A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

A multiple criteria decision-making approach for increasing the preparedness level of sales departments against COVID-19 and future pandemics: A real-world case

The impact of the pandemic and the lockdown has been more devastating than expected on the world economy. It is essential to formulate strategies in real-time. In this research, a multicriteria decision-making model for increasing the preparedness level of sales departments when facing COVID-19 waves and future pandemics is proposed. The model is comprised of 8 criteria, 29 sub-criteria, and 7 alternatives. The study is based on the integration of the AHP and TOPSIS techniques. AHP is used for calculating the criteria and sub-criteria weights. While, TOPSIS is used for calculating the preparedness level, ranking the companies, and identifying the weaknesses that should be addressed for increasing their effectiveness in the current market scenario. The model is developed with the aid of an experts’ group from the electrical appliance sector and studies from the reported literature. This application is completely novel in the literature and has been applied in the wild with remarkable companies in Colombia. A case study in the electrical appliance sector is presented as a pilot study but it should be noted that the methodology is flexible and scalable in any scenario

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThe objective of this thesis is to contribute for a structured and systematic decision-making process for industrial companies, particularly involving several actors, helping them make the best use of their resources. The paradigms of how industrial companies operate have been progressively changing over the last two decades. The flexible and dynamic flow of information and persons over companies has created new challenges and opportunities for industry. It is not possible to dissociate an enterprise from its human resources and the knowledge they create and use. Companies face decisions constantly, involving several actors and situations. With the market pressure and rapid changing environments, decisions are becoming more complex, and involving more people with complementary expertise. The knowledge processes are only efficient if the actors can anchor and relate the information handled to the extended enterprise. Therefore, an enterprise model is a fundamental aspect to support decision-making in industry. This work includes an overview of existing modelling methodologies and standards. Afterwards, it proposes an enterprise model to represent an extended or virtual enterprise, suitable not only for decision-making applications but also for others. This thesis considers methods and systems to support decision and analyses decision types and processes. Afterwards, the thesis presents some considerations on decision-making in industry and a generic decision-making process, including, a review of decision criteria commonly used in industry. Two of the methods widely used in some of the mentioned areas, case-based reasoning and the analytic hierarchy process, have been used in the scope of problem solving and decision-making, respectively. This thesis presents an approach based on a combination of case-based reasoning and analytic hierarchy process to support innovation, particularly product design in industry. The combination overcomes shortcomings of both methods to provide the most adequate decision support for multi-disciplinary teams in innovation processes. Moreover, the work presented proposes an algorithm for automatic adjustment of the weight of the actors in the decision process. This thesis includes case studies, developed in the scope of several research projects, used as practical applications of the work developed. These practical applications include seven test cases (with two manufacturing companies, two assembling companies, two engineering services companies and one software company) where the proposed enterprise model and methods have been applied with the purpose of supporting decisions. This highlights the wide application of the proposed model, describing its possible interpretations and the successful use of the decision support approach in industrial companies.Projects PICK (IST-1999-10442), AIM (IST-2001-52222), FOKSai (COOP-CT-2003-508637), InLife (FP6-2005-NMP2-CT-517018), InAmI (FP6-2004-IST-NMP-2-16788) and K-NET (FP7-ICT-1-215584), all of which were partially funded by the Research Framework Programs of the European Unio

Understanding location decisions of energy multinational enterprises within the European smart cities’ context: An integrated AHP and extended fuzzy linguistic TOPSIS method

Becoming a smart city is one of the top priorities in the urban agenda of many European cities. Among the various strategies in the transition path, local governments seek to bring innovation to their cities by encouraging multinational enterprises to deploy their green energy services and products in their municipalities. Knowing how to attract these enterprises implies that political leaders understand the multi-criteria decision problem that the energy sector enterprises face when deciding whether to expand to one city or another. To this end, the purpose of this study is to design a new manageable and controllable framework oriented to European cities’ public managers, based on the assessment of criteria and sub-criteria governing the strategic location decision made by these enterprises. A decision support framework is developed based on the AHP technique combined with an extended version of the hesitant fuzzy linguistic TOPSIS method. The main results indicate the higher relative importance of government policies, such as degree of transparency or bureaucracy level, as compared to market conditions or economic aspects of the city’s host country. These results can be great assets to current European leaders, they show the feasibility of the method and open up the possibility to replicate the proposed framework to other sectors or geographical areas.The authors acknowledge the support from the European Union “Horizon 2020 Research and Innovation Programme” under the grant agreements No 731297. Also, this research has been partially supported by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.Peer ReviewedPostprint (published version

Multi-criteria decision methods to support the maintenance management of complex systems

[ES] Esta tesis doctoral propone el uso de métodos de toma de decisiones multi-criterio (MCDM, por sus iniciales en inglés) como herramienta estratégica para apoyar la gestión del mantenimiento de sistemas complejos. El desarrollo de esta tesis doctoral se enmarca dentro de un acuerdo de cotutela entre la Università degli Studi di Palermo (UNIPA) y la Universitat Politècnica de València (UPV), dentro de sus respectivos programas de doctorado en 'Ingeniería de Innovación Tecnológica' y 'Matemáticas'. Estos programas están estrechamente vinculados a través del tópico MCDM, ya que proporciona herramientas cruciales para gestionar el mantenimiento de sistemas complejos reales utilizando análisis matemáticos serios. El propósito de esta sinergia es tener en cuenta de forma sólida la incertidumbre al atribuir evaluaciones subjetivas, recopilar y sintetizar juicios atribuidos por varios responsables de la toma de decisiones, y tratar con conjuntos grandes de esos elementos. El tema principal del presente trabajo de doctorado es el gestionamiento de las actividades de mantenimiento para aumentar los niveles de innovación tecnológica y el rendimiento de los sistemas complejos. Cualquier sistema puede ser considerado objeto de estudio, incluidos los sistemas de producción y los de prestación de servicios, entre otros, mediante la evaluación de sus contextos reales. Esta tesis doctoral propone afrontar la gestión del mantenimiento a través del desarrollo de tres líneas principales de investigación estrechamente vinculadas. ¿ La primera es el núcleo, e ilustra la mayoría de los aspectos metodológicos de la tesis. Se refiere al uso de métodos MCDM para apoyar decisiones estratégicas de mantenimiento, y para hacer frente a la incertidumbre que afecta a los datos/evaluaciones, incluso cuando están involucrados varios responsables (expertos en mantenimiento) en la toma de decisiones. ¿ La segunda línea desarrolla análisis de fiabilidad para sistemas complejos reales (también en términos de fiabilidad humana) sobre cuya base se debe implementar cualquier actividad de mantenimiento. Estos análisis consideran la configuración de fiabilidad de los componentes del sistema en estudio y las características específicas del entorno operativo. ¿ La tercera línea de investigación aborda aspectos metodológicos importantes de la gestión de mantenimiento y enfatiza la necesidad de monitorizar el funcionamiento de las actividades de mantenimiento y de evaluar su efectividad utilizando indicadores adecuados. Se ha elaborado una amplia gama de casos de estudio del mundo real para evaluar la eficacia de los métodos MCDM en el mantenimiento y así probar la utilidad del enfoque propuesto.[CA] Aquesta tesi doctoral proposa l'ús de mètodes de presa de decisions multi-criteri (MCDM, per les seves inicials en anglès) com a eina estratègica per donar suport a la gestió del manteniment de sistemes complexos. El desenvolupament d'aquesta tesi doctoral s'emmarca dins d'un acord de cotutela entre la Università degli Studi di Palermo (UNIPA) i la Universitat Politècnica de València (UPV), dins dels seus respectius programes de doctorat en 'Enginyeria d'Innovació Tecnològica' i ' Matemàtiques '. Aquests programes estan estretament vinculats a través del tòpic MCDM, ja que proporciona eines crucials per gestionar el manteniment de sistemes complexos reals utilitzant anàlisis matemàtics profunds. El propòsit d'aquesta sinergia és tenir en compte de forma sòlida la incertesa en atribuir avaluacions subjectius, recopilar i sintetitzar judicis atribuïts per diversos responsables de la presa de decisions, i tractar amb conjunts grans d'aquests elements en els problemes plantejats. El tema principal del present treball de doctorat es la gestió de les activitats de manteniment per augmentar els nivells d'innovació tecnològica i el rendiment dels sistemes complexos. Qualsevol sistema pot ser considerat objecte d'estudi, inclosos els sistemes de producció i els de prestació de serveis, entre d'altres, mitjançant l'avaluació dels seus contextos reals. Aquesta tesi doctoral proposa afrontar la gestió del manteniment mitjançant el desenvolupament de tres línies principals d'investigació estretament vinculades. ¿ La primera és el nucli, i il·lustra la majoria dels aspectes metodològics de la tesi. Es refereix a l'ús de mètodes MCDM per donar suport a decisions estratègiques de manteniment, i per fer front a la incertesa que afecta les dades/avaluacions, fins i tot quan estan involucrats diversos responsables (experts en manteniment) en la presa de decisions. ¿ La segona línia desenvolupa anàlisis de fiabilitat per a sistemes complexos reals (també en termes de fiabilitat humana) sobre la qual base s'ha d'implementar qualsevol activitat de manteniment. Aquestes anàlisis consideren la configuració de fiabilitat dels components del sistema en estudi i les característiques específiques de l'entorn operatiu. ¿ La tercera línia d'investigació aborda aspectes metodològics importants de la gestió de manteniment i emfatitza la necessitat de monitoritzar el funcionament de les activitats de manteniment i d'avaluar la seva efectivitat utilitzant indicadors adequats. S'ha elaborat una àmplia gamma de casos d'estudi del món real per avaluar l'eficàcia dels mètodes MCDM en el manteniment i així provar la utilitat de l'enfocament proposat.[EN] This doctoral thesis proposes using multi-criteria decision making (MCDM) methods as a strategic tool to support maintenance management of complex systems. The development of this doctoral thesis is framed within a cotutelle (co-tutoring) agreement between the Università degli Studi di Palermo (UNIPA) and the Universitat Politècnica de València (UPV), within their respective programmes of doctorates in 'Technological Innovation Engineering' and 'Mathematics'. Regarding this thesis, these programmes are closely linked through the topic of MCDM, providing crucial tools to manage maintenance of real complex systems by applying in-depth mathematical analyses. The purpose of this connection is to robustly take into account uncertainty in attributing subjective evaluations, collecting and synthetizing judgments attributed by various decision makers, and dealing with large sets of elements characterising the faced issue. The main topic of the present doctoral work is the management of maintenance activities to increase the levels of technological innovation and performance of the analysed complex systems. All kinds of systems can be considered as objects of study, including production systems and service delivery systems, among others, by evaluating their real contexts. Thus, this doctoral thesis proposes facing maintenance management through the development of three tightly linked main research lines. ¿ The first is the core and illustrates most of the methodological aspects of the thesis. It refers to the use of MCDM methods for supporting strategic maintenance decisions, and dealing with uncertainty affecting data/evaluations even when several decision makers are involved (experts in maintenance). ¿ The second line develops reliability analyses for real complex systems (also in terms of human reliability analysis) on the basis of which any maintenance activity must be implemented. These analyses are approached by considering the reliability configuration of both the components belonging to the system under study and the specific features of the operational environment. ¿ The third research line focuses on important methodological aspects to support maintenance management, and emphasises the need to monitor the performance of maintenance activities and evaluate their effectiveness using suitable indicators. A wide range of real real-world case studies has been faced to evaluate the effectiveness of MCDM methods in maintenance and then prove the usefulness of the proposed approach.Carpitella, S. (2019). Multi-criteria decision methods to support the maintenance management of complex systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11911

Unveiling and unraveling aggregation and dispersion fallacies in group MCDM

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics

An information model for lean, agile, resilient and green supply chain management

Dissertação para a obtenção de Grau de Mestre em Engenharia e Gestão IndustrialIn modern business environments, an effective Supply Chain Management (SCM) is crucial to business continuity. In this context, Lean, Agile, Resilient and Green (LARG), are advocated as the fundamental paradigm for a competitive Supply Chain (SC) as a whole. In fact, competition between supply chains (SC) has replaced the traditional competition between companies. To make a supply chain more competitive, capable of responding to the demands of customers with agility, and capable of responding effectively to unexpected disturbance, in conjugation with environmental responsibilities, and the necessity to eliminate processes that add no value, companies must implement a set of LARG SCM practices and Key Performance Indicators (KPI) to measure their influence on the SC performance. However, the selection of the best LARG SCM practices and KPIs is a complex decision-making problem, involving dependencies and feedbacks. Still, any decision-making must be supported by real and transparent data. This dissertation intends to provide two integrated models to assist the information management and decision-making. The first is an information model to support a LARG SCM, allowing the exchange and storage of data/information through a single information platform. In this model three types of diagrams are developed, Business Process Diagram (BPD), Use Cases Diagram and Class Diagram to assist the information platform design. The second is a decision-making model, designated LARG Analytical Network Process (ANP) to select the best LARG SCM practices/KPI to be implemented in SCs. Both models are developed and validated within the automotive SC, namely in Volkswagen Autoeuropa

A contribution to supply chain design under uncertainty

Dans le contexte actuel des chaînes logistiques, des processus d'affaires complexes et des partenaires étendus, plusieurs facteurs peuvent augmenter les chances de perturbations dans les chaînes logistiques, telles que les pertes de clients en raison de l'intensification de la concurrence, la pénurie de l'offre en raison de l'incertitude des approvisionnements, la gestion d'un grand nombre de partenaires, les défaillances et les pannes imprévisibles, etc. Prévoir et répondre aux changements qui touchent les chaînes logistiques exigent parfois de composer avec des incertitudes et des informations incomplètes. Chaque entité de la chaîne doit être choisie de façon efficace afin de réduire autant que possible les facteurs de perturbations. Configurer des chaînes logistiques efficientes peut garantir la continuité des activités de la chaîne en dépit de la présence d'événements perturbateurs. L'objectif principal de cette thèse est la conception de chaînes logistiques qui résistent aux perturbations par le biais de modèles de sélection d'acteurs fiables. Les modèles proposés permettent de réduire la vulnérabilité aux perturbations qui peuvent aV, oir un impact sur la continuité des opérations des entités de la chaîne, soient les fournisseurs, les sites de production et les sites de distribution. Le manuscrit de cette thèse s'articule autour de trois principaux chapitres: 1 - Construction d'un modèle multi-objectifs de sélection d'acteurs fiables pour la conception de chaînes logistiques en mesure de résister aux perturbations. 2 - Examen des différents concepts et des types de risques liés aux chaînes logistiques ainsi qu'une présentation d'une approche pour quantifier le risque. 3 - Développement d'un modèle d'optimisation de la fiabilité afin de réduire la vulnérabilité aux perturbations des chaînes logistiques sous l'incertitude de la sollicitation et de l'offre

Reasoning of Competitive Non-Functional Requirements in Agent-Based Models

During the decision-making process in real-time competitive environments, there is a need to perform concurrent optimisation of multiple competitive objectives to select an optimal design decision for interdependent stakeholders. To handle such issues, this thesis successfully assimilates the goal-oriented requirements-engineering knowledge with analytical decision-making approaches to facilitate reasoning and analysis by encouraging stakeholders’ involvement. This leads to optimal decisions with domain knowledge improvement in the agent-based i*-goal model by balancing multiple conflicting non-functional requirements reciprocally