M., "Optimization model for long
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Winston, W.L., "Operations research: applications and algorithms", Wadsworth Publishing
Company for Duxbury Press, Belmont, California, (1994).
8
Up-time is also known as a run or campaign-length and essentially restricts a shutdown of the unit-operation
for a specified number of time-periods in the future.
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Challenges in the Enterprise", Springer, 61-95, (2009).
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Appendix A - CIFL-IMF.UPS (UOPSS) File
i n d u s t r I A L g o r i t h m s
All Rights Reserved (c)
checksum,184
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Unit-Operation-Port-State-Superstructure (UOPSS) *.UPS File.
! (This file is automatically generated from the Python program IAConstructer.py)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@sType,@sSubtype,@sUse
A,,perimeter,,
A,,pool,,
B,,perimeter,,
B,,pool,,
BATCH,EXISTING,processb,,
BATCH,EXPANSION1,processb,,
BATCH,EXPANSION2,processb,,
BUDGET,EXPENDITURE,perimeter,,