Management and Industrial Engineering

Series editor

J. Paulo Davim, Aveiro, Portugal

More information about this series at http://www.springer.com/series/11690

Jorge Luis García-Alcaraz • Giner Alor-HernándezAidé Aracely Maldonado-MacíasCuauhtémoc Sánchez-RamírezEditors

New Perspectives on AppliedIndustrial Toolsand Techniques

123

EditorsJorge Luis García-AlcarazUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMexico

Giner Alor-HernándezDivision of Postgraduate and ResearchStudies

Instituto Tecnológico de OrizabaOrizabaMexico

Aidé Aracely Maldonado-MacíasUniversidad Autónoma de Ciudad JuárezCiudad Juárez, ChihuahuaMexico

Cuauhtémoc Sánchez-RamírezDivision of Postgraduate and ResearchStudies

Instituto Tecnológico de OrizabaOrizabaMexico

ISSN 2365-0532 ISSN 2365-0540 (electronic)Management and Industrial EngineeringISBN 978-3-319-56870-6 ISBN 978-3-319-56871-3 (eBook)DOI 10.1007/978-3-319-56871-3

Library of Congress Control Number: 2017940362

© Springer International Publishing AG 2018This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar ordissimilar methodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exemptfrom the relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

Printed on acid-free paper

This Springer imprint is published by Springer NatureThe registered company is Springer International Publishing AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Currently, there is a lack of interoperability, optimization, and integration amongtools that is preventing a proper knowledge reuse in the life cycle of critical systemsin industry. This situation implies that factories are facing major challenges that gobeyond the own complexity of these systems. Although interoperability, opti-mization, and integration issues have been widely studied, the truth is that nosolution has been fully and successfully applied to this critical sector in whichsafety is a common and likely the most relevant factor. Furthermore, other keypoints also include (1) keep backward compatibility with existing sound systems inindustry, (2) improve current practices to cover the whole life cycle of the engi-neering of complex systems for industry, (3) tackle the bricks that existing tools andtechniques contain in order to prepare a new suite of smart and advances practicesthat can be adequately applied to these complex systems, and (4) address thechallenge of evolving systems that require the cooperation of large sets ofstakeholders.

The goal of this book will disseminate current trends among innovative andhigh-quality research regarding the implementation of conceptual frameworks,strategies, techniques, methodologies, informatics platforms, and models fordeveloping advanced industrial tools and techniques and their application inindustry. The specific objectives can be summarized as follows:

• Create a collection of theoretical, real-world, and original research works in thefield of applied industrial tools and techniques

• Go beyond the state of the art in the field of industrial and software engineering.• Publish successful applications and use cases of studies of new approaches,

applications, methods, techniques for developing advanced industrial tools,methodologies and techniques and their application in different fields

• Provide an appropriate dissemination venue from both academia and industrialcommunities. The proposed book aims then at helping in communicating anddisseminating relevant recent research on industrial and software engineering.

Moreover, the topics in this book are the interest to academics, researchers,students, stakeholders, and consultants.

v

This book contains one kind of contribution: regular research papers. Theseworks have been edited according to the norms and guidelines of Springer VerlagEditorial. Several calls for chapters were distributed among the main mailing listsof the field for researchers to submit their works to this issue. In the first deadline,we received a total of 40 expressions of interest in the form of abstracts. Due to thelarge amount of submissions, abstracts were subject to a screening process to ensuretheir clarity, authenticity, and relevancy to this book. After the screening process,30 proposals were invited to submit full versions. At least two reviewers wereassigned to every work to proceed with the peer-reviewed process. Twenty-fourchapters were finally accepted for their publication after corrections requested byreviewers and editors were addressed.

This book content is structured in four parts: (1) Lean Manufacturing Tools andTechniques Applied to Industry, (2) Applications of Artificial IntelligenceTechniques for Industry, (3) Ergonomics Tools and Applications in IndustrialProcesses, and (4) Application of Logistics Tools to Improve Industrial Processes.

Part I Lean Manufacturing Tools and Techniques Applied to Industry: Thispart contains ten chapters.

Chapter 1, named SEM: A Global Technique—Case Applied to TPM, carried outby Martínez-Loya et al, from Universidad Autónoma de Ciudad Juárez (México),proposes a Structural Equation Modeling (SEM) applied the TPM technique toimprove the productivity in maquiladora industry of Ciudad Juárez.

Chapter 2, entitled Green Production Attributes and its Impact in Company’sSustainability, by Mendoza-Fong et al. from Universidad Autónoma de CiudadJuárez (México) and Instituto Tecnológico de Orizaba (México) designed aStructural Equation Modeling with four latent variables: green policy attributes,green attributes preproduction, green attributes in production processes and greenattributes in postproduction process to select a supplier. The proposed model wasvalidated in maquiladora industry of Ciudad Juárez, México.

Chapter 3, entitled Collaborative Multiobjective Model for Urban GoodsDistribution Optimization, by Arango-Serna et al. from Universidad Nacional deColombia (Facultad de Minas-Sede Medellín, Colombia) and Universidad de SanBuenaventura (Colombia) is presenting a genetic multiobjective model for thegoods distribution optimization through collaborative inventory between m sup-pliers and n customers. The model is based on vendor-managed inventory(VMI) strategy and was developed to improve a food distribution process inMedellín downtown (Colombia).

Chapter 4, entitled Multiagent System Modeling for the Coordination ofProcesses of Distribution of Goods Using a Memetical Algorithm, proposed byArango-Serna et al from Universidad Nacional de Colombia, Facultad deMinas-Sede Medellín (Colombia), and Universidad de San Buenaventura(Colombia), presented a multiagent model for the collection and delivery of goodsin a four-level distribution network integrated with a memetic algorithm thatfacilitates the resource assignment in the several levels and improving thedistribution process.

vi Preface

Chapter 5, named Operational Risk Prioritization in Supply Chain with 3PLUsing Fuzzy QFD, carried out by Osorio-Gómez et al. from Universidad del Valle(Colombia) and Universidad Autónoma de Ciudad Juárez (México), proposed theintegration of QFD—Fuzzy Logic for the prioritization of operational risks iden-tified on a supply chain, according to their impact on the most important perfor-mance indicators. This proposal was applied to two case studies for Colombiancompanies.

Chapter 6, entitled An Alternative to Multi-response Optimization Using aBayesian Approach, presented by Limón-Romero et al, from UniversidadAutónoma de Baja California (México), Universidade Federal de Santa Catarina(Brazil), Universidad Autónoma de San Luis Potosí (México) and UniversidadEstatal de Sonora (México), proposed the modification of a technique of simulta-neous optimization of multiple response variables that works using a Bayesianpredictive distribution to incorporate different weights to the response variablesaccording to their importance in the cost or functionality of products.

Chapter 7, entitled A Methodology for Optimizing the Parameters in a Processof Machining a Workpiece Using Multi-objective Particle Swarm Optimization,proposed by Vergara-Villegas et al, from Universidad Autónoma de Ciudad Juárez(México), used a methodology based on multiobjective particle swarm optimizationalgorithm in order to identify the optimal parameters for machining a workpiecewith a milling.

Chapter 8, named Lean Manufacturing: A Strategy for Waste Reduction, pro-posed by Báez-López et al, from Universidad Autónoma de Baja California(México), Universidad Autónoma de Ciudad Juárez (México), and Universidad deZaragoza (Spain), discussed the current state of lean manufacturing methodologyand described the leading tools that have contributed to waste reduction andincreased productivity in the industrial sector.

Chapter 9, named Collaborative New Product Development and theSupplier/Client Relationship: Cases from the Furniture Industry, presented byReis-Silva and Carrizo-Moreira from University of Aveiro (Portugal), identified theprocedures and management methods used by firms of the furniture industry oncollaborative new product development (CNPD) involving supplier–customerrelationships.

Chapter 10, entitled Realization and Demand for Training in the PlanningProcesses of Change: Empirical Evidences in the Wine Industry in Rioja Spain,proposed by Gil and Mataveli from University of La Rioja and National DistanceEducation University (Spain), analyzed the strategic planning and the implemen-tation of and requirements for continuous training in the Rioja wine sector in Spainand contributes to the field of study of administration of companies by investigatingkey industrial knowledge in the Spanish economy.

Part II Applications of Artificial Intelligence Techniques for Industry: Thispart contains five chapters.

Chapter 11, named Generation of User Interfaces for Mobile Applications UsingNeuronal Networks, presented by Sánchez-Morales et al, from InstitutoTecnológico de Orizaba (México) and Universidad Tecnológica de la Mixteca

Preface vii

(México), presented a software component for generating user interfaces for mobileapplications by using pattern recognition, image processing, and neural networkstechniques.

Chapter 12, entitled Association Analysis of Medical Opinions about theNon-realization of Autopsies in a Mexican Hospital, proposed by Rubio-Delgadoet al, from Instituto Tecnológico de Orizaba (México), Hospital Regional de RioBlanco (México), and Universidad Autónoma del Estado de México (México),applied different techniques such as data mining that allowed the construction of amodel, which is represented by a set of rules. The rules suggest that in opinion fromdoctors, some factors are related to decrease autopsies realization in the hospital.

Chapter 13, entitled Interdependent Projects Selection with PreferenceIncorporation, presented by Gomez et al, from Instituto Tecnológico de Madero(México), Universidad Autónoma de Ciudad Juárez (México), and InstitutoTecnológico de Tijuana (México), developed a strategy, based on ant colonyoptimization that incorporates the decision-maker's preferences into the solution ofa case of project portfolio problem under conditions of synergy, cannibalization,redundancy, and with interactions between projects. The algorithm was experi-mentally tested, and the results show a good performance of it over a random set ofinstances.

Chapter 14, named MED-IS-IN, an Intelligent Web App for RecognizingNon-prescription Drugs, presented by Ceh-Varela et al, from UniversidadTecnológica Metropolitana (México), Instituto Tecnológico de Mérida (México),and Instituto Tecnológico de Orizaba, explained that self-medication andself-prescription are common practices that can be observed in many countriesaround the world. The consequences of self-medication can range from a mildallergic reaction to death and for this reason, authors develop a Web App, whichuses a classifier model for counter medication based on computer vision andmachine learning techniques, such as Bag-of-visual-words, K-Means and SupportVector Machines.

Chapter 15, entitled A Brief Review of IoT Platforms and Applications inIndustry, by Machorro-Cano et al, from Instituto Tecnológico de Orizaba (México)and Universidad del Papaloapan (México), presented the application of Internet ofThings (IoT) in the industry, describing its application domains, platforms, andvarious study cases. In addition, it presents a comparative analysis of the studycases, as well as the trends and challenges of the IoT according to each domain ofapplication.

Part III Ergonomics Tools and Applications in Industrial Processes: Thispart contains six chapters.

Chapter 16, entitled A Theoretical Framework About the Impact of HumanFactors on Manufacturing Process Performance, by Arredondo-Soto et al., fromUniversidad Autónoma de Baja California (México) and Instituto Tecnológico deTijuana (México), proposed a theoretical framework of how the human factor, sincethe social, economic, and environmental dimension, affects the productivity. Theauthors analyzed and contrast the Toyota Production System (TPS), the FordProduction System (FPS), and the Caterpillar Production System (CPS).

viii Preface

Chapter 17, named Effects of Organizational Culture and Teamwork onManufacturing Systems’ Performance, by Realyvásquez et al, from InstitutoTecnológico de Tijuana (México) and Universidad Autónoma de Ciudad Juárez(México), determined the relationships between two macroergonomic elements,organizational culture and teamwork, and manufacturing systems’ performance(manufacturing processes, customers, and organizational performance).

Chapter 18, entitled Methodology to Determine Product Dimensions Based onUser Anthropometric Data, by Hernández-Arellano et al, from UniversidadAutónoma de Ciudad Juárez (México) and Universidad Autónoma de BajaCalifornia (México), proposed a method for dimensioning products based on user–product interactions and the user's anthropometric dimensions.

Chapter 19, Manual Lifting Standards: Ergonomic Assessment and Proposalsfor Redesign for Industrial Applications, by Prado-León Herrera-Lugo fromUniversidad de Guadalajara (México), presented fundamental guidelines in the fieldof ergonomics and, in particular, one of the most relevant tools for evaluating therisk implied in lifting heavy loads: The National Institute of Occupational Safetyand Health Lifting Equation, along with strategies to prevent or reduce this riskthrough three case studies related to industrial jobs within the context of México.

Chapter 20, named Relationship Between Social Support and BurnoutDimensions in Middle and Senior Managers of the Manufacturing Industry inCiudad Juárez, presented by Valadez-Torres et al, from Universidad Autónoma deCiudad Juárez (México), analyzed the relationships between social support and thethree dimensions of burnout syndrome (BS) (emotional exhaustion, cynicism, andprofessional efficacy). The research was carried out in six manufacturing companiesfrom Ciudad Juárez, México, and the sample included 361 middle and seniormanagers from different departments.

Chapter 21, named Stressing the Stress or the Complexity of the Human Factor:Psychobiological Consequences of Distress, by Serrano and Costa fromUniversidad de Valencia (Spain), addressed the topic of stress in industry, in orderto point out several aspects that can contribute to a better understanding of theeffects of stress on human factors and to show how stress is associated with diseasesin order to emphasize the need to tackle job stress prevention.

Part IV Application of Logistics Tools to Improve Industrial Processes: Thispart contains three chapters.

Chapter 22, named A Systemic Conceptual Model to Assess the Sustainability ofIndustrial Ecosystems, by Mota-López et al, from Instituto Tecnológico de Orizaba(México) and Universidad Veracruzana (México), proposed a conceptual model toanalyze four environmental impact factors: water consumption, energy consump-tion, emission of water pollutants, and emission of air pollutants, and evaluate thedamage they cause to ecosystems. This model is supported by systems dynamicssince it is a tool that successfully integrates all involved elements to effectivelyassess sustainability.

Chapter 23, entitled An Evolutive Tabu-Search Metaheuristic Approach for theCapacitated Vehicle Routing Problem, by Caballero-Morales et al, fromUniversidad Popular Autónoma del Estado de Puebla A.C. (México), presented the

Preface ix

design of a metaheuristic in order to provide near-optimal solutions for large CVRPinstances. The proposed metaheuristic integrates a two-stage solution process: First,a set of feasible CVRP routes are obtained by means of a tabu search(TS) algorithm, and second, a Genetic Algorithm (GA) is integrated to improve thefeasibility of each CVRP route.

Chapter 24, Production Planning for a Company in the Industry of CompactDiscs Mass Replications, by Moreno et al, from Universidad Panamericana CampusGuadalajara (México), analyzed a production planning problem for a company thatmass-replicates compact discs. The combination of attributes of the orders and theavailable machines for the processes generate a high complexity to determine theappropriate production routes and sequencing. To optimize the utilization of theproduction capacities, two approaches were proposed: a simulation model and alinear programming model.

Once a brief summary of chapters has been provided, we would also like toexpress our gratitude to the reviewers who kindly accepted to contribute in theevaluation of chapters at all stages of the editing process.

Ciudad Juárez, Mexico Jorge Luis García-AlcarazOrizaba, Mexico Giner Alor-HernándezCiudad Juárez, Mexico Aidé Aracely Maldonado-MacíasOrizaba, Mexico Cuauhtémoc Sánchez-Ramírez

x Preface

Acknowledgements

Guest editors will always be grateful for the talented technical reviewers whohelped review and improve this book. The knowledge and enthusiasm they broughtto the project was simply amazing.

Thus, we would like to thank:All our colleagues and friends from Universidad Autonoma de Ciudad Juarez

and the Instituto Tecnológico de Orizaba and for all their support.We equally and especially wish to thank Springer Verlag and associate editors of

Management and Industrial Engineering book series, for granting us the opportu-nity to edit this book and providing valuable comments to improve the selection ofresearch works.

Guest editors are grateful to the National Technological of Mexico for sup-porting this work. This book was also sponsored by the National Council of Scienceand Technology (CONACYT) as part of the project named Thematic Network inIndustrial Process Optimization, as well as by the Public Education Secretary(SEP) through PRODEP.

xi

Contents

Part I Lean Manufacturing Tools and Techniques Appliedto Industry

1 SEM: A Global Technique—Case Applied to TPM . . . . . . . . . . . . . 3Valeria Martínez-Loya, José Roberto Díaz-Reza,Jorge Luis García-Alcaraz and Jessica Yanira Tapia-Coronado

2 Green Production Attributes and Its Impact in Company’sSustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23José Roberto Mendoza-Fong, Jorge Luis García-Alcaraz,Humberto de Jesús Ochoa-Domínguez and Guillermo Cortes-Robles

3 Collaborative Multiobjective Model for Urban GoodsDistribution Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Martin Dario Arango-Serna, Julian Andres Zapata-Cortesand Conrado Augusto Serna-Uran

4 Multi-agent System Modeling for the Coordination of Processesof Distribution of Goods Using a Memetic Algorithm . . . . . . . . . . . 71Martin Dario Arango-Serna, Conrado Augusto Serna-Uranand Julian Andres Zapata-Cortes

5 Operational Risk Prioritization in Supply Chainwith 3PL Using Fuzzy-QFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Juan Carlos Osorio-Gómez, Diego Fernando Manotas-Duque,Leonardo Rivera-Cadavid and Ismael Canales-Valdiviezo

6 An Alternative to Multi-response Optimization Usinga Bayesian Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Jorge Limon-Romero, Guilherme Luz-Tortorella, Cesar Puente,José María Moreno-Jiménez and Marco Maciel-Monteon

xiii

7 A Methodology for Optimizing the Parameters in a Processof Machining a Workpiece Using Multi-objective ParticleSwarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129Osslan Osiris Vergara-Villegas, Carlos Felipe Ramírez-Espinoza,Vianey Guadalupe Cruz-Sánchez, Manuel Nandayapaand Raúl Ñeco-Caberta

8 Lean Manufacturing: A Strategy for Waste Reduction . . . . . . . . . . 153Marina De la Vega-Rodríguez, Yolanda Angélica Baez-Lopez,Dora-Luz Flores, Diego Alfredo Tlapaand Alejandro Alvarado-Iniesta

9 Collaborative New Product Development and the Supplier/ClientRelationship: Cases from the Furniture Industry . . . . . . . . . . . . . . . 175Luís Filipe Reis-Silva and António Carrizo-Moreira

10 Realization and Demand for Training in the PlanningProcesses of Change: Empirical Evidences in the WineIndustry in Rioja, Spain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197Alfonso J. Gil and Mara Mataveli

Part II Applications of Artificial Intelligence Techniquesfor Industry

11 Generation of User Interfaces for Mobile ApplicationsUsing Neuronal Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Laura N. Sánchez-Morales, Giner Alor-Hernández,Rosebet Miranda-Luna, Viviana Y. Rosales-Moralesand Cesar A. Cortes-Camarillo

12 Association Analysis of Medical Opinions Aboutthe Non-realization of Autopsies in a Mexican Hospital . . . . . . . . . . 233Elayne Rubio Delgado, Lisbeth Rodríguez-Mazahua,Silvestre Gustavo Peláez-Camarena, José Antonio Palet Guzmánand Asdrúbal López-Chau

13 Interdependent Projects Selection with PreferenceIncorporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253Claudia G. Gomez, Laura Cruz-Reyes, Gilberto Rivera,Nelson Rangel-Valdez, Maria Lucila Morales-Rodriguezand Mercedes Perez-Villafuerte

14 MED-IS-IN, an Intelligent Web App for RecognizingNon-prescription Drugs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273Eduardo Ceh-Varela, Gandhi Hernández-Chan,Marisol Villanueva-Escalante and José Luis Sánchez-Cervantes

xiv Contents

15 A Brief Review of IoT Platforms and Applications in Industry . . . .. . . . 293Isaac Machorro-Cano, Giner Alor-Hernández,Nancy Aracely Cruz-Ramos, Cuauhtémoc Sánchez-Ramírezand Mónica Guadalupe Segura-Ozuna

Part III Ergonomics Tools and Applications in Industrial Processes

16 A Theoretical Framework About the Impact of Human Factorson Manufacturing Process Performance . . . . . . . . . . . . . . . . . . . . . . 327Karina C. Arredondo-Soto, Teresa Carrillo-Gutiérrez,Marcela Solís-Quinteros and Guadalupe Hernández-Escobedo

17 Effects of Organizational Culture and Teamworkon Manufacturing Systems’ Performance . . . . . . . . . . . . . . . . . . . . . 353Arturo Realyvásquez, Aidé Aracely Maldonado-Macíasand Liliana Avelar-Sosa

18 Methodology to Determine Product Dimensions Basedon User Anthropometric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373Juan Luis Hernández-Arellano, Julián Israel Aguilar-Duqueand Karla Gabriela Gómez-Bull

19 Manual Lifting Standards: Ergonomic Assessmentand Proposals for Redesign for Industrial Applications . . . . . . . . . . 387Lilia R. Prado-León and Enrique Herrera-Lugo

20 Relationship Between Social Support and Burnout Dimensionsin Middle and Senior Managers of the Manufacturing Industryin Ciudad Juárez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409Sonia G. Valadez-Torres, Aidé Aracely Maldonado-Macías,Rocío Camacho-Alamilla and Liliana Avelar-Sosa

21 Stressing the Stress or the Complexity of the Human Factor:Psychobiological Consequences of Distress . . . . . . . . . . . . . . . . . . . . 431Miguel Ángel Serrano and Raquel Costa

Part IV Application of Logistics Tools to Improve IndustrialProcesses

22 A Systemic Conceptual Model to Assess the Sustainabilityof Industrial Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451Dulce-Rocío Mota-López, Cuauhtémoc Sánchez-Ramírez,Magno-Ángel González-Huerta, Yara Anahi Jiménez-Nietoand Adolfo Rodríguez-Parada

Contents xv

23 An Evolutive Tabu-Search Metaheuristic Approachfor the Capacitated Vehicle Routing Problem . . . . . . . . . . . . . . . . . . 477Santiago-Omar Caballero-Morales, José-Luis Martínez-Floresand Diana Sánchez-Partida

24 Production Planning for a Company in the Industry of CompactDiscs Mass Replications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497Miguel A. Moreno, Omar Rojas, Elias Olivares-Benitez,Samuel Nucamendi-Guillénand Hector Roberto Garcia de Alba Valenzuela

xvi Contents

Contributors

Julián Israel Aguilar-Duque Faculty of Engineering, Architecture and Design,Autonomous University of Baja California, Ensenada, Baja California, Mexico

Giner Alor-Hernández División de Estudios de Posgrado e Investigación,Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Alejandro Alvarado-Iniesta Departamento de Ingeniería Industrial e Ingenieríade Manufactura, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez,Chihuahua, Mexico

José Antonio Palet Guzmán Hospital Regional de Rio Blanco, H.R.R.B, RíoBlanco, Veracruz, Mexico

Martin Dario Arango-Serna Facultad de Minas-Sede Medellín, UniversidadNacional de Colombia, Medellín, Antioquia, Colombia

Karina C. Arredondo-Soto Universidad Autónoma de Baja California, Tijuana,Baja California, Mexico

Liliana Avelar-Sosa Department of Industrial and Manufacturing Engineering,Universidad Autonoma de Ciudad Juárez, Ciudad Juarez, Chih, Mexico

Yolanda Angélica Baez-Lopez Facultad de Ingeniería, Arquitectura y Diseño,Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico

Santiago-Omar Caballero-Morales Posgrado en Logística y Dirección de laCadena de Suministro, Universidad Popular Autónoma del Estado de Puebla A.C.,Puebla, Puebla, Mexico

Rocío Camacho-Alamilla Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chih, Mexico

Ismael Canales-Valdiviezo Department of Electric Engineering and Computation,Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

xvii

Teresa Carrillo-Gutiérrez Universidad Autónoma de Baja California, Tijuana,Baja California, Mexico

António Carrizo-Moreira GOVCOPP and Department of Economics,Management, Industrial Engineering and Tourism, University of Aveiro, CampusUniversitário de Santiago, Aveiro, Portugal

Eduardo Ceh-Varela Information Technology and Communication School,Universidad Tecnológica Metropolitana, Mérida, Mexico

Cesar A. Cortes-Camarillo División de Estudios de Posgrado e Investigación,Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Guillermo Cortes-Robles Instituto Tecnológico de Orizaba, Orizaba, Veracruz,Mexico

Raquel Costa Department of Psychobiology, University of Valencia, Valencia,Spain

Nancy Aracely Cruz-Ramos Division of Research and Postgraduate Studies,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Laura Cruz-Reyes National Mexican Institute of Technology/Madero Institute ofTechnology, Tamaulipas, Mexico

Vianey Guadalupe Cruz-Sánchez Instituto de Ingeniería y Tecnología,Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

Marina De la Vega-Rodríguez Facultad de Ingeniería, Arquitectura y Diseño,Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico

José Roberto Díaz-Reza Department of Electric Engineering and Computation,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Dora-Luz Flores Facultad de Ingeniería, Arquitectura y Diseño, UniversidadAutónoma de Baja California, Ensenada, Baja California, Mexico

Hector Roberto Garcia de Alba Valenzuela Facultad de Ingeniería, UniversidadPanamericana, Zapopan, Jalisco, Mexico

Jorge Luis García-Alcaraz Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Alfonso J. Gil University of La Rioja and National Distance Education University(UNED), Logroño, Spain

Claudia G. Gomez National Mexican Institute of Technology/Madero Institute ofTechnology, Tamaulipas, Mexico

Magno-Ángel González-Huerta Division of Research and Postgraduate Studies,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

xviii Contributors

Karla Gabriela Gómez-Bull Department of Industrial Engineering andManufacturing, Autonomous University of Ciudad Juarez, Ciudad Juárez,Chihuahua, Mexico

Juan Luis Hernández-Arellano Department of Design, Autonomous Universityof Ciudad Juarez, Ciudad Juárez, Chihuahua, Mexico

Gandhi Hernández-Chan Information Technology and Communication School,Universidad Tecnológica Metropolitana, Mérida, Mexico

Guadalupe Hernández-Escobedo Instituto Tecnológico de Tijuana, CalzadaTecnológico Sin Número, Tijuana, Baja California, Mexico

Enrique Herrera-Lugo University of Guadalajara Art, Architecture and DesignCenter, Guadalajara, Jalisco, Mexico

Yara Anahi Jiménez-Nieto Faculty of Accounting and Administration,Universidad Veracruzana Campus Ixtaczoquitlán, Veracruz, Mexico

Jorge Limon-Romero Facultad de Ingeniería, Arquitectura y Diseño, UniversidadAutónoma de Baja California, Ensenada, Baja California, Mexico

Guilherme Luz-Tortorella Universidade Federal de Santa Catarina,Florianópolis, Santa Catarina, Brazil

Asdrúbal López-Chau Universidad Autónoma del Estado de México, Zumpango,Estado de México, Mexico

Isaac Machorro-Cano Division of Research and Postgraduate Studies, InstitutoTecnológico de Orizaba, Orizaba, Veracruz, Mexico

Marco Maciel-Monteon Facultad de Ingeniería, Arquitectura y Diseño,Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico

Aidé Aracely Maldonado-Macías Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chih, Mexico

Diego Fernando Manotas-Duque Escuela de Ingeniería Industrial—Facultad deIngeniería, Universidad del Valle, Cali, Colombia

José-Luis Martínez-Flores Posgrado en Logística y Dirección de la Cadena deSuministro, Universidad Popular Autónoma del Estado de Puebla A.C., Puebla,Puebla, Mexico

Valeria Martínez-Loya Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Mara Mataveli University of La Rioja, Logroño, Spain

José Roberto Mendoza-Fong Universidad Autónoma de Ciudad Juárez, Juárez,Chihuahua, Mexico

Contributors xix

Rosebet Miranda-Luna Instituto de Electrónica y Mecatrónica, UniversidadTecnológica de la Mixteca, Huajuapan de León, Oaxaca, Mexico

Maria Lucila Morales-Rodriguez National Mexican Institute ofTechnology/Madero Institute of Technology, Tamaulipas, Mexico

José María Moreno-Jiménez Facultad de Economía y Empresa, Universidad deZaragoza, Zaragoza, Zaragoza, Spain

Miguel A. Moreno Facultad de Ingeniería, Universidad Panamericana, Zapopan,Jalisco, Mexico

Dulce-Rocío Mota-López Division of Research and Postgraduate Studies,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Manuel Nandayapa Instituto de Ingeniería y Tecnología, Universidad Autónomade Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

Samuel Nucamendi-Guillén Facultad de Ingeniería, Universidad Panamericana,Zapopan, Jalisco, Mexico

Humberto de Jesús Ochoa-Domínguez Universidad Autónoma de CiudadJuárez, Juárez, Chihuahua, Mexico

Elias Olivares-Benitez Facultad de Ingeniería, Universidad Panamericana,Zapopan, Jalisco, Mexico

Juan Carlos Osorio-Gómez Escuela de Ingeniería Industrial—Facultad deIngeniería, Universidad del Valle, Cali, Colombia

Silvestre Gustavo Peláez-Camarena División de Estudios de Posgrado eInvestigación, Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Mercedes Perez-Villafuerte Computer Science in the Graduate Division,National Mexican Institute of Technology/Tijuana Institute of Technology, BajaCalifornia, Mexico

Lilia R. Prado-León University of Guadalajara Art, Architecture and DesignCenter, Guadalajara, Jalisco, Mexico

Cesar Puente Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí,San Luis Potosí, Mexico

Carlos Felipe Ramírez-Espinoza Instituto de Ingeniería y Tecnología,Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

Nelson Rangel-Valdez CONACYT, National Mexican Institute ofTechnology/Madero Institute of Technology, Tamaulipas, Mexico

Arturo Realyvásquez Department of Industrial Engineering, InstitutoTecnológico de Tijuana, Tijuana, Mexico

xx Contributors

Luís Filipe Reis-Silva Department of Economics, Management, IndustrialEngineering and Tourism, University of Aveiro, Campus Universitário de Santiago,Aveiro, Portugal

Leonardo Rivera-Cadavid Escuela de Ingeniería Industrial—Facultad deIngeniería, Universidad del Valle, Cali, Colombia

Gilberto Rivera Institute of Engineering and Technology, AutonomousUniversity of Ciudad Juarez, Chihuahua, Mexico

Lisbeth Rodríguez-Mazahua División de Estudios de Posgrado e Investigación,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Adolfo Rodríguez-Parada Faculty of Accounting and Administration,Universidad Veracruzana Campus Ixtaczoquitlán, Veracruz, Mexico

Omar Rojas Escuela de Ciencias Económicas y Empresariales, UniversidadPanamericana, Zapopan, Jalisco, Mexico

Viviana Y. Rosales-Morales División de Estudios de Posgrado e Investigación,Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Elayne Rubio Delgado División de Estudios de Posgrado e Investigación,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Mónica Guadalupe Segura-Ozuna Universidad del Papaloapan (UNPA),Tuxtepec, Oaxaca, Mexico

Conrado Augusto Serna-Uran Universidad de San Buenaventura. Carrera,Medellín, Antioquia, Colombia

Miguel Ángel Serrano Department of Psychobiology, University of Valencia,Valencia, Spain

Marcela Solís-Quinteros Universidad Autónoma de Baja California, Tijuana,Baja California, Mexico

José Luis Sánchez-Cervantes CONACYT—Instituto Tecnológico de Orizaba,Orizaba, Mexico

Laura N. Sánchez-Morales División de Estudios de Posgrado e Investigación,Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Diana Sánchez-Partida Posgrado en Logística y Dirección de la Cadena deSuministro, Universidad Popular Autónoma del Estado de Puebla A.C., Puebla,Puebla, Mexico

Cuauhtémoc Sánchez-Ramírez Division of Research and Postgraduate Studies,Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

Contributors xxi

Jessica Yanira Tapia-Coronado Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Diego Alfredo Tlapa Facultad de Ingeniería, Arquitectura y Diseño, UniversidadAutónoma de Baja California, Ensenada, Baja California, Mexico

Sonia G. Valadez-Torres Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chih, Mexico

Osslan Osiris Vergara-Villegas Instituto de Ingeniería y Tecnología, UniversidadAutónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

Marisol Villanueva-Escalante Instituto Tecnológico de Mérida, Mérida, Mexico

Julian Andres Zapata-Cortes Universidad de San Buenaventura. Carrera,Medellín, Antioquia, Colombia

Raúl Ñeco-Caberta Instituto de Ingeniería y Tecnología, Universidad Autónomade Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico

xxii Contributors

List of Figures

Fig. 1.1 Structural equation model graphical representation.a General, b Specific . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Fig. 1.2 SEM publications by the main author’s country . . . . . . . . . . . . 9Fig. 1.3 Timeline of SEM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Fig. 1.4 Use of SEM by sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Fig. 1.5 Proposed model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Fig. 1.6 Evaluated model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Fig. 2.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Fig. 2.2 Evaluated model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Fig. 3.1 Chromosome representation (individuals). Source Authors . . . . 58Fig. 3.2 a Crossover, b Mutation procedures. Source Authors . . . . . . . . 59Fig. 3.3 Comparison of ach objective function values for individuals

1, 14 and 7. Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . 63Fig. 3.4 Surface dispersion of the individuals of the solution set.

Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Fig. 3.5 Pareto frontiers by pairs of objective functions. a Unsatisfied

TW versus required paths, b unsatisfied TW versus cost,c required paths versus costs. Source Authors . . . . . . . . . . . . . . 64

Fig. 4.1 Multi-agent architecture for the distribution problem . . . . . . . . . 75Fig. 4.2 Communication protocols between agents for the SMAD . . . . . 77Fig. 4.3 UML sequence diagram for the evolutionary process

in SISMAM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Fig. 4.4 Structure of behaviors in SISMAN. UML classes diagram . . . . 83Fig. 4.5 a Distribution of factories, hubs and terminals,

b Distribution of customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Fig. 4.6 Evolution total route travel in terminal T22. . . . . . . . . . . . . . . . 87Fig. 5.1 “WHAT”–“HOW” correlation scores. . . . . . . . . . . . . . . . . . . . . 100Fig. 5.2 Risks impact in strategic objectives . . . . . . . . . . . . . . . . . . . . . . 102Fig. 5.3 “WHAT”–“HOW” correlation scores. . . . . . . . . . . . . . . . . . . . . 104Fig. 5.4 Risks impact in strategic objectives . . . . . . . . . . . . . . . . . . . . . . 105Fig. 6.1 Contour plot for yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

xxiii

Fig. 6.2 Contour plot for viscosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Fig. 6.3 Contour plot for molecular weight. . . . . . . . . . . . . . . . . . . . . . . 115Fig. 6.4 Overlapping of yield, viscosity, and molecular weight

contour plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Fig. 6.5 Desirability function according to different

values of s and t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Fig. 6.6 Comparison of D1 values in the four scenarios . . . . . . . . . . . . . 125Fig. 6.7 Comparison of D2 values in the four scenarios . . . . . . . . . . . . . 126Fig. 7.1 The necessary stages for solving the multi-objective problem . . . . 136Fig. 7.2 The VIWA VF3KM400 milling machine used for workpieces

machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Fig. 7.3 The drawing of the first workpiece . . . . . . . . . . . . . . . . . . . . . . 138Fig. 7.4 The first workpiece designed in: a Solidworks®,

b Mastercam X® . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Fig. 7.5 The first workpiece designed . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Fig. 7.6 The drawing of the second workpiece . . . . . . . . . . . . . . . . . . . . 140Fig. 7.7 The second workpiece designed in: a CATIA,

b Mastercam X® . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Fig. 7.8 The second workpiece designed . . . . . . . . . . . . . . . . . . . . . . . . 142Fig. 7.9 The algorithm for performing MOPSO (Reyes et al. 2006) . . . . 144Fig. 8.1 House of the Toyota production system . . . . . . . . . . . . . . . . . . 157Fig. 8.2 General process map. General process map is used to illustrate

general emergency department flow. Our staff whenredesigning process to improve quality and flow uses adetailed process map. We also use process maps tocommunicate changes in workflow to staff . . . . . . . . . . . . . . . . 166

Fig. 8.3 Example of value stream map (VSM). The VSM is generatedafter a period of observation during which cycle times (C/T)are measured. The VSM provides the process improvementteam with an overview of value added (C/T) and non-valueadded (waste) activities in a process, which help them focuson high-yield areas and map progress . . . . . . . . . . . . . . . . . . . . 166

Fig. 8.4 JIT production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Fig. 10.1 The relationships between change, training and learning.

Source Gil et al. (2015, p. 218) . . . . . . . . . . . . . . . . . . . . . . . . . 200Fig. 11.1 User interfaces generation process from image processing. . . . . 216Fig. 11.2 XML-based document structure configuration . . . . . . . . . . . . . . 219Fig. 11.3 Rule for the UI Design Pattern: Login . . . . . . . . . . . . . . . . . . . . 220Fig. 11.4 Partial tree representation of the rule for Login

UI Design Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Fig. 11.5 Samples of UI Design Patterns: a Video, b Datalist,

c Login and d Carrousel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Fig. 11.6 Freehand generated image that represents a Login pattern. . . . . 226Fig. 11.7 Pattern recognition in nonideal UI elements . . . . . . . . . . . . . . . 227

xxiv List of Figures

Fig. 11.8 .txt file generated encapsulating the main characteristicsof the UI Design Pattern identified . . . . . . . . . . . . . . . . . . . . . . 227

Fig. 11.9 a Loading the XML-based configuration file generated.b Source code of the application packed into a .ZIP file . . . . . . 228

Fig. 11.10 a Structure of generated source code for the user interfaceLogin. b Login patron deployed from Android Studio. . . . . . . . 228

Fig. 12.1 Main areas explored by the survey . . . . . . . . . . . . . . . . . . . . . . 240Fig. 12.2 Level of education of the doctors . . . . . . . . . . . . . . . . . . . . . . . 240Fig. 12.3 Intervention of the physicians in autopsy cases . . . . . . . . . . . . . 241Fig. 14.1 Architecture of web application . . . . . . . . . . . . . . . . . . . . . . . . . 279Fig. 14.2 Classifier’s sequence diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 280Fig. 14.3 New image classification and information retrieval . . . . . . . . . . 281Fig. 14.4 Ontology architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Fig. 14.5 Omeprazole hierarchy in drugs ontology . . . . . . . . . . . . . . . . . . 282Fig. 14.6 Four-phase methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283Fig. 14.7 An example of the images used for training . . . . . . . . . . . . . . . 284Fig. 14.8 A SIFT orientation histogram for one of the Sensibit

D box keypoints on the left . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Fig. 14.9 Extracting the visual-word vectors (Kodinariya

and Makwana 2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Fig. 14.10 The elbow plot to choose k . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Fig. 14.11 Visual word histogram. a Histograms for Alka Seltzer

boxes, b histograms for different Pepto Bismol bottles . . . . . . . 287Fig. 14.12 Confusion matrix and classification report for training

dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288Fig. 14.13 Confusion matrix and classification report for the test

dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Fig. 15.1 IoT application domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295Fig. 15.2 Service-Oriented Architecture (SOA) for the IoT

(Li et al. 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304Fig. 15.3 Trends and challenges of the IoT application domains . . . . . . . 320Fig. 16.1 Research structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329Fig. 16.2 The research methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330Fig. 16.3 Aspects of labor practices and decent work . . . . . . . . . . . . . . . . 333Fig. 16.4 Aspects of human rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334Fig. 16.5 Aspects of society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334Fig. 16.6 Aspects of product responsibility . . . . . . . . . . . . . . . . . . . . . . . . 334Fig. 17.1 Hypothetical structural model . . . . . . . . . . . . . . . . . . . . . . . . . . 359Fig. 17.2 Directs effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364Fig. 17.3 Significant directs effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365Fig. 18.1 Dimensions 1, 2, and 3 of the bench . . . . . . . . . . . . . . . . . . . . . 379Fig. 18.2 Dimensions 1, 2, 3, and 4 of the school desk . . . . . . . . . . . . . . 381Fig. 18.3 Dimensions 5 and 6 of the school desk . . . . . . . . . . . . . . . . . . . 382Fig. 18.4 Dimensions 7, 8, 9, and 10 of the school desk . . . . . . . . . . . . . 383

List of Figures xxv

Fig. 19.1 Loading dock workers (beer cartons) . . . . . . . . . . . . . . . . . . . . . 394Fig. 19.2 Utilizing an elevator system to lift the load . . . . . . . . . . . . . . . . 396Fig. 19.3 Using a leveler pallet rotator allows placing the stack

in the most convenient position for the worker . . . . . . . . . . . . . 396Fig. 19.4 Using a deflector on the conveyor . . . . . . . . . . . . . . . . . . . . . . . 397Fig. 19.5 Packer (dextrose sacks) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398Fig. 19.6 Roller conveyor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Fig. 19.7 Milk crate loader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400Fig. 19.8 Robotic arm for loading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402Fig. 19.9 Automatic loading or stacking system . . . . . . . . . . . . . . . . . . . . 402Fig. 19.10 Packer’s working posture, outside NIOSH guidelines . . . . . . . . 403Fig. 19.11 Analysis of packer posture with 3DSSPP © v.6.0 . . . . . . . . . . . 404Fig. 20.1 Hypothetical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414Fig. 20.2 Structural model: Burnout-social support . . . . . . . . . . . . . . . . . . 423Fig. 21.1 Work stress and risk prevention diagram. . . . . . . . . . . . . . . . . . 433Fig. 21.2 Model of job stress and burnout . . . . . . . . . . . . . . . . . . . . . . . . 443Fig. 22.1 Block diagram for the conceptual model . . . . . . . . . . . . . . . . . . 462Fig. 22.2 Methodology to assess industrial ecosystem sustainability . . . . . 464Fig. 22.3 Causal loop diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467Fig. 22.4 Water consumption feedback loop . . . . . . . . . . . . . . . . . . . . . . . 468Fig. 22.5 Emissions of water pollutants feedback loop . . . . . . . . . . . . . . . 468Fig. 22.6 Energy consumption feedback loop . . . . . . . . . . . . . . . . . . . . . . 469Fig. 22.7 Emissions of air pollutants feedback loop . . . . . . . . . . . . . . . . . 469Fig. 23.1 VRP distribution network (one depot, N = 26 customers) . . . . . 481Fig. 23.2 General diagram of the E-TS metaheuristic . . . . . . . . . . . . . . . . 483Fig. 23.3 General diagram of the TS algorithm . . . . . . . . . . . . . . . . . . . . 484Fig. 23.4 Description of the diversification moves for the TS algorithm

(depot in location 0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Fig. 23.5 General diagram of the GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486Fig. 23.6 Description of the reproduction operators for the GA . . . . . . . . 487Fig. 23.7 Illustrative example 1: results of the E-TS metaheuristic

on the instance X-n327-k20 (depot in location 1) . . . . . . . . . . . 488Fig. 23.8 Illustrative example 2: results of the E-TS metaheuristic

on the instance X-n513-k21 (depot in location 1) . . . . . . . . . . . 489Fig. 23.9 Results of the E-TS metaheuristic on the real-world

instance (depot in location 1). . . . . . . . . . . . . . . . . . . . . . . . . . . 490Fig. 24.1 Basic scheme for simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 504Fig. 24.2 Overview of the simulation model. . . . . . . . . . . . . . . . . . . . . . . 504Fig. 24.3 Time share in different processes in the simulation model . . . . . 508Fig. 24.4 Time share for mini-plants 5 and 9 . . . . . . . . . . . . . . . . . . . . . . 508Fig. 24.5 Time share for offset cells 1 and 2 . . . . . . . . . . . . . . . . . . . . . . 509Fig. 24.6 Time share for screen cells 3, 4 and 5. . . . . . . . . . . . . . . . . . . . 509

xxvi List of Figures

Fig. 24.7 Utilization of replication and printing machines alongthe three planning days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Fig. 24.8 Comparative of number of discs to be producedby each model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512

Fig. 24.9 Prioritization of discs by each method . . . . . . . . . . . . . . . . . . . . 513

List of Figures xxvii

List of Tables

Table 1.1 Journals name and number of publications . . . . . . . . . . . . . . . 8Table 1.2 Items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Table 1.3 Questionnaire validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Table 1.4 Model fit indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Table 1.5 Direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Table 2.1 Green attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Table 2.2 Area of the company in which it performs and gender . . . . . . 36Table 2.3 Latent variable coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Table 2.4 Descriptive analysis of items . . . . . . . . . . . . . . . . . . . . . . . . . . 37Table 2.5 Direct effects and conclusion of hypotheses . . . . . . . . . . . . . . 40Table 2.6 Sum of indirect effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Table 2.7 Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Table 3.1 Demand, inventory cost, and other information

of customers. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . 61Table 3.2 Solution set for the multiobjective model . . . . . . . . . . . . . . . . 62Table 3.3 Comparison between results for individuals 1, 14

and the current conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Table 4.1 Collection and delivery routes (R1, R2, R3) . . . . . . . . . . . . . . 86Table 4.2 Solution routes 1 with memetic algorithm . . . . . . . . . . . . . . . . 87Table 5.1 Papers related to multi-attribute tools in supply chain risk

management evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94Table 5.2 Papers related to QFD and FQFD applications

in SCM and SCRM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96Table 5.3 Linguistic scale to FQFD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Table 5.4 WHATs and their weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Table 5.5 Risks prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Table 5.6 WHATs and their weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Table 5.7 Risks prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Table 6.1 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Table 6.2 Some combinations of the control factors to be used

in the simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

xxix

Table 6.3 Specifications of the response variables . . . . . . . . . . . . . . . . . . 122Table 6.4 Results obtained from the simulation. . . . . . . . . . . . . . . . . . . . 123Table 6.5 Weights considered for the response variables

in each scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Table 6.6 Optimal process operating conditions . . . . . . . . . . . . . . . . . . . 125Table 6.7 Confidence intervals for the desirabilities in each

response variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Table 7.1 A summary of 16 works where multi-objective

optimization of machining parameters was presented . . . . . . . 135Table 7.2 Results obtained from experiment 1 . . . . . . . . . . . . . . . . . . . . 146Table 7.3 Improvement percentages from experiment 1 . . . . . . . . . . . . . 147Table 7.4 Results obtained from experiment 2 . . . . . . . . . . . . . . . . . . . . 148Table 7.5 Improvement percentages from experiment 2 . . . . . . . . . . . . . 148Table 8.1 A brief history of manufacturing system design . . . . . . . . . . . 156Table 8.2 Origin and evolution of the principles lean . . . . . . . . . . . . . . . 156Table 8.3 Process principles of lean product development . . . . . . . . . . . 161Table 8.4 Tools and techniques mentioned in period

1 and period 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Table 9.1 Main characteristics of the firms studied . . . . . . . . . . . . . . . . . 180Table 9.2 New product development process . . . . . . . . . . . . . . . . . . . . . 181Table 9.3 Characteristics of CNPD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183Table 9.4 Criteria for selecting suppliers and product typology

in CNPD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Table 9.5 Characterization of innovation . . . . . . . . . . . . . . . . . . . . . . . . . 190Table 9.6 Summary of NPD at sub-sector level . . . . . . . . . . . . . . . . . . . 190Table 10.1 Sample characteristics of wine company . . . . . . . . . . . . . . . . . 201Table 10.2 Strategic challenges of the wineries of the Rioja

wine industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204Table 10.3 Analysis of variance for independent variables:

“cultivation”, “processing” and “marketing” andthe dependent variable “conducting training courses” . . . . . . . 204

Table 10.4 Analysis of variance for independent variables:“cultivation”, “processing” and “marketing” andthe dependent variable “demand training courses”. . . . . . . . . .

Table 11.1 Comparative analysis of related works . . . . . . . . . . . . . . . . . . 215Table 11.2 Training and evaluation results with 1 hidden layer . . . . . . . . 224Table 11.3 Training and evaluation results with 2 hidden layer . . . . . . . . 225Table 12.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Table 12.2 Summary of the survey applied to the medical staff . . . . . . . . 239Table 12.3 Datasets characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Table 12.4 Results of applying the Apriori algorithm

to the dataset C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242Table 12.5 Results of applying the Apriori algorithm

to the dataset C1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

xxx List of Tables

Table 12.6 Results of applying the Apriori algorithmto the dataset C1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Table 12.7 Results of applying the Apriori algorithmto the dataset C1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

Table 12.8 Results of applying the Apriori algorithmto the dataset C2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

Table 12.9 Results of applying the Apriori algorithmto the dataset D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

Table 12.10 Preliminary results of the medical opinions about autopsies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

Table 12.11 Results of applying the Apriori algorithmto the dataset C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Table 12.12 Results of applying the Apriori algorithmto the dataset C1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Table 12.13 Results of applying the Apriori algorithmto the dataset C2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Table 12.14 Results of applying the Apriori algorithmto the dataset D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Table 12.15 Preliminary results of the medical opinions aboutautopsies using lift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Table 13.1 Comparison among the solution set producedby ACOS and the ranking method . . . . . . . . . . . . . . . . . . . . . 267

Table 13.2 Results from ACOS over instances with 100 projectsand nine objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268

Table 13.3 Results from ACOS over instances with 25 projectsand four objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Table 14.1 Medication images distribution . . . . . . . . . . . . . . . . . . . . . . . . 283Table 15.1 IoT application platform (Han et al. 2016) . . . . . . . . . . . . . . . 306Table 15.2 Comparative analysis for IoT study cases (A) . . . . . . . . . . . . . 315Table 15.3 Comparative analysis for IoT study cases (B) . . . . . . . . . . . . . 316Table 15.4 Comparative analysis for IoT study cases (C) . . . . . . . . . . . . . 317Table 15.5 Comparative analysis for IoT study cases (D) . . . . . . . . . . . . . 318Table 15.6 Comparative analysis for IoT study cases (E) . . . . . . . . . . . . . 319Table 16.1 Economic indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331Table 16.2 Lean manufacturing tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342Table 16.3 TPS strategic performance indicators. . . . . . . . . . . . . . . . . . . . 342Table 16.4 Disciplines of commitment . . . . . . . . . . . . . . . . . . . . . . . . . . . 343Table 16.5 FPS strategic performance indicator . . . . . . . . . . . . . . . . . . . . 344Table 16.6 CPS strategic performance indicator . . . . . . . . . . . . . . . . . . . . 345Table 16.7 General economic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346Table 16.8 General environmental. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346Table 16.9 General social . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346Table 17.1 MCQ validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363Table 17.2 Model fit and quality indices . . . . . . . . . . . . . . . . . . . . . . . . . . 364

List of Tables xxxi

Table 17.3 Sum of indirect effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366Table 17.4 Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367Table 18.1 Methodological process of the industrial design . . . . . . . . . . . 376Table 18.2 Steps 2, 3, 4 and 5. Dimensioning the bench . . . . . . . . . . . . . 378Table 18.3 Shows the final dimensions of the bench. . . . . . . . . . . . . . . . . 380Table 18.4 Steps 2, 3, 4 and 5. Dimensioning the school desk . . . . . . . . . 381Table 18.5 Dimensions of the product. . . . . . . . . . . . . . . . . . . . . . . . . . . . 383Table 19.1 MMH: component task, its elements and indicators . . . . . . . . 389Table 20.1 Surveyed employees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418Table 20.2 Descriptive analysis of job content . . . . . . . . . . . . . . . . . . . . . 419Table 20.3 Descriptive analysis of Burnout . . . . . . . . . . . . . . . . . . . . . . . . 420Table 20.4 Validation of latent variables . . . . . . . . . . . . . . . . . . . . . . . . . . 421Table 20.5 Combined loadings and cross-loadings . . . . . . . . . . . . . . . . . . 421Table 20.6 Model fit and quality indices . . . . . . . . . . . . . . . . . . . . . . . . . . 422Table 20.7 Direct effects between latent variables . . . . . . . . . . . . . . . . . . . 424Table 20.8 Validation of hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425Table 20.9 R2 contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426Table 20.10 Sum of indirect effects between latent variables . . . . . . . . . . . 426Table 20.11 Total effects in latent variables . . . . . . . . . . . . . . . . . . . . . . . . 426Table 22.1 Sustainability assessment tools . . . . . . . . . . . . . . . . . . . . . . . . 455Table 22.2 Characteristics of the sustainability assessment tools . . . . . . . . 456Table 22.3 Environmental risk by industry sector . . . . . . . . . . . . . . . . . . . 461Table 22.4 Variables of the causal loop diagram. . . . . . . . . . . . . . . . . . . . 466Table 23.1 Size of CVRP instances solved with constructive,

local search, population, learning, and hybrid heuristicsand metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480

Table 23.2 Results of the E-TS metaheuristic on benchmarkinstances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

Table 24.1 Indexation of discs formats and printing techniques . . . . . . . . 503Table 24.2 Mini-plants and cells where orders can be processed

for each format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Table 24.3 Orders and discs planned for production by the simulation

model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507Table 24.4 Orders and discs planned for production by the LPM . . . . . . . 510Table 24.5 Some orders in the master plan obtained by the LPM. . . . . . . 510Table 24.6 Orders and discs planned by the company by day

of the week . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511Table 24.7 Required processing times (min) . . . . . . . . . . . . . . . . . . . . . . . 512

xxxii List of Tables