Nesta tese propõe-se um método heurístico para o problema de Projeto de Rede da Cadeia de Suprimentos (Supply Chain Network Design) considerando vários aspectos de relevância prática, tais como: fornecedores e matérias-primas, localização e operação de instalações, atribuição de Centros de Distribuição (CD), e grande número de clientes e produtos. Uma eficiente abordagem heurística de duas fases é proposta para a obtenção de soluções viáveis para os problemas, que inicialmente é modelado como um Programa Linear Inteiro Misto (PLIM) de grande escala. Na fase de construção, uma estratégia de Linear Programming Rounding é aplicada para se obter os valores iniciais para as variáveis de localização inteira do modelo. Simultaneamente, um método Multi-start foi desenvolvido para gerar soluções iniciais diversificadas para cada nova iteração da heurística de Rounding. Na segunda fase, dois procedimentos de Busca Local foram desenvolvidos no sentido de melhorar a solução fornecida pelo método de Rounding. Implementamos duas diferentes abordagens de Busca Local: remoção-inserção e troca. Uma técnica de Busca Tabu para orientar o procedimento de Busca Local para explorar os diferentes espaços de soluções foi desenvolvida. As formulações e algoritmos foram implementados na linguagem C++ utilizando ferramentas de otimização da COIN-OR. O método de solução foi experimentado em instâncias geradas aleatoriamente, com tamanhos diferentes em termos do número de parâmetros, tais como o número de produtos, zonas de clientes, CDs e fábricas considerando um sistema logístico de quatro níveis. As implementações computacionais mostram que o método de solução proposto obteve resultados satisfatórios quando comparados com a literatura. Para validar este método heurístico também foi usado em um caso realista, com base em dados de uma empresa de borracha que está reestruturando sua cadeia de suprimentos devido ao projeto de uma nova uma nova fábrica e produção de novos produtos. A abordagem heurística proposta revelou-se adequada para aplicação prática em um caso real de uma indústria multicommodity em um contexto determinístico. / In this thesis we propose a heuristic method for the Supply Chain Network Design (SCND) problem considering several aspects of practical relevance: suppliers and raw materials, location and operation facilities, distribution center (DC) assignments, and large numbers of customers and products. An efficient two-phase heuristic approach is proposed for obtaining feasible solutions to the problems, which is initially modeled as a large-scale Mixed Integer Linear Program (MILP). In the construction phase, a linear programming rounding strategy is applied to obtain initial values for the integer location variables in the model. Simultaneously, a Multi-start method was developed to generate diversified initial solutions from each new iteration in the rounding heuristic. In the second phase, two Local Search procedures were developed towards to improve the solution provided by the rounding method. We implemented two different Local Search approaches: removal-insertion and exchange. A Tabu Search technique was developed to guide the Local Search procedure to explore the different spaces of solutions. The formulations and algorithms were implemented in C++ code language using the optimization engine COIN-OR. The solution method was experimented in randomly generated instances, with different sizes in terms of the number of parameters, such as number of products, customer zones, DCs, and factories considering a four-echelon logistic system. The computational implementations show that the solution method proposed obtained satisfactory results when compared to the literature review. To validate this heuristic method was also used in a realistic case, based on data from a rubber company that is restructuring its supply chain due to the overture of a new factory, producing new products. The proposed heuristic approach proved appropriate to practical application in a realistic case of a multi commodity industry in a deterministic context.

Nesse trabalho apresentamos a modelagem matemática do Projeto de Rede da Cadeia de Suprimentos (Supply Chain Network Design) de uma empresa responsável pela produção e distribuição de commodities. O investimento em uma nova fábrica exige que a empresa tenha seu projeto logístico readequado. O desenvolvimento das formulações matemáticas é baseado no trabalho de Jayaraman e Pirkul (2001). Foram realizadas 3 (três) formulações matemáticas de diferentes abordagens para solução do projeto logístico. As implementações computacionais foram realizadas no CPLEX© Optimization Studio Academic Research versão 12.2. As experiências computacionais foram desenvolvidas através de cenários que buscaram representar problemas de dimensões reais. Ainda na etapa de experimentação foram realizadas análises de sensibilidade dos modelos. Um estudo para identificar as demandas de mercado e os custos de transporte foi realizado, sendo assim, foi possível aplicar os modelos no estudo de caso a partir de dados reais de demanda e custos. Para execução das instâncias reais foram selecionadas 2 (duas) abordagens distintas: Single-source e Arcbased. Diferentes market shares foram definidos para construção de novos cenários e instâncias reais foram executadas dentro de limites computacionais de tempo satisfatórios. Os resultados encontrados para cada cenário foram comparados e analisados em relação à resolução do problema proposto. A dissertação contribui para a área de modelagem da cadeia de suprimentos fornecendo modelos que apresentaram resultados satisfatórios para a tomada de decisão nessa fase do projeto logístico da indústria tema dessa pesquisa. / We present a mathematical modeling of the Supply Chain Network Design (SCND) for a company responsible for producing and distributing commodities. Investment in a new plant requires that the company has reconfigured its logistics project. The development of mathematical formulations is based on the work of Jayaraman and Pirkul (2001). Were performed three (3) mathematical formulations for different approaches to solving the logistics project. Computational implementations were done in CPLEX© Optimization Academic Research Studio version 12.2. Computational experiments were developed through scenarios that sought to represent problems of the real dimensions. Still in the experimentation fase were performed sensitivity analysis of models. A study to identify market demands and transportation costs was made, so the models could be applied in the case study data from actual demand and costs. For carrying out the actual instances were selected 2 (two) different approaches: Single-source-based and Arc. Different market shares were set to build new scenarios and actual instances have been implemented within limits satisfactory computational time. The results for each scenario were compared and analyzed for solving the proposed problem. The thesis contributes to modeling area of supply chain models that provide satisfactory results for decision making in this phase of the project's logistics industry theme of this research.

D. Tech. (Logistics management, Faculty of Management Science), Vaal University of Technology / Nowadays, the business environment is characterised by faster technological development, customer satisfaction, shorter product life cycles, and more intense global competition. This new competitive landscape has forced firms to acquire new ways to achieve competitive advantage actively since a firm's competitive advantage is now dependent on operating efficiency and productivity across functional areas of the firm. The most successful manufacturers seem to be those that have carefully linked their internal processes to external suppliers and customers in unique supply chains. The manufacturing sector represents 15 per cent of gross domestic product (GDP) of the South African commercial industry. Competitive challenges in the modern business environment have resulted in the need for firms to collaborate their business processes strategically across other business units within the supply chain. Supply chain network theory, dynamic capabilities theory, and the resource-based view theory are discussed to better understand the importance of firms' supply chain capabilities, supply chain agility, and firm performance. The primary objective of this research study was to investigate the association between supply chain capabilities (supply chain network design, supply chain information competency, and supply chain integration) and supply chain agility on firm performance in the manufacturing sector of the Gauteng province in South Africa. It also aimed to ascertain the kind of relationships between supply chain network with supply chain agility, supply chain network with firm performance, supply chain information competency with supply chain agility, supply chain integration with supply chain agility, supply chain integration with firm performance, and finally supply chain agility with firm performance. Structured questionnaires were distributed to various manufacturing firms in the Gauteng province. The data were collected from manufacturing firm managers and owners. SPSS 25.0 was used to analyse the data, and AMOS 25.0 used to perform the confirmatory factor analysis (CFA) and path analysis. The structural equation modelling (SEM) was used to assess the proposed model fit and to test the statistically significant relationship between the various hypotheses. The research study results revealed that supply chain technologies and supply chain vi agility positively influence firm manufacturing performance. This study contributes new knowledge to the existing literature by providing a research framework that can enhance manufacturing firms' performance and also offer practical recommendations based on the research findings for manufacturing firms and future research. Furthermore, as one of the first studies to address the association between supply chain capabilities and supply chain agility on firm performance in the Gauteng province, this study generates new insights and information, as well as outlines the strategic reasons for manufacturing firms' managers and owners to improve on their organisational relationships. Hence, the study found that firms have realised significant supply chain coordination through supply chain capabilities and supply chain agility and that firms have been able to attain strategic goals, reduce risks and improve internal and external coordination of operational processes. Keywords: supply chain network design; supply chain information competency; supply chain integration; supply chain agility; firm performance.

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Tesis por compendio / [ES] Los sistemas tradicionales de planificación y control de la producción (PPC) se centran en producir lo que demanda el mercado, con la calidad, el calendario y los volúmenes previstos al mínimo coste, ajustándose al mismo tiempo a las disrupciones de la cadena de suministro. La exploración e implementación de nuevos avances tecnológicos en el marco de la industria 4.0 (I4.0), como sistemas ciberfísicos (CPS), fabricación en la nube (CMfg), fabricación aditiva (AM), big data, inteligencia artificial y la Internet de las cosas (IoT), podrían cambiar aspectos organizativos tales como las responsabilidades de PPC. En este contexto, no se identificaron estudios sobre un sistema para la toma de decisiones, arquitecturas y marcos conceptuales para los nuevos sistemas inteligentes de PPC e I4.0. En este contexto de nuevos cambios tecnológicos y organizativos a los que tienen que hacer frente las pequeñas y medianas empresas (PYMEs), surge el problema de diseñar herramientas de PPC que permitan la integración y colaboración de las operaciones de producción. Así, basándose en las nuevas tecnologías de producción digital y en las herramientas organizativas que darán soporte a las fábricas inteligentes conectadas del futuro, se identificó la falta de un sistema integrado de PPC e I4.0. Esta tesis doctoral es un compendio de artículos que abordan una amplia revisión bibliográfica sobre la PPC en un entorno de I4.0. También, se propone un marco conceptual y el diseño de modelos y algoritmos para la toma de decisiones y dar soporte a las funciones de PPC en un contexto digital I4.0 basado en las nuevas tecnologías de producción digital y herramientas organizativas que darán soporte a las fábricas inteligentes colaborativas y conectadas del futuro. Los modelos matemáticos y algoritmos propuestos se centran en resolver el problema del diseño y planificación de una cadena de suministro sostenible y resiliente en la que las decisiones estratégicas y tácticas se toman de forma integrada. Los modelos, algoritmos y método de resolución se han programado en Python. Los modelos han sido validados mediante un software que genera instancias de datos sintéticos y permite evaluar la complejidad computacional de los mismos. El desarrollo de este tipo de modelos y algoritmos supone una contribución al ámbito académico e investigador y, concretamente, en el área de PPC. / [CA] En l'actualitat, els sistemes tradicionals de planificació i control de la producció (PPC) se centren en produir el que demanda el mercat, amb la qualitat, el calendari i els volums previstos al mínim cost, ajustant-se al mateix temps a les pertorbacions. L'exploració i implementació de nous avanços tecnològics, com CPS, fabricació en el núvol (CMfg), fabricació additiva (AM), big data, intelligència artificial i el IoT, podrien canviar aspectes organitzatius, com les responsabilitats de PPC. En aquest context, no es van identificar estudis sobre un sistema per a la presa de decisions, arquitectures i marcs conceptuals per als nous sistemes intelligents de PPC i I4.0. En aquest context de nous canvis tecnològics i organitzatius als quals han de fer front les petites i mitjanes empreses (PIME), sorgeix el problema de dissenyar eines de PPC que permeten la integració i collaboració de les operacions de producció. Així, basant-se en les noves tecnologies de producció digital i en les eines organitzatives que donaran suport a les fàbriques intelligents connectades del futur, es va identificar la falta d'un sistema integrat de la PPC i I4.0. Aquesta investigació és un compendi d'articles que aborden una àmplia revisió bibliogràfica sobre la PPC en un entorn I4.0. També proposa un marc conceptual i el disseny de models i algorismes per a la presa de decisions i per a donar suport a les funcions de PPC en un context digital I4.0 basat en les noves tecnologies de producció digital i eines organitzatives que donaran suport a les fàbriques intelligents col·laboratives i connectades del futur. Els models matemàtics i algorismes proposats se centren en resoldre el problema del disseny d'una cadena de subministrament sostenible i resistent en la qual les decisions estratègiques i tàctiques es prenen de forma integrada. Els models, algorismes i mètode de resolució s'han programat en Python. Els models han sigut validats mitjançant un programari que genera instàncies de dades sintètiques i permet avaluar la complexitat computacional dels models. El desenvolupament d'aquesta mena de models i algorismes suposa una important contribució a l'àmbit acadèmic. / [EN] Currently, traditional production planning and control (PPC) systems focus on producing what the market demands with the expected quality, schedule and volumes at a minimum cost, while adjusting for disruption. The exploration and implementation of new technological advances, such as CPS, cloud manufacturing (CMfg), additive manufacturing (AM), big data, artificial intelligence and the Internet of Things (IoT), could change organisational aspects like PPC responsibilities. In this context, no studies on a system for decision making, architectures and conceptual frameworks for the new intelligent systems of PPC and industry 4.0 (I4.0) have been identified. In this context of new technological and organisational changes that small-and medium-sized enterprises (SMEs) have to face, the problem of designing PPC tools that enable the integration and collaboration of production operations arises. Thus, based on the new digital production technologies and organisational tools that will support the connected smart factories of the future, lack of an integrated PPC and I4.0 system was identified. The present doctoral thesis is a compendium of articles addressing a comprehensive literature review on PPC in an I4.0 environment. It also proposes a conceptual framework and the design of models and algorithms for decision making and to support PPC functions in a digital I4.0 context based on the new digital production technologies and organisational tools that will support the collaborative and connected smart factories of the future. The proposed mathematical models and algorithms focus on solving the problem of designing a sustainable and resilient supply chain where strategic and tactical decisions are made in an integrated way. The models, algorithms and resolution method have been programmed in Python. The models have been validated by means of software that generates synthetic data instances and allows the models' computational complexity to be evaluated. The development of this type of models and algorithms is a significant contribution to the academic field. / I would like to thank the following projects and universities for having financed the publications included in this doctoral thesis: • European Commission Horizon 2020 project entitled "Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003. • Spanish Ministry of Science, Innovation and Universities project entitled 'Optimization of zero-defect production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00). • European Union H2020 program with grant agreement no. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)". • European Union H2020 Program with grant agreement nº 825631 "Zero- Defect Manufacturing Platform (ZDMP)". / Cañas Sánchez, HE. (2023). Models, Algorithms and Digital Technologies for the Automation and Collaboration of Connected Smart Factories in an Industry 4.0 Environment [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202312 / Compendio