Computational Intelligence in Engineering and Project Management (Studies in Computational Intelligence, 1134)

✍ Scribed by Pedro Yobanis Piñero Pérez (editor), Janusz Kacprzyk (editor), Rafael Bello Pérez (editor), Iliana Pérez Pupo (editor)

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 365
• Edition: 1st ed. 2024
• Category: Library

✦ Synopsis

This book is dedicated to all those interested in the application of artificial intelligence in engineering and project management. Most of the jobs are focused on achieving agile project development. New algorithms that combine various computational intelligence techniques are applied in different areas of knowledge in project management.

In this book, computational intelligence is presented as the branch of AI that encompasses various techniques aimed at simulating human tolerance in decision-making processes in environments with uncertainty and imprecision. Among the precursor techniques of computational intelligence are: evolutionary algorithms, artificial neural networks, fuzzy set theory, and fuzzy systems. However, other areas such as the rough set, linguistic data summary, natural language processing, the conversational systems, fuzzy cognitive maps, collective intelligence, the neutrosophic theory, and other fuzzy logic extensions are contributing to the application and extension of computational intelligence

The book is organized into three parts, as shown below. The first part constitutes a critical review of computational intelligence in project management. The second part presents new computational intelligence techniques and their applications in project planning, control, and monitoring processes. In particular, the use of conversational systems and their applicability in the agile management of portfolio programs and projects stand out. Part three of the book exemplifies the use of computing techniques with words and other computational intelligence techniques for organizational decision-making.

The authors of the book stand out for their extensive experience in the development of basic and applied applications of computational intelligence. The authors Janusz Kacprzyk, Pedro Y. Piñero Pérez, Rafael E. Bello Pérez, and Iliana Pérez Pupo have published several books associated with artificial intelligence and computational intelligence applied to projects. They continue working on fundamental-oriented and applied research on different artificial intelligence techniques to help with decision-making in different areas of knowledge.

Authors would like to thank all the engineers, professors, and researchers without whose efforts this book could not have been written.

✦ Table of Contents

Preface
Acknowledgments
Contents
Critical Review of Computational Intelligence in Project Management
Conversational Systems and Computational Intelligence, A Critical Analysis
1 Introduction
2 Methodology Used for Trend Analysis
2.1 Review Protocol Used in the Search
2.2 Resultados Preliminares Del Análisis De Las Tendencias
2.3 Results of the Analysis of the Applications of Conversational Systems
3 Results of the Analysis of the Architectural Models of Conversational Systems
3.1 Basic Smart Conversational Characterization
3.2 Representation of Knowledge in Conversational Systems
3.3 Active Learning Supported by Human Agents
3.4 Large Languaje Model (LLM) Chatbots Characterization
4 Integration Analysis of Conversational Systems with Specific Computational Intelligence Techniques
4.1 Neutrosophic Theory and Other Extensions in Conversational System Evaluations
4.2 Linguistic Summarization of Data in the Learning of Conversational Systems
4.3 Reinforcement Learning Combined with Conversational Systems
5 Conclusions
References
Fuzzy Cognitive Maps, Extensions and Applicability as an Explanatory Artificial Intelligence Model
1 Introduction
2 Review Protocol Used in the Exploratory Study
3 Characterization and Evolution of Fuzzy Cognitive Maps
4 Analysis of Extensions of Simple Fuzzy Cognitive Maps
4.1 Linguistic Fuzzy Cognitive Maps
4.2 Competitive Fuzzy Cognitive Maps (CFCMs)
4.3 Triangular Fuzzy Cognitive Maps (TrFCMs)
4.4 Case-Based Fuzzy Cognitive Maps (CBFCMs)
4.5 Fuzzy Gray Cognitive Maps (FGCMs)
4.6 Evidence-Based Cognitive Maps (ECMs)
4.7 Fuzzy Cognitive Maps Based on Distributed Degrees of Belief (BDD-FCMs)
4.8 Approximate Cognitive Networks (RCNs)
4.9 Rule-Based Fuzzy Cognitive Maps (RBFCMs)
5 Analysis of Extensions of Multiple Fuzzy Cognitive Maps
5.1 Hierarchical Fuzzy Cognitive Maps (JFCMs)
5.2 Distributed Fuzzy Cognitive Maps (DFCMs)
5.3 Multilayer Fuzzy Cognitive Maps (MFCMs)
5.4 Parallel Fuzzy Cognitive Maps (PFCMs)
5.5 Analysis of Validation Methods Used in the Research Consulted
6 Conclusions
References
Project Scheduling a Critical Review of Both Traditional and Metaheuristic Techniques
1 Introduction
2 Systematic Review Protocol
3 Treatment of Planning Problems by Project Management Schools
3.1 Project Planning as Seen from the PMBOK Guide
3.2 Approach from the International Organization for Standardization (ISO)
3.3 CMMI Approach
3.4 Analysis Regarding the Tools that Support the Standards
4 Characterization of Planning and Modeling Problems as an Optimization Problem
4.1 Characterization and Solution Trends of the RCPSP Problem
4.2 Characterization and Solution Trends of the RCMPSP Problem
4.3 Characterization and Solution Trends of the MMRCPSP Problem
4.4 Characterization and Solution Trends of the MMRCMPSP Problem
5 Algorithms Reported in the Bibliography in the Solution of the MMRCPSP Problem
5.1 Characterization of EDA Algorithms in Solving Planning Problems Considering the Correlation of Variables
6 Conclusions
References
Systematic Review of Augmented Reality (AR) and Bim for the Management of Deadlines, Costs and Quality
1 Introduction
2 Literature Review
2.1 Software AR/BIM Review
3 State of the Art and Development of the Conceptual Model
3.1 Bibliometric Analysis—AR-BIM Software
4 Validation of the Theoretical Model
4.1 Questionnaire Preparation
4.2 Expert Panel Selection
4.3 First Round of Consultations
4.4 Results of the First Round of Consultations
4.5 Second Round of Consultations
4.6 Results Second Round of Consultations
5 Result Analysis
6 Conclusions
References
Assessing Adoption Archetypes of Advanced Technologies in Industrial Clusters
1 Introduction
2 Methodology of Multiple—Case Studies for Industrial Clusters
3 Research Framework
3.1 Design Principles
3.2 The Framework
4 Questionnaire (ICMAT): Main Characteristics
5 Analysis of Results
5.1 Comparative Analysis
5.2 Limitations and Future Work
6 Conclusions
References
Computational Intelligence in Project Planning and Monitoring
Combining EDA and Simulated Annealing Strategies in Project Scheduling Construction
1 Introduction
2 Characterization and Modeling of the Problem, MMRCMPSP
2.1 Modeling of the Optimization Problem Associated with the Problem, MMRCPSP
2.2 Computational Design of the Individual to Solve the Optimization Problem
3 Distribution Estimation Algorithms for Solving the Planning Problem
3.1 Algorithm for Improving Individuals Based on Local Search Strategies
3.2 FDA_BRA6 Algorithm for Solving the MMRCPSP Problem
3.3 UMDA_BRA8 Algorithm for Solving the MMRCPSP Problem
3.4 Scope and Limitations of the Proposed Algorithms
4 Results Analysis
4.1 Parameters of the Algorithms Used in the Experimentation
4.2 Test 1. With Databases with 16 Tasks, Three Modes, Two Renewable Resources and Two Non-renewable Resources
4.3 Test 2. Analysis of the Influence of Variations in Non-renewable Resources (Databases: n0, n3_12 and n3_32)
4.4 Test 3. Analysis of the Influence of Variations in the Number of Renewable Resources (Databases: R4_12, R4_32, R5_12 and R5_32)
4.5 Test 4. Validation of the Dependent Variable in the Dimension “Effectiveness of the Algorithms in the Face of Variations in the Number of Modes
4.6 Test 5. Validation of the Dependent Variable in the Dimension “Effectiveness of the Algorithms in the Face of Variations in the Number of Tasks”
4.7 Test 6. Validation of the Dependent Variable in the Dimension “Global Performance of the Algorithms”
5 Conclusions
References
Platform as Service for Data Analysis Suppoted by Computational Intelligence Techniques
1 Introduction
2 Platform Architecture Proposal as Services for Data Analysis
2.1 Systems View of the Architecture
2.2 Architecture Integration View
2.3 Architecture Data View
2.4 Architecture Security View
2.5 View of Architecture Technologies
2.6 Architecture Deployment View
2.7 View of Processes for Managing the Algorithm Repository
3 Analysis of Results
3.1 Analysis Project Layer
3.2 Algorithm Repository Layer
4 Conclusions
References
Ecosystem for Construction of Hybrid Conversational Systems (BRasa)
1 Introduction
2 New BRasa Architectural Model Supported by Different Soft Computing Techniques
2.1 BRasa Knowledge Subsystem
2.2 Augmenting LDS Generation (BRasa_LDS) Subsystem
2.3 BRasa_Prescriptive
2.4 Conversational Drive Development (CDD) Subsystem
2.5 Trainer Subsystem
2.6 User Response Model Subsystem
2.7 BRIntelligent Data Analysis and Services Subsystem
2.8 BRasa Information Retrieval Subsystem
2.9 Conversations and Stories Management Subsystem
3 Analysis of Results
3.1 Definition of the Variables Used in Validation
3.2 Results of the Evaluation of the Independent Variable “Efficiency”
3.3 Results of the Evaluation of the Dependent Variable “Efficacy”
4 Conclusions
References
Design of a Technological System with Artificial Intelligence to Manage Projects Through the Use of Knowledge Management and Lessons Learned
1 Introduction
2 Literature Review
2.1 Artificial Intelligence (AI)
2.2 Knowledge Management
2.3 Learned Lessons
3 Basis for the Design Development Proposal
3.1 Methodological Guidelines for the Design Proposal
3.2 Integrated Project Management System (Sisgip)
4 Expected Design Results
5 Analysis Results
5.1 Economic Impact
5.2 Social Impact
6 Design Challenges
7 Conclusions
References
Artificial Intelligence Contribution to the Development of Cuban Port Logistics Chains
1 Introduction
2 Simulation Procedure
2.1 STEP 1: Definition of the Scenarios for the Port Logistics Chain
2.2 STEP 2: Planning the Simulation Model
2.3 STEP 3: Data Collection and Processing
2.4 STEP 4: Model Construction
2.5 STEP 5: Model Verification and Validation
2.6 STEP 6: Experimentation
2.7 STEP 7: Presentation and Analysis of the Results
3 Simulation of the Rice Chain of the Cienfuegos Port
3.1 Definition of the Activity List
3.2 Approach to the Interrelationship Between the Modules
4 Discussion
5 Conclusions
References
Decision-Making in Project-Oriented Organizations Supported by Computational Intelligence’s Techniques
Digital Transformation in Project Oriented Organizations, Supported by Intelligence Ecosystems
1 Introduction
2 Model for Digital Transformation and Improvement of Project-Oriented Organizations
2.1 Focused on Clients in Close Connection with Strategic Projection
2.2 Agile Management of Lessons Learned from Portfolios, Programs and Projects
2.3 Agile Planning, Control and Monitoring Based on Objective Indicators
2.4 Decision-Making is Supported by Computational Intelligence Techniques
2.5 Development Ecosystem that Supports the Entire Model
3 Results Analysis
3.1 Results of the to-be Model Diagnosis in Entity B
3.2 Results of the Introduction of the Proposed Model in Entity B
4 Conclusions
References
Sustainability Management Framework: Case Study of a Cuban IT Project Organization
1 Introduction
2 Brief Conceptual Framework
3 Framework for Sustainability Management
3.1 Phases for the Implementation of the Framework
4 Results and Discussion
5 Conclusions
References
A Formal Representation of Standards for Project Management: Case PMBOK
1 Introduction
2 Background
2.1 Ontologies
3 An Ontological Model to Manage the Knowledge of Project Management Standards
3.1 Ontology Evaluation
4 Representing the Knowledge of PMBOK
5 Conclusions
References
Ranking of Success Forecasts for Computer Engineering Students Based on Computing with Words
1 Introduction
2 Materials and Methods
3 Results
3.1 Solution of the Problem by Means of FLINSTONES
3.2 Results After the Analyzed Period
4 Conclusions
References
Computing with Words to Assess the Perceived Quality of IT Products and Projects
1 Introduction
2 Methodology
2.1 Phase 1 “Definition of Evaluation Criteria”
2.2 Phase 2 “Collection of User Preferences”
2.3 Phase 3 “Evaluation of Perceived Quality”
2.4 Phase 4 “Interpretation of Results”
3 Analysis Results
3.1 Qualitative Comparison with Other Models of Perceived Quality Assessment of Services
4 Conclusions
References