O aumento da variabilidade da demanda ao longo de uma cadeia de abastecimento é conhecido como Efeito Chicote (EC). A modelagem deste fenômeno é fundamental para a quantificação de sua intensidade, ajudando a reduzir seus impactos negativos sobre o nível de serviço e sobre os estoques em uma cadeia de abastecimento. Esta tese apresenta uma proposta de modelagem do EC que tem por objetivo aumentar a precisão na quantificação deste fenômeno em ambientes com demanda e lead time estocásticos. O novo modelo considera dois elementos que não estão presentes nos principais modelos disponíveis na literatura: a variabilidade no lead time de entrega de pedidos e a incorporação de um ajuste para contemplar uma política adequada de tratamento dos excessos de estoque. Além disso, define de modo mais preciso o papel do coeficiente de variação da demanda na quantificação do EC. A utilização do modelo proposto aumenta a eficiência da gestão de cadeias de abastecimento ao contribuir para atenuar a propagação do EC, elevar o nível de serviço e reduzir os níveis local e global dos estoques. Neste documento, os principais modelos de quantificação do EC são apresentados e analisados, com destaque para os trabalhos de Lee et al. (1997b), Chen et al. (2000), Fransoo e Wouters (2000) e Warburton (2004); nessa análise foram identificadas várias deficiências, capazes de produzir fortes distorções no processo de quantificação do EC. O modelo proposto supre integralmente estas deficiências e apresenta elementos que indicam que a intensidade e o comportamento estocástico e serial do EC só podem ser adequadamente modelados se a variabilidade do lead time for considerada e se os excessos de estoque forem utilizados no cálculo do tamanho dos pedidos. O novo modelo, além de contribuir para o entendimento da dinâmica do EC e para a ampliação do respectivo campo de discussão, representa adequadamente a complexidade das relações entre as variáveis associadas ao EC, o que lhe confere alta capacidade preditiva. Complementarmente, demonstra-se que o modelo de Chen et al. (2000) constitui um caso particular do modelo proposto. / The increase in demand variability as information flows from customers to manufacturers in a supply chain is known as the Bullwhip Effect (BE). Modeling this phenomenon is fundamental in measuring its intensity, aiming at reducing its negative impacts on both service and inventory levels in the supply chain. In this dissertation we propose a new, more precise mathematical model for quantifying the BE in systems with stochastic demand and lead time. The new model takes into account the lead time variability and is adjusted to a more realistic treatment of negative order quantities that may arise in some inventory cycles, two elements not present in the main available models in the literature. In addition, the model enables a more precise assessment of the role that the demand coefficient of variation plays in the quantification of the BE. The use of the proposed model enables an improved management of the supply chain by attenuating the propagation of the BE, increasing the service level and reducing inventory levels both locally and globally. In this dissertation, the main models for quantifying the BE are presented and analyzed, with emphasis in the works of Lee et al. (1997b), Chen et al. (2000), Fransoo and Wouters (2000) and Warburton (2004); in that analysis were identified several deficiencies, able to generate severe distortions in the quantification of the BE. The proposed model fully overcomes these deficiencies and presents elements that indicate that the intensity and stochastical and serial behavior of the BE can only be appropriately modeled if the lead time variability is considered and if inventory excesses are used in the order size calculation. The new model, in addition to contribute to the understanding of the BE dynamics enriching its analysis, represents appropriately the complexity of relationships among variables associated with the BE, contributing to its high predictive capacity. Finally, it is demonstrated that the model in Chen et al. (2000) represents a special case of the proposed model.

The implicit assumption in conventional inventory models is that the stored products maintain the same utility forever, i.e., they can be stored for an infinite period of time without losing their value or characteristics. However, generally speaking, almost all products experience some sort of deterioration over time. Some products have very small deterioration rates, and henceforth the effect of such deterioration can be neglected. Some products may be subject to significant rates of deterioration. Fruits, vegetables, drugs, alcohol and radioactive materials are examples that can experience significant deterioration during storage. Therefore the effect of deterioration must be explicitly taken into account in developing inventory models for such products. In most existing deteriorating inventory models, time is treated as a continuous variable, which is not exactly the case in practice. In real-life problems time factor is always measured on a discrete scale only, i.e. in terms of complete units of days, weeks, etc. In this research, we present several discrete-in-time inventory models and identify optimal ordering policies for a single deteriorating product by minimizing the expected overall costs over the planning horizon. The various conditions have been considered, e.g. periodic review, time-varying deterioration rate, waiting-time-dependent partial backlogging, time-dependent demand, stochastic demand etc. The objective of our research is two-fold: (a) To obtain optimal order quantity and useful insights for the inventory control of a single deteriorating product over a discrete time horizon with deterministic demand, variable deterioration rates and waiting-time-dependent partial backlogging ratios; (b) To identify optimal ordering policy for a single deteriorating product over a finite horizon with stochastic demand and partial backlogging. The explicit ordering policy will be developed for some special cases. Through computational experiments and sensitivity analysis, a thorough and insightful understanding of deteriorating inventory management will be achieved.

Le premier chapitre est une présentation des principaux concepts et résultatsconcernant la finance d'entreprise à la lumière de certains développementsrécents de l'économie du travail.Le deuxième chapitre vise à établir les propriétés d'optimalité concernantl'investissement et l'embauche d'une entreprise dans le cadre de lamaximisation d'une utilité linéaire.Le troisième chapitre traite de la problématique (éventuelle) du désinvestissementet du licenciement. Nous étudions en particulier les problèmesde la prise de décision optimale du dirigeant faisant face soit à une croissancedu marché, soit au contraire à une chute de la demande pour son produit.Le quatrième chapitre reconsidère la question en prenant en compte spécifiquementd'une borne supérieure sur la quantité pouvant être réellementvendue.Le cinquième chapitre prend en compte le phénomènes possibles de retourà la moyenne du prix unitaire du produit vendu.Le sixième et dernier chapitre reconsidère les problèmes de décision optimalepour différentes formes de dette possibles. / The first chapter is an overview of the main concepts and resultson corporate finance in the light of certain developmentsrecent labor economics .The second chapter aims to establish the optimal properties forinvestment and hiring a company under themaximizing a linear utility .The third chapter deals with the problem (if any) divestmentand firing . Nosu study particular problemsthe optimal decision of the leader facing either growthmarket , on the contrary to a drop in demand for its product.The fourth chapter reconsiders the issue , taking into account specifican upper bound on the amount that can actually besold.The fifth chapter considers the possible phenomena of retuthe average unit price of the product sold .The sixth and final chapter reconsiders the problems of optimal decisionfor different possible forms of debt

To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to-order (MTO) systems is eliminating inventories and reducing the non-value-added processes and wastes; however, these systems are based on the assumption that the manufacturing environments and customers' demand are deterministic. Research shows that in MTO systems variability and uncertainty in the demand levels causes instability in the production flow, resulting in congestion in the production flow, long lead times, and low throughput. Neither strategy is wholly satisfactory. A new alternative approach, multi-channel manufacturing (MCM) systems are designed to manage uncertainties and variabilities in demands by first focusing on customers' response time. The products are divided into different product families, each with its own manufacturing stream or sub-factory. MCM also allocates the production capacity needed in each sub-factory to produce each product family. In this research, the performance of an MCM system is studied by implementing MCM in a real case scenario from textile industry modeled via discrete event simulation. MTS and MTO systems are implemented for the same case scenario and the results are studied and compared. The variables of interest for this research are the throughput of products, the level of on-time deliveries, and the inventory level. The results conducted from the simulation experiments favor the simulated MCM system for all mentioned criteria. Further research activities, such as applying MCM to different manufacturing contexts, is highly recommended.

No description available.

No description available.

This thesis presents a comprehensive investigation into the performance and generalizability of optimization approaches for the single-echelon (r, Q) inventory management policy under stochastic demand, specifically focusing on demand characterized by a Poisson distribution. The research integrates both classical optimization techniques and advanced metaheuristic methods, with a particular emphasis on Genetic Programming (GP), to assess the effectiveness of various heuristics. The study systematically compares the performance of these approaches in terms of both accuracy and computational efficiency using two well-known datasets. To rigorously evaluate the generalizability of the heuristics, an extensive random dataset of 10,000 instances, drawn from a vast population of approximately 24 billion instances, was generated and employed in this study. Our findings reveal that the exact solution provided by the Federgruen-Zheng Algorithm consistently outperforms hybrid heuristics in terms of computational efficiency, confirming its reliability in smaller datasets where precise solutions are critical. Additionally, the extended Cooperative Coevolutionary Genetic Programming (eCCGP) heuristic proposed by Lopes et al. emerges as the most efficient in terms of runtime, achieving a remarkable balance between speed and accuracy, with an optimality error gap of only 1%. This performance makes the eCCGP heuristic particularly suitable for real-time inventory management systems, especially in scenarios involving large datasets where computational speed is paramount. The implications of this study are significant for both theoretical research and practical applications, suggesting that while exact solution, i.e., the Federgruen-Zheng Algorithm is ideal for smaller datasets, the eCCGP heuristic provides a scalable and efficient alternative for larger, more complex datasets without substantial sacrifices in accuracy. These insights contribute to the ongoing development of more effective inventory management strategies in environments characterized by stochastic demand. / Thesis / Master of Science (MSc)

Tato disertační práce se zabývá vývojem, modelováním a analýzou poptávkově orientovaných úloh, které zahrnují marketingová, operační a logistická rozhodnutí. Úlohy jsou zvoleny tak, aby mohly být dále rozšířeny o koncept tzv. dynamického oceňování a jiných dynamických marketingových rozhodnutí. V práci jsou využity dvě základní poptávkově orientované úlohy: a) úloha kolportéra novin, která je zvolena pro její jednoduchou formu a která tak slouží jako nástroj pro ilustrativní ukázky rozhodovacích procesů v podobných typech úloh, a b) úloha návrhu dopravní sítě, kde jsou využity některé výsledky a znalosti získané při řešení úlohy kolportéra novin. Kolportér (či obecně maloobchodník) čelí náhodné poptávce, která může být postupně ovlivněna oceňováním, marketingovými (tj. reklamními) rozhodnutími a nakonec jejich kombinací. Poptávka obsahuje tedy náhodnou složku, která je pomocí přístupů stochastické optimalizace modelována ve specifickém tvaru (tj. aditivní či multiplikativní tvar). Závislost cena-poptávka je zachycena pomocí nelineární klesající poptávkové funkce, zatímco (vhodná) reklama vede ke zvýšení poptávky (běžně rostoucí s-křivka či konkávní funkce). Výsledky získané při řešení úlohy kolportéra novin s oceňováním jsou následně využity v úloze návrhu dopravní sítě. Tato stochastická úloha je modelována (reformulována) pomocí dvou přístupů stochastické optimalizace: wait-and-see přístup a here-and-now přístup. Jelikož tato implementace vede na lineární či nelineární celočíselnou (navíc scénářovou) úlohu, jsou v práci zmíněny taky výpočetní nástroje. Autor pro řešení používá (původní) tzv. hybridní algoritmus, což je kombinace heuristického (genetického) algoritmu a nástroje optimalizačního softwaru. Potenciální aplikace sestavených modelů, obzvláště v oblasti odpadového hospodářství, jsou diskutovány v závěrečné části disertační práce.

Les problèmes de localisation-routage à deux échelons (2E-LRP) sont devenus un domaine de recherche important dans le domaine de la logistique et de la gestion de la chaîne d'approvisionnement. Le 2E-LRP représente un problème d'optimisation dans les systèmes de distribution non dirigés, visant à organiser le transport de marchandises entre les plateformes et les clients par le biais d'installations intermédiaires appelées satellites. Ce problème implique de prendre des décisions simultanées concernant l'emplacement d'un ou deux niveaux d'installations (plateformes et/ou satellites) et de créer un ensemble limité d'itinéraires aux deux échelons afin de répondre efficacement à toutes les demandes des clients. Récemment, la communauté scientifique s'est intéressée de plus en plus à l'étude et à la résolution de problèmes plus réalistes. Cet intérêt provient de la reconnaissance du fait que les systèmes de distribution du monde réel sont caractérisés par une multitude de complexités et d'incertitudes qui ont un impact significatif sur l'efficacité opérationnelle, la rentabilité et la satisfaction des clients. Les chercheurs ont reconnu la nécessité d'aborder ces complexités et incertitudes pour développer des solutions pratiques et efficaces. Cette thèse comprend trois études différentes, chacune correspondant à un article de recherche autonome. Dans les trois articles, nous nous concentrons sur différents 2E-LRP riches qui comprennent plusieurs attributs en interaction. Ces variantes du problème sont appelées problèmes de localisation-routage à deux échelons et à attributs multiples (2E-MALRP). Pour analyser l'influence des incertitudes sur les solutions optimales et les processus de prise de décision, nous considérons à la fois les perspectives déterministes et stochastiques. Cette approche nous permet de mieux comprendre le comportement de ces problèmes complexes. Le premier document de recherche abordé dans cette thèse se concentre sur un problème de localisation-routage déterministe à deux échelons et à attributs multiples avec synchronisation de la flotte dans les installations intermédiaires (2E-MALRPS). Le cadre du problème comprend divers facteurs, notamment la demande de marchandises multiples dépendant du temps, les fenêtres temporelles, le manque de capacité de stockage dans les installations intermédiaires et la nécessité de synchroniser les flottes opérant à différents échelons. Dans le 2E-MALRPS, tous les paramètres, tels que les demandes des clients, les temps de trajet et les coûts, sont connus avec certitude. Dans cet article, nous introduisons le cadre du problème, présentons une formulation de programmation en nombres entiers mixtes et proposons un cadre de découverte de discrétisation dynamique comme méthode de résolution du problème. Le deuxième article de cette thèse traite du problème de localisation-routage à deux échelons en cas de demandes stochastiques et corrélées (2E-MLRPSCD). Contrairement au 2E-MALRPS, le 2E-MLRPSCD prend en compte les incertitudes liées aux demandes des clients, ainsi que la corrélation entre ces demandes. Nous formulons le problème sous la forme d'un modèle de programmation stochastique en deux étapes. Au cours de la première étape, des décisions sont prises concernant la conception des installations satellites, tandis qu'au cours de la deuxième étape, des décisions de recours déterminent la manière dont les demandes observées sont servies. Nous proposons une métaheuristique de couverture progressive comme méthode de résolution. Dans cette approche, nous incorporons deux structures de population dans le cadre de la couverture progressive. Ces structures renforcent la diversité des décisions de conception obtenues pour chaque sous-problème de scénario et fournissent des informations pertinentes pour améliorer la qualité de la solution. En outre, nous introduisons et comparons trois nouvelles stratégies différentes pour accélérer la recherche de l'espace de solution pour le problème stochastique. Finalement, le troisième article présenté dans cette thèse se concentre sur un problème de localisation-routage multi-attributs à deux échelons avec des temps de trajet stochastiques (2E-MALRPSTT). Le 2E-MALRPSTT combine un problème multi-attributs riche avec des éléments stochastiques, en particulier en considérant des temps de trajet stochastiques. Pour traiter le problème stochastique complet, un cadre de couverture progressive (PH) est proposé en s'appuyant sur les lignes directrices méthodologiques définies dans notre deuxième article pour le 2E-MLRPSCD. En outre, une heuristique basée sur la décomposition est introduite pour accélérer le cadre PH, et deux nouvelles stratégies d'agrégation sont présentées pour accélérer le processus de consensus concernant les décisions de la première étape. Les contributions présentées dans cette thèse couvrent divers aspects de la modélisation et des méthodologies de solution pour les 2E-MALRP riches, à la fois d'un point de vue déterministe et d'un point de vue stochastique. Les trois articles inclus dans cette thèse démontrent l'efficacité des approches proposées à travers des campagnes expérimentales étendues, mettant en évidence leur efficacité de calcul et la qualité des solutions, en particulier dans les cas difficiles. En abordant les aspects déterministes et stochastiques de ces 2E-MALRP, cette thèse vise à contribuer à l'ensemble des connaissances en optimisation de la logistique et de la chaîne d'approvisionnement, à répondre aux besoins importants de la littérature actuelle et à fournir des informations importantes pour les systèmes de distribution à deux échelons dans divers contextes. / The Two-Echelon Location-Routing Problems (2E-LRPs) have emerged as a prominent research area within the field of logistics and supply chain management. The 2E-LRP represents an optimization problem in undirected distribution systems, aiming to streamline freight transportation between platforms and customers through intermediate facilities known as satellites. This problem involves making simultaneous decisions concerning the location of one or two levels of facilities (platforms and/or satellites) and creating a limited set of routes at both echelons to effectively serve all customer demands. In recent years, there has been a growing interest among the scientific community in studying and solving more realistic problem settings. This interest arises from the recognition that real-world distribution systems are characterized by a multitude of complexities and uncertainties that significantly impact operational efficiency, cost-effectiveness, and customer satisfaction. Researchers have acknowledged the need to address these complexities and uncertainties to develop practical and effective solutions. This dissertation comprises three distinct studies, each serving as a self-contained research article. In all three articles, we focus on different rich 2E-LRPs that encompass multiple interacting attributes. These problem variants are referred to as two-echelon multi-attribute location-routing problems (2E-MALRPs). To analyze the influence of uncertainties on optimal solutions and decision-making processes, we consider both deterministic and stochastic perspectives. This approach allows us to gain insights into the behavior of these complex problem settings. The first research paper addressed in this thesis focuses on a deterministic two-echelon multi-attribute location-routing problem with fleet synchronization at intermediate facilities (2E-MALRPS). The problem setting encompasses various factors, including time-dependent multicommodity demand, time windows, lack of storage capacity at intermediate facilities, and the need for synchronization of fleets operating at different echelons. In the 2E-MALRPS, all parameters, such as customer demands, travel times, and costs, are known with certainty. In this paper, we introduce the problem setting, present a mixed-integer programming formulation, and propose a dynamic discretization discovery framework as the solution method to address the problem. The second paper in this thesis addresses the two-echelon multicommodity location-routing problem with stochastic and correlated demands (2E-MLRPSCD). In contrast to the 2E-MALRPS, the 2E-MLRPSCD takes into account uncertainties related to customer demands, as well as the correlation among these demands. We formulate the problem as a two-stage stochastic programming model. In the first stage, decisions are made regarding the design of satellite facilities, while in the second stage, recourse decisions determine how the observed demands are allocated and served. We propose a progressive hedging metaheuristic as the solution method. In this approach, we incorporate two population structures within the progressive hedging framework. These structures enhance the diversity of the design decisions obtained for each scenario subproblem and provide valuable insights for improving the solution quality. Additionally, We also introduce and compare three different novel strategies to accelerate the search for the solution space for the stochastic problem. Finally, the third paper presented in this thesis focuses on a multi-attribute two-echelon location-routing problem with stochastic travel times (2E-MALRPSTT). The 2E-MALRPSTT combines a rich multi-attribute problem setting with stochastic elements, specifically considering stochastic travel times. To address the complete stochastic problem, a progressive hedging metaheuristic is proposed building on the methodological guidelines defined in our second paper for the 2E-MLRPSCD. Furthermore, a decomposition-based heuristic is introduced to accelerate the PH framework, and two novel selection strategies are presented to expedite the consensus process regarding the first-stage decisions. The contributions presented in this thesis encompass various aspects of modeling and solution methodologies for rich 2E-MALRPs from both deterministic and stochastic perspectives. The three articles included in this thesis demonstrate the effectiveness of the proposed approaches through extensive experimental campaigns, highlighting their computational efficiency and solution quality, particularly in challenging instances. By addressing the deterministic and stochastic aspects of these 2E-MALRPs, this thesis aims to contribute to the broader body of knowledge in logistics and supply chain optimization, fill important gaps in the present literature and provide valuable insights for two-echelon distribution systems in diverse settings.