Swedish home care industry has been facing both external and internal problems, such as ageing population, varying quality and unsatisfactory continuity. Accordingly, workforce scheduling system, as one of the most common and useful software within home care planning nowadays, is in need of constant improvement and upgrading. This master’s thesis aimed to explore and analyze improvement potential of an established workforce scheduling system for an IT-company. The thesis was divided into two phases, of which a pre-study in Phase I tried to understand the planning process for planners and identify the perceived problems and shortcomings of the current system from a planner’s perspective. Based on the analysis from the pre-study, the caregiver continuity was chosen as the research area for Phase II. The current system was re-implemented and was modelled as an optimization problem. Furthermore, the system mainly consisted of two key parts, mixed integer linear programming (MILP) and heuristics. Different approaches in terms of modifications in both MILP and heuristics were applied to the re-implemented system. The performance of the modifications was measured by multiple evaluation indicators. The test results showed that there was a potential to improve caregiver continuity with 1.2% to almost 13% depending on the modification type. The modifications were lastly suggested for further examination regarding their practical appropriateness by applying them to the current running algorithm. / Den svenska hemtjänsten möter både yttre och inre problem såsom åldrande befolkning, varierande kvalitet och bristande kontinuitet. Schemaläggningssystemet som är en av de vanligaste och användbaraste programvarorna inom hemtjänsten behöver därmed en ständig förbättring och uppgradering som bemöter de existerande utmaningarna. Detta examensarbete hade som syfte att utforska och analysera förbättringspotentialen av ett etablerat schemaläggningssystem för ett ITföretag. Arbetet var indelat i två faser, varav förstudien i Fas I försökte förstå planerarnas planeringsprocesser och identifiera upplevda problem och brister i det nuvarande systemet utifrån ett planerares perspektiv. Baserat på analysen från förstudien, personalkontinuitet valdes som ett forskningsområde för Fas II. Nuvarande systemet implementerades om och det modellerades som ett optimeringsproblem. Systemet bestod huvudsakligen av två nyckeldelar, blandat heltalslinjärprogrammering (MILP) och heuristik. Olika metoder i form av modifieringar i både MILP och heuristik tillämpades på det omimplementerade systemet. Modifieringarnas prestanda mättes sedan med flera utvärderingsindikatorer. Testresultaten visade att, beroende på vilken modifiering det gäller, fanns det en potential att förbättra personalkontinuiteten med 1,2% till nästan 13%. Det föreslogs slutligen att modifieringarnas praktiska lämplighet behövs undersökas ytterligare genom att applicera det på det nuvarande systemet som är i drift.

With increasing shares of variable renewable energy sources in the power mix, the need for energy storage solutions is projected to increase as well. Storage can in such combined systems help mitigate the issues with relying on intermittent sources by time-shifting the supply and smoothing out frequency fluctuations, to name some examples. This thesis has focused on Azelio ABs flagship product, the TES.POD, which is a long-duration thermal energy storage technology. When integrated with, for example, solar PV power, the TES.POD can store excess energy and dispatch it during times of low supply or when during the evening/night. The aim of the thesis has been the development of a day-ahead dispatch optimization tool for systems that include multiple TES.PODs, combined into a Cluster, and solar PV. The model was to be built using the Python programming language and based on Mixed-Integer-Linear-Programming (MILP) methods. The PV+storage system was then allowed to be connected to supplementary power sources such as a larger electric grid, or diesel generators in off-grid locations. The purpose of the optimization model is to find the most economic way to operate the individual TES.PODs while also keeping track of other system components, using a cost-based objective function (minimize costs). A focus has been on using high time resolution (small time step) in order to investigate the impact that the TES.PODs dynamic constraints has on operation. Another strength compared to pre-existing models was the ability to operate individual units indifferent to each other, as opposed to having them all operated in unison. Final results from benchmarking tests and two case studies indicated that using the optimization tool with smaller time steps had an effect on key indicators, and could lead to improved economy in the system. It was observed in both cases that the cost of electricity was reduced by running the optimization tool with time steps of either two or three minutes when compared with using an hourly resolution. Furthermore, several usage parameters for the TES.PODs, notably the total amount of operated hours and energy output per cycle, saw improvements which could lead to reduced cost of operation and maintenance. While not the main intent, testing different Cluster sizes and amount of installed PV capacity with the model, it could also be used in strategic decisions for system sizing. However, due to rapidly growing computational times in systems with large TES.POD clusters and using smaller time steps, the possibility of adding more complexity to the model in future work must be done with caution. To combat this issue, either improvements to the model formulation could be attempted, or by using more powerful hardware or optimizer (imported software algorithm that handles solving the model).

There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. To meet the increasing demand radio access networks (RAN) used for mobile communication are continuously being improved with the current generation enabling larger virtualization of the network through the Cloud RAN (C-RAN) architecture. This facilitates the usage of commercial off-the-shelf servers (COTS) in the network replacing specialized hardware servers and making it easier to scale up or down the network capacity after traffic demand. This thesis looks at how we can efficiently identify servers needed to meet traffic demand in a network consisting of both COTS servers and specialized hardware servers while trying to reduce the energy consumption of the network. We model the problem as a network where the antennas and radio heads are connectedto the core network through a C-RAN and a specialized hardware layer. The network is then represented using a graph where the nodes represent servers in the network. Using this problem model as a base we then generate problem instances with varying topologies, server profiles, and traffic demands. To find out how the traffic should be passed through the network we test two different methods: A mixed integer linear programming (MILP) method focused on energy minimization and a graph attention network (GAT) predictor combined with the energy minimization MILP. To help evaluate the results we also create three other methods: a MILP model that tries to spread the traffic as evenly as possible, a random predictor combined with the energy minimization MILP and a greedy method. Our results show that the energy optimization MILP method can be used to create optimal solutions, but it suffer from a slow computation time compared to the other methods. The GAT model shows promising results in making predictions regarding what servers should be included in a network making it possible to reduce the problem size and solve it faster with MILP. The mean energy cost of the solutions created using the combined GAT/MILP method was 4% more than just using MILP but the time gain was substantial for problems of similar size as the GAT was trained on. With regards to computation time the combined GAT/MILP method used was 85% faster than using only MILP. For networks of almost double the size than the ones that the GAT model was trained on the solutions of the combined GAT and MILP methods had a mean energy cost increase of 7% while still showing a strong speedup, being 93% faster than when only using MILP.

There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been researched extensively and proven to have significant fuel-saving benefits, however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. The realization of these formations requires serious consideration of formation contingencies and safety during closer-in maneuvering of large commercial aircraft. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This thesis investigates the utility of optimization modeling in providing insight into generation of aircraft escape paths for formation contingency planning. Three high-altitude commercial aircraft formation scenarios are presented; formation join, formation emergency exit, and formation escape. The model-generated paths are compared with pilot-generated escape plans using the author’s pilot expertise. The model results compare well with pilot intuition and are useful in presenting solutions not previously considered, in evaluating separation requirements for improvement of escape path planning and in confirming the viability of the pilot-generated plans. The novel optimization model formulation presented in this thesis is the first model shown to be capable of generating escape paths comparable to pilot- generated escape plans and is also the first to incorporate avoidance of persistent and drifting wake turbulence within the formation.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.

Nous nous intéressons au Problème de Dimensionnement de Lots mono-produit (PDL) dans une chaîne logistique composée d'un détaillant et d'un fournisseur en y intégrant le contrat buyback et l'approvisionnement par batch. L'objectif est de déterminer un plan d'approvisionnement pour le détaillant pour satisfaire ses demandes déterministes sur un horizon fini, tout en minimisant ses coûts d'approvisionnement et de stockage. Concernant le coût d'approvisionnement, nous supposons deux structures différentes : FTL (Full Truck Load) et OFB (Only Full Batch). Trois types de contrat buyback sont étudiés : avec des périodes de retour fixes, avec une limite de temps sur les retours, et avec des retours uniquement dans les périodes d'approvisionnement. Chaque contrat est caractérisé par un pourcentage de retour maximal qui peut être égal à 100% (retour total) ou inférieur à 100% (retour partiel). Pour le PDL sous le contrat buyback avec des périodes de retour fixes, nous supposons le cas de ventes perdues (lost sales). En outre, un autre concept ajouté dans les PDL sous les trois types de contrat buyback réside dans le fait que le détaillant peut jeter la quantité invendue et non retournée au fournisseur, appelé mise au rebut (disposal). Nous avons modélisé ces différentes extensions du PDL par des Programmes Linéaires en Nombres Entiers (PLNE). Nous avons ensuite développé des algorithmes exacts polynomiaux de programmation dynamique pour certaines extensions, et montré la NP-difficulté pour d'autres. Pour chaque problème résolu en temps polynomial, nous avons comparé l'efficacité et les limites de l'algorithme proposé avec celles des quatre formulations en PLNE. Nous avons également proposé des modèles mathématiques pour les PDL sous d'autres types de contrats de réservation de capacité dans le cas déterministe à multi-périodes. / We study the single-item Lot Sizing Problem (LSP) in a supply chain composed of a retailer and a supplier by integrating the buyback contract and the batch ordering. The purpose is to determine a replenishment planning for the retailer to satisfy his deterministic demands over a finite horizon, while minimizing the procurement and inventory costs. Regarding the procurement cost, we assume two different structures: FTL (Full Truck Load) and OFB (Only Full Batch). We consider three types of buyback contract: with fixed return periods, with a time limit on returns, and with returns permitted only in procurement periods. Each contract is characterized by the maximum return percentage being either equal to 100% (full return) or less than 100% (partial return). For the LSP under the buyback contract with fixed return periods, we assume the concept of lost sales. Another concept considered in the LSP's under the three types of buyback contract is the disposal of the unsold and unreturned quantities. We model these different LSP extensions as a Mixed Integer Linear Program (MILP). Thereafter, we develop exact polynomial time dynamic programming algorithms for some extensions and show the NP-hardness of others. For each problem solved in polynomial time, we compare the efficiency and the limits of the proposed algorithm with those of four MILP formulations by performing different tests. Finally, we propose mathematical models for the LSP's under other types of the capacity reservation contract in the deterministic and multi-period case.

Cette thèse s'intéresse au développement de stratégies de décalage de charge pouvant être appliquées à un parc de chauffe-eau Joule (CEJ).On propose une modélisation entrée-sortie du système que constitue le CEJ. L'idée est de concevoir un modèle précis et peu coûteux numériquement, qui pourrait être intégré dans un CEJ intelligent. On présente notamment un modèle phénoménologique multi-période d'évolution du profil de température dans le CEJ ainsi qu'un modèle de la demande en eau chaude. On étudie des stratégies d'optimisation pour un parc de CEJ dont la résistance peut être pilotée par un gestionnaire central. Trois cas de figures sont étudiés. Le premier concerne un petit nombre de ballons intelligents et présente une méthode de résolution d'un problème d'optimisation en temps discret. Puis, on s'intéresse à un parc de taille moyenne. Une heuristique gardant indivisibles les périodes de chauffe (pour minimiser les aléas thermo-hydrauliques) est présentée. Enfin, un modèle de comportement d'un nombre infini de ballon est présenté sous la forme d'une équation de Fokker-Planck. / This thesis focuses on the development of advanced strategies for load shifting of large groups of electric hot water tanks (EHWT).The first part of this thesis is dedicated to representing an EHWT as an input-output system. The idea is to design a simple, tractable and relatively accurate model that can be implemented inside a low-power computing unit embedded in a smart EHWT, for practical applications of optimization strategies. It includes in particular a phenomenological multi-period model of the temperature profile in the tank and a realistic domestic hot water consumption model.The second part focuses on the design of optimal control strategies for a group of tanks. Three use-cases are studied. The first one deals with a small number of smart and controllable EHWT for which we propose a discrete-time optimal resolution method. The second use-case adresses a medium-scale group of controllable tanks and proposes a heuristic which keeps the heating period undivided to minimize thermo-hydraulic hazards. Finally, we present the modelling of the behavior of a infinite population of tanks under the form of a Fokker-Planck equation.

Cette thèse a pour objectif de modéliser et résoudre des problèmes d’optimisation d’un système d’auto-partage avec des voitures électriques dit « à un seul sens », où les utilisateurs peuvent prendre une voiture dans une station et la laisser ensuite dans une autre. Ce fonctionnement conduit généralement à une situation de déséquilibre dans la répartition des voitures avec certaines stations pleines et d’autres vides. Une des solutions utilisées par les opérateurs d’autopartage pour pallier ce problème est le recours à des agents pour déplacer les voitures selon le besoin. Identifier et répondre à ce besoin est un problème d’optimisation non trivial, notamment à cause de l’usage de véhicules électriques, ce qui engendre des contraintes de rechargement de batteries et d’autonomie. Le problème d’optimisation est décomposé en deux sous-problèmes : le premier est le problème d’affectation des voitures aux clients, ainsi que leurs routages, que nous nommons ROCSP pour Recharging One way Car Sharing Problem ; le second problème est celui du planning des agents et leurs routages que nous nommons ESRP pour Employee Scheduling Routing Problem. 1. Résolution du ROCSP : deux modélisations en Programmation Linéaire en Nombres Entiers (PLNE) sont proposées, la première basée sur les flots et la deuxième sur les chemins, ce qui fait que les deux modèles intègrent de manière différente les contraintes de recharge électrique. Comme la résolution exacte à travers les modèles PLNE s’avère très gourmande en temps de calcul et non adaptée aux instances d’auto-partage de taille réelle, nous proposons des heuristiques qui permettent dans un temps raisonnable d’optimiser la redistribution des voitures et la gestion du service. Ces heuristiques permettent de calculer le nombre de voitures et les différentes opérations de relocalisation (redistribution des voitures) à réaliser sur une journée donnée. 2. Résolution du ESRP : un modèle PLNE est proposé pour la résolution exacte du ESRP, et, en complément, des heuristiques sont proposées pour une résolution approchée et relativement rapide. L’objectif est la détermination du nombre minimal d’agents nécessaire pour effectuer les opérations de relocalisation qui découlent du premier problème, le ROCSP. Dans une partie prospective, et une fois les ROCSP et ESRP résolus dans leur version statique, nous nous focaliserons sur une autre variante du problème avec réservation dynamique. Nous proposons également d’explorer un nouveau concept - l’auto-copartage - qui se veut une hybridation entre autopartage et covoiturage. Les algorithmes proposés ont été validés sur le réseau Auto Bleue de la ville de Nice essentiellement, qui gère une flotte de véhicules électriques, en s’appuyant sur des modèles de génération de flux pour estimer la demande, mais aussi d’autres instances que nous avons générées pour simuler d’autres villes, au sein d’un Système d’Information Géographique. / This thesis aims at modelling and solving optimization problems related to the management of one-way-electric-car-sharing systems, where users can take a car from a station, use it, and then return it to another station. This generally leads to an imbalanced distribution of cars, with some full stations and other empty ones. A solution to this problem, implemented by car-sharing operators, is to employ staff agents to move cars as needed. However, identifying this need is a non-trivial optimization problem, especially since the system may be more constrained when the vehicles used are electric, which generates battery recharging and autonomy constraints. The global optimization problem addressed is then divided into two sub-problems. The first one is assigning the cars to customers, as well as their routing; it is denoted by ROCSP (Recharging OneWay Car Sharing Problem). The second problem involves agents planning and routing; it is denoted by ESRP (Employee Scheduling Routing Problem). 1. For the ROCSP, we propose two Mixed-integer linear programming (MILP) modelizations of the problem: One based on flows and the other based on paths. This means that the two models include the battery-recharging constraints in two different ways. As the exact resolution through the MILP models is quite expensive in terms of computational time and is not adapted for the resolution of real-size car-sharing instances, we introduce heuristics that enable the optimization of cars-redistribution and service management of the service within a reasonable amount of time. These heuristics allows the calculation of the number of cars and the various redistribution operations to be performed on a given day. 2. For the ESRP, this second problem is also addressed with MILP models for the exact resolution, and some heuristics are suggested for an approximate resolution. This process has reasonable calculation time and aims at finding the minimum number of agents to perform the necessary relocation operations that stem from the first problem, namely, the ROCSP. Once the ROCSP and ESRP solved in their static versions, we then focus on the ROCSP by exploring another variant of the problem : ROCSP with dynamic reservation. We also suggest to explore a new concept : Auto-CoPartage, which is a hybridization of car-sharing and carpooling. The stated algorithms are validated on the Auto Bleue electrical vehicles fleet in the network of the city of Nice, essentially by relying on flow generation models to estimate the demand, but also using other instances that we have generated for other cities. All the data are handled using a Geographical Information System.

Une vue d'ensemble des problèmes et des méthodes pour la conception des lignesd'assemblage et d'usinage est donnée. Un nouveau problème d'affectation de la main-d’œuvrepour une ligne d'assemblage multi-produit cadencée avec un objectif de minimiser lenombre d'opérateurs est étudié. Diverses relations de priorité entre les opérations et lesdifférentes fonctions définissant les temps d'opérations, en fonction du nombre d'opérateurssont considérés. Un nouveau problème d'optimisation multiobjectif pour une ligne d'usinagemono-produit est formulé. Plusieurs méthodes exactes et heuristiques et leursimplémentations informatiques pour les deux problèmes sont développées par l'auteur. Unmodule logiciel d'aide à la décision pour résoudre ces problèmes est développé et implémentédans un environnement d'un nouveau PLM d'IBM dans le cadre du projet européen amePLM.Ce module est testé sur un exemple réel de conception d'une ligne de montage des moteurschez Mercedes Benz en Allemagne. / An overview of existing problems and methods for the design of assembly and transfer lines is given. A new workforce assignment problem for a paced multi-product assembly line with a goal of minimizing the number of workers is studied. Various precedence relations between operations and functions of operation processing times dependent on the number of workers areconsidered. A new problem of multi-objective optimization for a single product transfer line is solved. Several exact and heuristic methods and their computer implementations for both problems are developed by the author. An application of developed approaches to solving a real production problem relevant to the European project amePLM is demonstrated.

Made available in DSpace on 2016-06-02T19:53:34Z (GMT). No. of bitstreams: 1 STARK_Felipe_2015.pdf: 2619946 bytes, checksum: 296213b74067cc59aea0beae2cf8f165 (MD5) Previous issue date: 2015-03-20 / Universidade Federal de Minas Gerais / The increase in the municipal and industrial waste generation has caused enviromental and public health problems and as a consequence laws exist to address the issue. In Brazil, with the Federal Law No. 12,350 / 10 about the Política Nacional de Resíduos Sólidos reverse logistics (RL) of some products has become mandatory, including waste tires. However, RL of waste tires has been structured since environmental government agency resolutions propose treatment for tires disposal incorrectly in the environment and new generation of waste tires. Currently, the reverse network is managed by the tire manufacturers and importers associations, and destinations are commonly used are co-processing in cement kilns, and the recovery of rubber and steel as secondary products, by processes such as scrapping or lamination. Increasing transportation and operation costs in the logistics network make the network design critical for the full compliance with the legal goal. This network design is aligned with a planning that considers the financial issues like the minimization of costs or the maximization of profit, while meets the requirements of environmental government agencies. So it involves key strategic decisions, as the location of facilities and material flows, taking into account many parameters simultaneously. The objective of this study is to propose a model for waste tires reverse logistics considering: (i) flows from the output of the collection points (called ecopontos ) to the destination companies; (ii) the possibility of processes as the sorting of used tires in usable condition or not; (iii) the grinding process as an intermediate phase and for which type of destination the tire would be sent. Still are considered fixed costs for the installation of storage centers, for sorting used tires, and intermediary companies, for grinding and separation of components, in addition to the variable operating costs, transportation and potential revenues generated from the substitution of raw materials or fuel in destination companies. The model is a mixed integer linear problem (MILP) with multiple time periods. Experiments are done with a single and multiple periods, finally were present some sensitivity analysis. Other financial constraints as the annual budget and an approach that includes the carbon footprint (CO2) in the transport and processing are explored. The results showed configurations that meet the goal and have a small profit, indicating that storage centers are preferred in places far from destination points and low demand, while the pre-processing companies have opposite behavior. When using the carbon footprint, it was found that the sorting of used tires gain more importance in the view of reducing emissions, because there is less emission in the reuse or refurbishment compared to the manufacturing of new tires. / O aumento na geração de resíduos urbanos e industriais tem ocasionado problemas de ordem ambiental e de saúde pública, e, como consequência, legislações específicas surgiram para tratar do assunto. No Brasil, a Lei Federal nº 12.350/10, da Política Nacional de Resíduos Sólidos, tornou obrigatória a logística reversa (LR) de alguns produtos, entre os quais o pneu usado sem condição de rodagem (inservível). Entratanto, a LR de pneus inservíveis já se apresentava em fase de estruturação, uma vez que resoluções ambientais propunham o tratamento do passivo deixado por anos de descarte incorreto dos pneus no meio ambiente. Atualmente, a rede reversa de pneus é administrada por associações de fabricantes e importadores, e as destinações comumente utilizadas são o coprocessamento, em fornos de cimenteira, e a recuperação da borracha e do aço como subprodutos, por meio de processos como a granulação ou a laminação. Os crescentes custos do transporte terrestre e as proposições de melhorias na rede logística tornam a configuração da rede de logística reversa de pneus como uma proposta para o total cumprimento da meta, alinhados ao planejamento que considere as questões financeiras como a minimização de custos logísticos ou maximização do lucro, enquanto cumprem as exigências dos órgãos ambientais. Esta configuração envolve decisões estratégicas essenciais, como a localização de instalações e determinação dos fluxos de materiais, sendo que muitos parâmetros estão presentes simultaneamente. O objetivo deste estudo é propor um modelo de configuração de rede logística reversa de pneus inservíveis considerando: (i) fluxos a partir da saída dos pontos de coleta (ecopontos) até as empresas destinadoras; (ii) processos como a possibilidade da triagem dos pneus usados em servíveis e inservíveis; (iii) a trituração como fase intermediária e para qual tipo de destinação enviar. São considerados ainda custos fixos para instalação de centros de armazenamento, para triagem dos pneus usados, e empresas intermediárias, para trituração e separação de componentes, além dos custos variáveis de operação, transporte e as possíveis rendas geradas com a substituição da matéria-prima ou combustível nas empresas destinadoras. O modelo apresenta formulação linear inteira mista (MILP) com múltiplos períodos. São feitos experimentos com único período, e com a variação de parâmetros. Posteriormente são estudadas restrições financeiras como orçamento anual e uma abordagem que inclui a pegada de carbono (CO2) no transporte e processamento. Os resultados encontrados apresentaram configurações que cumprem a meta e apresentam um pequeno lucro, indicando que os centros de armazenamento são preferíveis em locais afastados das destinadoras e com baixa demanda, enquanto as empresas intermediárias apresentam comportamento oposto. Quando se utilizou a pegada de carbono, verificou-se que a triagem de pneus ganha mais importância, dada a redução nas emissões do reuso ou reforma dos pneus em relação à fabricação de novos pneus.