Edrisi Muñoz

Ontological framework for enterprise-wide integrated decision-making at operational level

In the domain of chemical process engineering, there is an increased interest in the integration of the enterprise hierarchical levels for decision-making purposes. At the scheduling level, decisions on the allocation of tasks to resources, sequencing and timing of tasks must be managed. However, such decisions are directly related to other enterprise actions, such as control and planning, but they are difficult to coordinate because they are modeled at different time and space scales, and their goals are not the same. In order to achieve integrated decisions supported by high quality information, there is a need to improve and develop robust computational tools and consistent models. In general, scheduling optimization approaches for decision-making differ depending on problem features, such as physical layout or time representation. Therefore, this work focuses on providing a framework based on a semantic model that captures the diversity in scheduling problem representation. Such semantic model uses the master recipe concept from the ANSI/ISA-88 standard perspective and encapsulates the scheduling decision task features. As a result, by the use of a single representation approach, any scheduling problem can be modeled and solved by its adequate optimization tool. The potential of a general model representation is presented by means of several case studies related to the scheduling function. Such case studies shed light to the model capabilities to represent different kinds and particular scheduling problems, achieving integration at the different decision support levels.

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.

Using mathematical knowledge management to support integrated decision-making in the enterprise

Abstract The basis of decision-making in the enterprise consists in formally representing the system and its subsystems in models which adequately capture those features which are necessary to reach consistent decisions. This work represents the elements of the enterprise which are included in mathematical models (i.e. decisions, parameters, constraints, performance indicators) in an ontology which captures the knowledge of the mathematical domain. Thus, this ontology relates the mathematical elements of the models to their corresponding semantic representation within the enterprise ontology. As a result, the mathematical symbolic abstractions of a given enterprise element in different models are directly linked to their actual unique meaning, and the integration of decisions in the enterprise is transparent and improved. The purpose of this work is illustrated in a case study related to capacity planning in the supply chain and scheduling problems.

Reinforcing the applicability of multi-model environments for software process improvement using knowledge management

Nowadays software development organizations look for tools and methods that help them maintain their competitiveness. A key approach for organizations in order to achieve this competitiveness is a successful implementation of software process improvement (SPI). Unfortunately, most of the times, software process improvement involves a path full of obstacles due to the lack of knowledge for choosing the right implementation. The most common and critical problem consists of the selection and application of the right reference model for guiding this implementation. As an effort for helping organizations in the selection of the right implementation of improvements, multi-model environments arised enabling the use of best practices from different reference models. Multi-model approach facilitates the improvement task in order to achieve the organizations business goals. In this context, effective integration of models and standards can play a crucial role in the implementation of multi-model environments as reference support tool. Nevertheless the use of multi-model approaches presents difficulties related to the lack of knowledge of how to manage the amount of information and the correct integration of different models and standards. In this sense, knowledge management technologies have proven to be highly promising support for knowledge sharing and system integration. This work presents an ontological framework based on a multi-model approach, which facilitates and supports the SPI for small and medium companies for a life cycle process improvement. Life cycle process improvement pursues the necessary actions in a deliberate, structured and methodical manner, required in each stage of the life cycle of software development, capable for improving process to current organization needs. Finally, a case study is presented in order to show the performance of the framework. Presents a general ontological framework for managing multi-model in SPI.Focuses in the correct implementation of SPI using multi-model environments.Is capable of representing any software process.Captures and homogenizes the structure of processes found in software organizations.Allows establishing a common understanding of the process improvement technologies.

Knowledge Management to Support the Integration of Scheduling and Supply Chain Planning using Lagrangean Decomposition

The complexity of integrated planning and scheduling models can be tackled with decomposition techniques based on duality and information flows between a master and a set of subproblems. Hence, the information sharing and communication of information from the industrial environments requires flexible structures, facilitating the use of analytic tools and providing higher flexibility for model building in industrial environments. In this work, an ontological framework is proposed to allow the virtualization of systems and processes and to implement a novel Lagrangean decomposition scheme based on hierarchical level decomposition. Indeed, the scheduling and planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.

Mathematical Knowledge Management for Enterprise Decision Making

Abstract The basis of decision-making in the enterprise consists of formally representing the system and its subsystems in a model which adequately captures those features which are necessary to reach consistent decisions. This works aims at integrating the elements of the enterprise (i.e., decisions, parameters, constraints, performance indicators) which are included in mathematical models to a semantic representation of the enterprise by the creation of an ontology which captures the meaning of the mathematical language. As a result, the integration of decisions in enterprise may be achieved. The purpose of this work is illustrated in a case study related to plant capacity in the supply chain and scheduling problems.

Integration of Methods for Optimization in a Knowledge Management Framework

Abstract The solution of process systems engineering problems involves their formal representation and application of algorithms and strategies related to several scientific disciplines, such as computer science or operations research. In this work, the domain of operations research is modelled within a semantic representation in order to systematize the application of the available methods and tools to the decision-making processes within organizations. As a result, an operation research ontology is created. Such ontology is embedded in a wider framework that contains two additional ontologies, namely, the enterprise ontology project and a mathematical representation, and additionally it communicates with optimization algorithms. The new ontology provides a means for automating the creation of mathematical models based on operations research principles.

Intelligent Mathematical Modelling Agent for Supporting Decision-Making at Industry 4.0

The basis of decision-making at industry consists of formally representing the system and its subsystems in a model, which adequately captures those features that are necessary to reach consistent decisions. New trends in semantics and knowledge models aim to formalize the mathematical domain and mathematical models in order to provide bases for machine reasoning and artificial intelligence. Hence, tools for improving information sharing and communication have proved to be highly promising to support the integration of performance assessment within industrial decision-making. This work presents an intelligent agent based on knowledge models and establishes the basis for automating the design, management, programming and solution of mathematical models used in the industry. A case study concerning a capacity limitation constraint demonstrates the performance of the agent and indicates the directions for future work.

Recipe Management based on ISA-88 using Semantic Technologies

Abstract The effective and timely use of enterprise wide optimization models requires robust and reliable data acquisition systems to extract model relevant parameters and to drive the proposed enterprise wide coordination strategies. This work aims to create a knowledge-based platform for systematic and standardized management of the general, site and master recipes within the process industry. The platform allows for the creation of a master recipe ready for the production planning and scheduling, as well as for the process management. As a result, the recipe management functionalities are supported by a traceable and reliable system.

Decision-Support Platform for Industrial Recipe Management

The effective and timely use of enterprise wide optimization models requires robust and reliable data acquisition systems to extract model relevant parameters and to drive the proposed enterprise wide coordination strategies. This work aims to create a knowledge-based platform for systematic and standardized management of the general, site and master recipes within the process industry. The platform allows the creation of a master recipe ready for the production planning and scheduling, as well as for the process management. As a result, recipe management functionalities are supported by a traceable and reliable system.