O presente trabalho aborda o Problema de Coleta Domiciliar Urbana (PCDU) de resíduos sólidos, tratado no nível tático de planejamento, em que zonas de coleta são definidas para cada dia da semana e designadas aos veículos coletores, cuja frota deve ser dimensionada. O problema estudado é baseado em um caso real, o qual possui como particularidades: cada zona de coleta é formada por regiões adjacentes e será representada por um nó-semente; a demanda de cada zona deverá ser atendida dentro do período de uma semana, conforme múltiplos programas possíveis de coleta; em um turno de um dia de trabalho um veículo poderá realizar múltiplas viagens; e há uma garagem para a frota e uma estação de transbordo, a qual possibilita que o veículo seja esvaziado para realizar outras viagens. A literatura apresenta alguns métodos heurísticos para a resolução de variantes deste problema, sendo os métodos exatos utilizados somente na resolução de instâncias pequenas, dado que o problema de VRP (Vehicle Routing Problem) é classificado como NP-hard. A imposição de adjacência é uma característica particular, a qual é justificada pela possível melhoria na utilização dos veículos em posterior planejamento operacional. São propostos um modelo matemático e um método heurístico para resolver o problema, sobre os quais são realizados experimentos computacionais. O método heurístico é aplicado sobre um estudo de caso de um problema de escala real, sendo obtida solução heurística como resultado. / The present work addresses the Urban Household Solid Waste Problem (UHSWP) on a tactical planning level, wherein collection zones are assigned to every week daywork and collection vehicles, which fleet is to be sized. The studied problem is based on a real case, such peculiarities as: each collection zone is a set of adjacent areas and a seed node represents it; the demand each zone must attended within a week, according to the several possible collection schedule; on a work day shift a vehicle can be assigned to multiple trips; and there is a base depot for the fleet and a transfer station, where the vehicles are unloaded, restoring their load capacity for the next trips. Literature presents heuristic methods for the solving of its problem variants, in which exact methos are only applied to small instances, due to the VRP (Vehicle Routing Problem) NP-hard property. The adjacency imposition is a peculiar feature, which is justified by the potential improvement on vehicle usage considering a posterior operational planning. A mathematical model and a heuristic method are proposed for the problem solving and evaluated by computational experiments. A real scale problem case study is solved by the heuristic method and the results are presented.

[pt] O aumento expressivo no consumo de combustíveis no Brasil trouxe grandes desafios para a cadeia de suprimento no país. Ao longo dos últimos cinco anos (2009-2013), o mercado brasileiro de gasolina e diesel cresceu 62,8 porcento e 32,0 porcento, respectivamente. Porém, os investimentos em produção e infraestrutura não acompanharam este crescimento, tornando estes desafios cada vez maiores. Na região Nordeste do país, que é suprida majoritariamente por cabotagem, a infraestrutura para movimentação dos derivados de petróleo está aquém daquela necessária, trazendo ineficiências à cadeia de suprimentos e aumentando significativamente os custos envolvidos nas operações. Para avaliação do suprimento desta região, foi proposto um modelo de programação matemática que considera todas as restrições que impactam diretamente o suprimento de gasolina e diesel, identificando inclusive aquelas restrições que são ocasionadas por outros agentes da cadeia, como por exemplo, insuficiência de tancagem dos clientes, restrição de calados dos portos e elevadas taxas de ocupação dos portos públicos, aumentando os custos de sobrestadia. Para complementar a análise, alternativas para direcionamento de investimento dos distribuidores de forma eficiente, minimizando os custos da cadeia de suprimento, foram avaliadas. O modelo também foi utilizado na discussão dos níveis de serviço praticados pela Petrobras no atendimento da demanda de gasolina e diesel nos polos da região. O trabalho teve uma abordagem da cadeia de suprimento voltada para o planejamento tático, se mostrando como uma ferramenta eficiente para suporte à tomada de decisões. / [en] The significant increase of fuel consumption in Brazil has brought major challenges to the supply chain in the country. Over the last five years (2009- 2013), the Brazilian market for gasoline and diesel has increased 62.8 percent and 32.0 percent, respectively. However, investments in production and infrastructure have not kept up this growth, increasing the challenges. In the Northeast region of the country, which is supplied largely by coastal shipping, the infrastructure for the petroleum products movement falls short of what is needed bringing several inefficiencies to the supply chain and significantly increasing the costs involved in operations. To assess the supply of this region, a mathematical programming model was proposed which considers all the constraints that directly impact the supply gasoline and diesel, including identifying those constraints that are caused by other actors in the chain, such as insufficient costumer s tankage, draught restriction of ports and high occupancy rates of public ports, raising the cost of demurrage. To complement the analysis, alternative scenarios to allocate the distributor s investments efficiently were evaluated to minimize the costs of the supply chain. The proposed model was also used to discuss the service levels committed by Petrobras in meet the demand for gasoline and diesel at the region. The study had an approach to supply chain oriented for tactical planning, showing as an efficient tool to support decision making.

This thesis addresses two planning problems motivated by the operations of PSOD air transportation: scheduled maintenance planning, and base location and fleet allocation. In the first part of the thesis, we study tactical planning for scheduled maintenance which determines the daily maintenance capacities for two operating conditions: a growth phase and the steady state. We model tactical maintenance capacity planning during the growth phase as an integer program and develop an optimization-based local search to solve the problem. Tactical planning of steady state maintenance capacity concerns a special case for which we determine the optimal and the long run capacities with a pseudo-polynomial time algorithm. In the second part of the thesis, we address operational planning for scheduled maintenance which is concerned with assigning itineraries to jets and determining the specific jets to be scheduled for maintenance on a daily basis given a certain maintenance capacity. We present a solution methodology that employs a look-ahead approach to consider the impact of our current decisions on the future and decomposes the problem exploiting the differences between jets with respect to the proximity to their next maintenance. We further develop an integrated framework in order to capture the interaction between operational level maintenance decisions and flight scheduling. In the third and final part of the thesis, we present the tactical level base location and fleet allocation problem. As PSOD air transportation experiences changes in travel demand and fleet size, decisions regarding where to open new bases and how to allocate the number of jets among the bases are made. We first present a solution approach in which high level information about flight scheduling is used in a traditional facility location problem. We next develop a model that works directly with transportation requests and integrates a simplified version of flight scheduling with the base location and fleet allocation decisions in order to capture more detail.

Le présent travail s’intéresse à la prise en compte de l’incertitude et du risque pour l’optimisation de la planification de production au niveau tactique d’une entreprise multi-sites d’une chaîne logistique. La méthode proposée permet d’assurer une planification des opérations de production et d’approvisionnement tout en intégrant au sein de son processus décisionnel un mécanisme de gestion de risque, en présence de diverses sources d’incertitude et d’ambigüité. Pour cela, une «bibliothèque» de critères structurés en deux classes indépendantes : critères de performance et critères de risque a été proposée, dans laquelle le décideur peut sélectionner ceux qui sont en cohérence avec ses préférences et sa stratégie de planification. La méthode doit chercher le bon compromis entre les performances et les risques prédéfinis par le décideur. Pour cela, nous nous somme dirigés dans un premier temps sur le développement d’une approche d’aide à la décision multicritères floue couplant un modèle analytique et la méthode TOPSIS floue. Cette approche consiste à générer un éventail de plans réalisables, caractérisés par leur performance et leur résistance aux risques. Le décideur peut alors choisir le plan qui reflète le compromis le plus adapté à sa stratégie de décision. Une deuxième approche d’optimisation multi-objectifs floue a été proposée dans un deuxième temps pour faire face à des problèmes de planification de grande taille au sein des chaînes logistiques opérant dans un environnement dynamique et incertain. Cette approche combine la méthode TOPSIS Floue, la programmation multi-objectifs possibiliste et la méthode du Goal Programming. L’objectif est de déterminer un plan jugé de bon compromis vis-à- vis des préférences du décideur par rapport aux objectifs de performance et de résistance aux risques. L’instanciation des deux approches proposées sur un exemple numérique a montré leur applicabilité et leur efficacité pour faire face à des problèmes de planification des chaînes logistiques utilisant des données incertaines et des préférences subjectives. Les expérimentations des deux approches permettant de tirer un ensemble d’enseignements utiles. / The work reported in this dissertation deals with risk-oriented integrated procurement–production approaches for tactical planning in a multi-echelon supply chain network presenting various sources of uncertainty and ambiguity. The proposed method allows planning of production and supply operations while integrating a risk management mechanism into its decision-making process, in the presence of various sources of uncertainty and ambiguity. So, a library" of criteria structured into two independent classes: Performance-based and risk-based decision criteria were proposed, in which the decision-maker can select those that are consistent with his preferences and his planning strategy. The method must seek the right compromise between performance and risk predefined by the decision-maker. To reach this goal, we initially focused on the development of a fuzzy multi-criteria decision making approach coupling an analytical model and the fuzzy TOPSIS method. This approach generates a range of feasible plans, characterized by their performance and their resistance to risks. The decision-maker can then choose the plan that reflects the compromise that best suits its decision strategy. Afterwards, a fuzzy multi-objective optimization approach was proposed to deal with large-scale planning problems within supply chains operating in a dynamic and uncertain environment. This approach second combines the Fuzzy TOPSIS method, the possibilistic multi-objective programming and the Goal Programming method. The objective is to determine a plan that is judged to be a good compromise compared to the decision maker's preferences regarding the performance and risk objectives. The instantiation of the two proposed approaches on a numerical example has shown their applicability and tractability to deal with supply chain planning problems in the presence of uncertain data and subjective preferences. The experiments of the two approaches make it possible to draw a useful set of lessons. The experiments of the two approaches show a set of useful issues.

Ce travail s’intéresse à la planification tactique de chaînes logistiques dans un environnement incertain et perturbé. Dans le cadre de relations « point-à point », nous proposons une approche permettant d’élaborer une planification tactique optimale et réactive d’un maillon d’une chaîne logistique en présence de paramètres incertains et de perturbations. Notre approche se fonde sur une structure à deux niveaux décisionnels. Le premier niveau effectue une planification agrégée en minimisant le coût global de production. Il établit ensuite « un plan de guidage » qui est transmis au niveau détaillé. Ce dernier effectue sa planification en suivant « au mieux » le plan de guidage et en prenant en compte les contraintes et données détaillées ignorées au niveau supérieur. Le niveau détaillé adopte un processus dynamique de planification à horizon glissant. Il réactualise ses données à chaque étape de planification afin d’assurer la réactivité du processus décisionnel. Nous caractérisons explicitement l’inertie du système décisionnel en distinguant deux phases : la phase d’anticipation et la phase de réalisation. Chaque phase est caractérisée par un délai temporel. Ainsi, nous proposons une modélisation originale du processus décisionnel de chaque décision via trois variables. Le niveau détaillé est formulé selon un programme linéaire.Au niveau agrégé, nous proposons un modèle global ayant l’originalité de prendre en compte les spécificités du processus décisionnel détaillé.Le couplage entre les deux niveaux est assuré par le plan de guidage. Selon les informations incluses dans le plan de guidage, le niveau agrégé accorde un certain degré d’autonomie au niveau détaillé, ceci conditionne la réactivité et la robustesse de la planification. Dans notre travail, nous considérons trois types de guidage : deux guidages budgétaires « globaux » et un guidage « prescriptif » par la sous-traitance agrégée.Notre approche est évaluée par simulation dans le cadre d’une demande incertaine. Pour cela, nous développons deux outils de simulation et un ensemble d’indicateurs de performances. Les expérimentations réalisées confirment la performance de notre approche par rapport à des approches classiques et mettent en évidence l’influence du type de guidage et du profil de la demande détaillée sur la réactivité et la robustesse des solutions trouvées. / This work focuses on the supply chain tactical planning problem in an uncertain and disrupted environment. As part of point-to-point relationships, we propose an optimal and reactive tactical planning approach of a supply chain link in the presence of uncertain parameters and disturbances.Our approach is based on a two-level decision structure. The first level performs an aggregate planning which minimizes the overall production cost. It establishes "a guiding plan" which is transmitted to the detailed level. This latter performs its planning by following a guiding plan and by taking into account detailed constraints and data.The detailed level adopts a dynamic planning process with a rolling horizon. It updates its data at every planning step to ensure a reactive decision making. We characterize explicitly the inertia of a decision making system by distinguishing two decision phases: the anticipation phase and the realization phase. Each phase is described by a time delay. Thus, we propose an original model of decision making process in which every decision is modeled by three variables. The detailed level is formulated according to a linear program.At the aggregate level, a view of the detailed decisional process is integrated by work-in-progress constraints. We propose an aggregate model whose originality is to consider the specifics of the detailed decision process.The coupling between the two levels is provided by the guiding plan. According to aggregated data included in this plan, the aggregate level gives a specific autonomy to the detailed level which conditions the reactivity and the robustness of the detailed planning. In our work, we consider three types of guidance: two “global” budget guidings and a more “precise” subcontracting aggregate guiding.Our approach is evaluated by simulation under uncertain demand. For this we develop two simulation tools and a set of performance indicators. The experiments carried out confirm the performance of our approach over conventional approaches and highlight the influence of the guiding and the detailed demand profile on the reactivity and robustness of the solutions

All companies deal with tactical planning questions and decisions, for example balance demand and supply, to be able to create an acceptable delivery ability without too much inventory or resources/capacities. For that, some companies use Sales and Operations Planning (S&OP) as their tactical planning process. The ongoing customization wave applies to more and more products and there is a general displacement from standard products, manufactured to stock, towards more customized ones where the product is either assembled-, manufactured-, or engineered-to-order (ETO). This displacement brings an increased complexity into tactical planning questions and decisions, which might be new to a company and must be handled efficiently. The use of S&OP in an ETO context is, however, rarely documented. The possibility for companies to store large amounts of data and the availability of technologies such as Machine Learning (ML) to make predictions, opens up for an improved decision support for S&OP. ML models are normally trained with large datasets, and this is a challenge in an ETO context since there are normally small datasets to work with. Moreover, the use of ML in S&OP and ETO contexts are rarely documented. The purpose of this thesis is, thus, to explore where and how ML can be a useful tool for tactical planning, such as in S&OP in an ETO context. This thesis takes the first steps toward using ML as a decision support for S&OP in an ETO context. Three studies have been performed to map the current state of ML in S&OP in ETO contexts, to understand the challenges and tasks connected to S&OP in an ETO context, and to explore some of the considerations required when implementing ML in S&OP in an ETO context. The main findings indicate that implementing ML in an ETO context with the purpose of improving S&OP requires an understanding of challenges and related tasks before starting any ML implementation projects. Further, considerations are required before starting to understand available data and to build data models. Tasks for ML must also be understood and agreed. Mechanisms behind occurring challenges need to be understood as well. What is driving trust for a technology and the business process is also important to understand and prepare for, ahead of an ML implementation. The results of the studies are (i) a model presenting the different parts of S&OP in an ETO context, (ii) specific challenges and related tasks, (iii) a model of critical aspects of trust connected to the process, the technology, and the combination of the two, and finally, (iv) a model for assisting in understanding the mechanisms behind capacity and load in engineering. / Alla företag hanterar taktiska planeringsfrågor och beslut för att till exempel balansera tillgång och efterfrågan för att kunna skapa en tillräckligt bra leveransförmåga utan för mycket lager eller resurser/kapacitet. Vissa företag använder sälj och verksamhetsplanering (SVP) som sin taktiska planeringsprocess. Den pågående vågen mot ökad kundanpassning gäller för allt fler produkter vilket ger en förskjutning från standardprodukter, tillverkade mot lager, till mer kundanpassade produkter där produkten antingen är monterad, tillverkad eller konstruerad mot order (engineer-to-order, ETO). Denna förskjutning medför en ny ökad komplexitet i frågor och beslut kopplade till taktisk planering för företag och måste hanteras på ett effektivt sätt. Litteraturen är fåtalig avseende användningen av SVP i en ETO-kontext. Möjligheter för företag att lagra stora mängder data och tillgänglighet av teknologier som maskininlärning (ML) ger möjligheter att använda ML-prediktioner som beslutsstöd i affärsprocesser som SVP. ML-modeller tränas normalt med stora datamängder, och det är en utmaning i en ETO-kontext eftersom det normalt är förknippat med små datamängder. Litteratur avseende användningen av ML i SVP och i ETO-kontext är också den fåtalig. Syftet med denna forskning är att utforska var och hur ML kan vara ett användbart verktyg för taktisk planering såsom SVP i en ETO-kontext. Här tas de första stegen mot användningen av ML som beslutsstöd för SVP i ett ETO-sammanhang. Tre studier har genomförts för att kartlägga litteratur kring ML för SVP i ETO-kontext, för att förstå utmaningarna och uppgifterna kopplade till SVP i en ETO-kontext och för att utforska några av de överväganden som krävs vid implementering av ML i SVP i en ETO-kontext. De viktigaste resultaten indikerar att implementering av ML i ett ETO-sammanhang med syftet att förbättra SVP kräver en förståelse för utmaningar och relaterade uppgifter innan man startar ML-implementeringsprojekt. Vidare krävs överväganden innan tillgängliga data analyseras och datamodeller byggs. De som ska utföra implementeringen behöver förstå och komma överens om vad uppgiften är. Förståelse för mekanismer bakom utmaningar som uppkommer krävs också. Vad som driver förtroende (trust) för en teknik och affärsprocess är också viktigt att förstå och vara förberedd på inför en ML-implementering. Resultaten av studierna är (i) en modell för att presentera de olika delarna av SVP i en ETO-kontext, (ii) specificerade utmaningar och relaterade uppgifter, (iii) en modell med kritiska aspekter av trust kopplat till processen, teknologin, och kombinationen av de två och slutligen (iv) en modell för att förstå mekanismerna bakom kapacitet och beläggning inom orderkonstruktion.

In this thesis, we study three supply chain planning problems. The first two problems fall in the tactical planning level, while the third one falls in the strategic/tactical level. We present a direct application for the first two planning problems in the wind turbines industry. For the third problem, we show how it can be applied to supply chains in the food industry. Many countries and localities have the explicitly stated goal of increasing the fraction of their electrical power that is generated by wind turbines. This has led to a rapid growth in the manufacturing and installation of wind turbines. The globally installed capacity for the manufacturing of different components of the wind turbine is nearly fully utilized. Because of the large penalties for missing delivery deadlines for wind turbines, the effective planning of its supply chain has a significant impact on the profitability of the turbine manufacturers. Motivated by the planning challenges faced by one of the world’s largest manufacturers of wind turbines, we present a comprehensive tactical supply chain planning model for manufacturing of wind turbines in the first part of this thesis. The model is multi-period, multi-echelon, and multi-commodity. Furthermore, the model explicitly incorporates backorder penalties with a general cost structure, i.e., the cost structure does not have to be linear in function of the backorder delay. To the best of our knowledge, modeling-based supply chain planning has not been applied to wind turbines, nor has a model with all the above mentioned features been described in the literature. Based on real-world data, we present numerical results that show the significant impact of the capability to model backorder penalties with general cost structures on the overall cost of supply chains for wind turbines. With today’s rapidly changing global market place, it is essential to model uncertainty in supply chain planning. In the second part of this thesis, we develop a two-stage stochastic programming model for the comprehensive tactical planning of supply chains under supply uncertainty. In the first stage, procurement decisions are made while in the second stage, production, inventory, and delivery decisions are made. The considered supply uncertainty combines supplier random yields and stochastic lead times, and is thus the most general form of such uncertainty to date. We apply our model to the same wind turbines supply chain. We illustrate theoretical and numerical results that show the impact of supplier uncertainty/unreliability on the optimal procurement decisions. We also quantify the value of modeling uncertainty versus deterministic planning. Supplier selection with quantity discounts has been an active research problem in the operations research community. In this the last part of this thesis, we focus on a new quantity discounts scheme offered by suppliers in some industries. Suppliers are selected for a strategic planning period (e.g., 5 years). Fixed costs associated with suppliers’ selection are paid. Orders are placed monthly from any of the chosen suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate all this in a multi-period multi-product multi-echelon supply chain planning problem and develop a mixed integer programming (MIP) model for it. Leading commercial MIP solvers take 40 minutes on average to get any feasible solution for realistic instances of our model. With the aim of getting high-quality feasible solutions quickly, we develop an algorithm that constructs a good initial solution and three other iterative algorithms that improve this initial solution and are capable of getting very fast high quality primal solutions. Two of the latter three algorithms are based on MIP-based local search and the third algorithm incorporates a variable neighborhood Descent (VND) combining the first two. We present numerical results for a set of instances based on a real-world supply chain in the food industry and show the efficiency of our customized algorithms. The leading commercial solver CPLEX finds only a very few feasible solutions that have lower total costs than our initial solution within a three hours run time limit. All our iterative algorithms well outperform CPLEX. The VND algorithm has the best average performance. Its average relative gap to the best known feasible solution is within 1% in less than 40 minutes of computing time.