This thesis considers the problem of performance modeling, analysis and control of capacitated re-entrant lines. Specifically, the first part of the thesis develops an analytical framework for the modeling, analysis and control of capacitated re-entrant lines, which is based on Generalized Stochastic Petri nets (GSPN) framework. The corresponding scheduling problem is systematically formulated, and the structure of the optimal policy is characterized and compared to that identified for "traditional" re-entrant lines. The second part of thesis addresses the problem of developing a systematic and computationally effective method for computing the optimal scheduling policy for any given configuration of capacitated re-entrant line. Specifically, the underlying scheduling problem is transformed to a Markov Decision Process (MDP) problem and an algorithm that systematically generates the MDP formulation for any given fab configuration is developed. The third part of thesis develops an effective approximating scheme based on the Neuro-Dynamic Programming (NDP) theory. In its general definition, the NDP method seeks the approximation of the optimal relative value function of the underlying MDP formulation by a parameterized function. Hence, an approximating structure for the considered problem is proposed and the quality of the generated approximations is systematically assessed. More specifically, this part of the thesis develops a set of "feature" functions and the mathematical apparatus necessary to evaluate the considered approximating scheme through a numerical experiment. The obtained results indicate that good quality approximations can be achieved by considering a set of features that characterize the distribution of the running process instances to the various processing stages and their lower order interactions. The last part of the thesis exploits the performance models developed in its earlier parts in order to provide an analytical characterization of the optimality of various deadlock resolution strategies for Markovian resource allocation systems under the objective of maximizing throughput.

This research introduces a general approach for integrating a probabilistic model of the changes in the committed orders with an analytical model of production and inventory planning under multi-period Quantity Flexibility contracts. We study a decentralized structure where a capacitated manufacturer, capable of subcontracting, serves multiple contract buyers who actually perform forecasts on a rolling horizon basis. We model the evolution of buyers&#039 / commitments as a multiplicative forecast evolution process accommodating contract revision limits. A finite Markovian approximation to this sophisticated evolution model is introduced for facilitating the associated complex probability modeling. We implement computational dynamic programming and introduce an effective approach for reducing state-space dimensionality building upon our forecast evolution structure. Computational investigation demonstrates how the manufacturer benefits from the existence of order commitments and subcontracting option by analyzing the interplay of decisions.

Planning and scheduling in semiconductor manufacturing is a difficult problem due to long cycle times, a large number of operational steps, diversified product types, and low-volume high-mix customer demand. This research addresses several problems that arise in the semiconductor industry related to front-end wafer fabrication operations and back-end assembly and test operations. The mathematical models built for these problems turn out to be large-scale mixed integer programs and hard to solve with exact methods. The major contribution of this research is to combine mathematical programming with metaheuristics to find high quality solutions within the time limits imposed by the industrial engineers who oversee the fabrication and test facilities. In order to reduce the size of problems that arise in practice, it is common to cluster similar product types into groups that reflect their underlying technology. The first part of the research is aimed at developing a greedy randomized adaptive search procedure (GRASP) coupled with path relinking (PR) to solve the capacitated clustering problem. The model is generic and can be applied in many different situations. The objective is to maximize a similarity measure within each cluster such that the sum of the weights associated with the product types does not exceed the cluster capacity in each case. In phase I, both a heaviest weight edge (HWE) algorithm and a constrained minimum cut (CMC) algorithm are used to select seeds for initializing the clusters. Feasible solutions are obtained with the help of a self-adjusting restricted candidate list. In phase II, three neighborhoods are defined and explored using the following strategies: cyclic neighborhood search, variable neighborhood descent, and randomized variable neighborhood descent (RVND). The best solutions found are stored in an elite pool. In a post-processing step, PR coupled with local search is applied to the pool members to cyclically generate paths between each pair. The elite pool is updated after each iteration and the procedure ends when no further improvement is possible. After grouping the product types into technologies, a new model is presented for production planning in a high volume fab that uses quarterly commitments to define daily target outputs. Rather than relying on due dates and priority rules to schedule lot starts and move work in process through the shop, the objective is to minimize the sum of the deviations between the target outputs and finished goods inventory. The model takes the form of a large-scale linear program that is intractable for planning horizons beyond a few days. Both Lagrangian relaxation and Benders decomposition were investigated but each proved ineffective. As a consequence, a methodology was developed which was more tailored to the problem’s structure. This involved creating weekly subproblems that were myopic but could be solved to optimality within a few minutes, and then postprocessing the results with a decomposition algorithm to fully utilize the excessive machine time. The heart of the post-processor consists of a rescheduling algorithm and a dispatching heuristic. The third part of the research focuses on the combinatorial problem of machinetooling setup and lot assignments for performing back-end operations. A new model and solution methodology are presented aimed at maximizing the weighted throughput of lots undergoing assembly and test, while ensuring that critical lots are given priority. The problem is formulated as a mixed-integer program and solved again with a GRASP that makes use of linear programming. In phase I of the GRASP, machine-tooling combinations are tentatively fixed and lot assignments are made iteratively to arrive at a feasible solution. This process is repeated many times. In phase II, a novel neighborhood search is performed on a subset of good solutions found in phase I. Using a linear programming-Monte Carlo simulation-based algorithm, new machine-tooling combinations are identified within the neighborhood of the solutions carried over, and improvements are sought by optimizing the corresponding lot assignments. / text

The minimum set cover problem is, without question, among the most ubiquitous and well-studied problems in computer science. Its theoretical hardness has been fully characterized--logarithmic approximability has been established, and no sublogarithmic approximation exists unless P=NP. However, the gap between real-world instances and the theoretical worst case is often immense--many covering problems of practical relevance admit much better approximations, or even solvability in polynomial time. Simple combinatorial or geometric structure can often be exploited to obtain improved algorithms on a problem-by-problem basis, but there is no general method of determining the extent to which this is possible. In this thesis, we aim to shed light on the relationship between the structure and the hardness of covering problems. We discuss several measures of structural complexity of set cover instances and prove new algorithmic and hardness results linking the approximability of a set cover problem to its underlying structure. In particular, we provide: - An APX-hardness proof for a wide family of problems that encode a simple covering problem known as Special-3SC. - A class of polynomial dynamic programming algorithms for a group of weighted geometric set cover problems having simple structure. - A simplified quasi-uniform sampling algorithm that yields improved approximations for weighted covering problems having low cell complexity or geometric union complexity. - Applications of the above to various capacitated covering problems via linear programming strengthening and rounding. In total, we obtain new results for dozens of covering problems exhibiting geometric or combinatorial structure. We tabulate these problems and classify them according to their approximability.

abstract: This thesis presents a successful application of operations research techniques in nonprofit distribution system to improve the distribution efficiency and increase customer service quality. It focuses on truck routing problems faced by St. Mary’s Food Bank Distribution Center. This problem is modeled as a capacitated vehicle routing problem to improve the distribution efficiency and is extended to capacitated vehicle routing problem with time windows to increase customer service quality. Several heuristics are applied to solve these vehicle routing problems and tested in well-known benchmark problems. Algorithms are tested by comparing the results with the plan currently used by St. Mary’s Food Bank Distribution Center. The results suggest heuristics are quite completive: average 17% less trucks and 28.52% less travel time are used in heuristics’ solution. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2015

abstract: Carbon Capture and Storage (CCS) is a climate stabilization strategy that prevents CO2 emissions from entering the atmosphere. Despite its benefits, impactful CCS projects require large investments in infrastructure, which could deter governments from implementing this strategy. In this sense, the development of innovative tools to support large-scale cost-efficient CCS deployment decisions is critical for climate change mitigation. This thesis proposes an improved mathematical formulation for the scalable infrastructure model for CCS (SimCCS), whose main objective is to design a minimum-cost pipe network to capture, transport, and store a target amount of CO2. Model decisions include source, reservoir, and pipe selection, as well as CO2 amounts to capture, store, and transport. By studying the SimCCS optimal solution and the subjacent network topology, new valid inequalities (VI) are proposed to strengthen the existing mathematical formulation. These constraints seek to improve the quality of the linear relaxation solutions in the branch and bound algorithm used to solve SimCCS. Each VI is explained with its intuitive description, mathematical structure and examples of resulting improvements. Further, all VIs are validated by assessing the impact of their elimination from the new formulation. The validated new formulation solves the 72-nodes Alberta problem up to 7 times faster than the original model. The upgraded model reduces the computation time required to solve SimCCS in 72% of randomly generated test instances, solving SimCCS up to 200 times faster. These formulations can be tested and then applied to enhance variants of the SimCCS and general fixed-charge network flow problems. Finally, an experience from testing a Benders decomposition approach for SimCCS is discussed and future scope of probable efficient solution-methods is outlined. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2017

Made available in DSpace on 2016-08-29T11:12:16Z (GMT). No. of bitstreams: 1 tese_6649_Dissertação - André Manhães.pdf: 1047859 bytes, checksum: 5602733dce0d5f103f7c07c76367c8c4 (MD5) Previous issue date: 2013-09-16 / O petróleo é a principal fonte energética do mundo contemporâneo, insumo básico de diversos setores econômicos. Com a descoberta do Pré-sal, o Brasil tem a oportunidade de tornar-se um dos maiores produtores de petróleo. Entretanto, para que isso seja alcançado, vários desafios deverão ser superados e, dentre eles, encontra-se o problema de transporte de empregados para operarem as plataformas offshore, distantes até 300km de distância da costa brasileira. Os problemas referentes ao deslocamento de empregados por meio de helicópteros são usualmente tratados como o Capacitated Helicopter Routing Problem (CHRP). Com base nas restrições de origem e de destino de cada cliente, no número de veículos e na capacidade e restrições de voo dos helicópteros, neste tipo de problema deseja-se minimizar os custos de aluguel de helicópteros mais o custo total de quilômetros voados. A presente dissertação propõe um modelo de Programação Linear Inteira Mista (PLIM) para o problema de roteirização de helicópteros com base no Dial-a-Ride Problem (DARP). Além do modelo apresentado, foram apresentados duas abordagens para a execução do modelo de forma exata: i) abordagem sem agrupamento, na qual as requisições que possuem origens iguais e destinos iguais são modeladas como requisições distintas e ii) abordagem com agrupamento, na qual requisições que possuem origens iguais e destinos iguais são aglutinados numa nova e única requisição. O modelo matemático foi executado no software CPLEX e os resultados mostraram que instâncias com até 25 requisições podem ser resolvidas pela abordagem com agrupamento / Oil is the main energy source of contemporary world; it is basic inputs of various economic sectors. With the discovery of Brazil pre-salt, there is an opportunity to become one of the largest oil producers. However, to achieve her own goals, Brazil must overcome several challenges, including the problem of transporting employees to operate offshore platforms 300km distant away from the Brazilian coast. Problems related to displacement of employees by helicopters are usually treated as Capacitated Helicopter Routing Problem (CHRP). Based on source and destination restrictions of each client, the number of vehicles, capacity and helicopter flight constraints, this type of problem proposes to minimize the cost of renting helicopters and the total cost of flown kilometers. This dissertation proposes a model of Mixed Integer Linear Programming (MILP) for the helicopters routing problem based on a Dial-a-Ride Problem (DARP). Besides the presented model, we presented two approaches to implementing the model in an exact way: i) non-clustered approach, in which requests that have the same origin and destination are equal modeled as separate requests; and ii) clustered approach, in which requests that have the same origins and destinations are clumped together in a new single request. The mathematical model was implemented in software CPLEX and results showed that instances with up to 25 requests can be resolved in the clustered approach

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2015-07-17T16:00:19Z No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) / Made available in DSpace on 2015-07-17T16:00:19Z (GMT). No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) Previous issue date: 2015-01-27 / In recent years, the Vehicle Routing Problem (VRP) has attracted an increasing attention from researchers due to the great difficulty of its solution and its presence in various practical situations. As consequence, there has been great effort to develop more robust, agile and flexible algorithms that can be modeled according to the scenario that describes the problem. The Capacitated Vehicle Routing Problem (CVRP) is a version of VRP and consists in determining a set of routes to be followed by a fleet of homogeneous vehicles, which must serve a set of customers. The objective is to minimize the total cost of the routes subject to the following restrictions: i) routes must start and end in the same distribution center; ii) each customer must be visited once and its demand must be met in full by only one vehicle and iii) the sum of customers' demands included in a route cannot exceed the vehicle capacity. The CVRP belongs to the class of NP-hard problems, that is, problems whose the solution usually requires non-polynomial complexity time algorithms and because of this are usually resolved with the use of heuristic and metaheuristics algorithms. In this work, it was investigated the optimization of CVRP using Genetic Algorithm (GA) with alternative chromosome representations and heuristics. To this end, three strategies, each one employing a different model of chromosome representation for encoding solution in AG were proposed. In addition, the heuristics of Gillett and Miller to generate solutions that are included in the initial population of GA and Hill-climbing for refinement of GA solutions, after a number of generations without improvement, were adopted. In the performed experiments, the results obtained by the proposed strategies were compared with each other and also with the best results found in the literature for a set of known instances. These experiments showed that the proposed strategies provided good results with respect to quality of solutions well as the computational cost. In addition, it was possible to evaluate the viability of each employed chromosome representation and the contribution of the heuristics in the convergence process of GA. / Nos últimos anos o Problema de Roteamento de Veículos (PRV) tem atraído cada vez mais a atenção de pesquisadores devido à grande dificuldade de solução e sua presença em várias situações do cotidiano. Em decorrência disso, tem havido um grande esforço para desenvolver algoritmos cada vez mais robustos, ágeis e flexíveis e que possam ser modelados com base no cenário que descreve o problema. O Problema de Roteamento de Veículos Capacitado (PRVC) é uma versão do PRV e consiste em encontrar um conjunto de rotas a serem seguidas por uma frota de veículos homogêneos, os quais devem atender a um conjunto de clientes. O objetivo é minimizar o custo total das rotas respeitando as seguintes restrições: i) as rotas devem iniciar e terminar no mesmo centro de distribuição; ii) cada cliente deve ser visitado uma única vez e sua demanda deve ser atendida integralmente por apenas um veículo e iii) a soma das demandas dos clientes incluídos em uma rota não pode exceder a capacidade do veículo. Problemas desta natureza podem ser classificados como NP-Hard, ou seja, possuem ordem de complexidade não polinomial e normalmente são resolvidos com uso de algoritmos heurísticos e meta-heurísticos. Neste trabalho investigou-se a otimização do PRVC usando Algoritmo Genético (AG) com representações cromossômicas e heurísticas alternativas. Para tanto, foram propostas três estratégias, cada uma delas empregando um modelo diferente de representação cromossômica para codificação da solução no AG. Além disso, foram empregadas as heurísticas de Gillett e Miller para gerar soluções que são incluídas na população inicial do AG e Subida/Descida de Encosta para refinamento das soluções, após um certo número de gerações sem melhoria. Nos experimentos realizados, os resultados obtidos pelas estratégias propostas foram comparados entre si e também com os melhores resultados encontrados na literatura para um conjunto de instâncias conhecidas. Pode-se constatar, a partir desses experimentos, que as estratégias apresentaram bons resultados tanto no que tange a qualidade das soluções quanto ao tempo computacional dispendido. Em adição, foi possível avaliar a viabilidade de cada uma das representações cromossômicas empregadas, além da contribuição das heurísticas no processo de convergência do ag.

IKEA Industry has factories in several locations where one location is in Hultsfred, Småland. IKEA Industry Hultsfred produces IKEA:s wardrobe collection PAX. In 2018, 2.8 million wardrobes were manufactured in Hultsfred. This thesis covers the subject of constructing and developing an optimization model which has the same characteristics as the plinth production at IKEA Industry Hultsfred. This means that the optimization model must consider production sequence; the setup time is different depending on which products that have been produced in the machine earlier. The thesis also covers to investigate how much time that is necessary to produce the plinths to Hultsfred, Portugal, and Germany under different circumstances. This thesis also balances inventory levels and setup time. I.e., how the inventory levels are changed if more emphasis is put into minimizing setup time, and the other way around. The results show that lower inventory levels and setup times are achievable over seven days if the production planning technique is changed.

Environmental issues are a vital concern in today’s world. The Swedish government and local businesses are developing a sustainable business and eco-friendly environment for city inhabitants. Last-mile pickup and delivery services are a key concern, which significantly impacts the environment and society. The Norwegian/Swedish parcel delivery company Bring, the reusable waste management company Ragn-Sells, the city of Stockholm, the research institute Sustainable Innovation, and the KTH Royal Institute of Technology are jointly working together in the Intercitylog2 project with a vision to handle better last-mile pickups or deliveries that are jointly serviced by small electric vehicles from an urban micro terminal. This thesis addresses the optimizations of simultaneous pickup and delivery operations using homogeneous vehicles and considering vehicle capacity, time windows and environmental constraints. A mathematical model is developed to address the problem using an exact commercial solver. The quality of the solutions has been evaluated with real pickup and deliveries of the participating company. The primary objective function is formulated to minimize the travel cost by finding the shortest path, and the results are compared with current routing operation data. KPIs are developed and evaluated based on the facts and figures from the obtained results of the experiments. The two scenarios, big vehicles and small vehicles are also developed and evaluated to find the best route optimization opportunity for the companies. The results show that the optimized operation could decrease delivery distance by 36.72% and 37.13% and delivery time by 43.65% and 47.08% for big and small vehicles operations, respectively, compared to the current routing operations. A round trip can complete within a defined time frame to avoid the battery running out during a route. Energy constraints demonstrate that using electric vehicles considerably reduce significant amounts of CO2 emission from the environment. / Miljöfrågor är en viktig fråga i dagens värld. Den svenska regeringen och lokala företag arbetar tillsammans för att utveckla ett hållbart företagande och miljövänlig miljö för stadens invånare. Last-mile hämtning och leveranstjänster är en viktig fråga, som avsevärt påverkar miljön och samhället.Det norsk/svenska paketleveransföretaget Bring, återanvändbara avfallshanteringsföretaget Ragn- Sells, Stockholms stad, forskningsinstitutet Sustainable Innovation och Kungliga Tekniska Högskolan arbetar tillsammans i Intercitylog2-projektet för en vision för att bättre hantera last-mile pickuper eller leveranser som gemensamt betjänas av små elfordon från en urban mikroterminal. Detta examensarbete behandlar optimering av samtidiga hämtnings- och leveransoperationer med homogena fordon och med hänsyn till fordonskapacitet, tidsfönster och miljömässiga begränsningar. En matematisk modell utvecklas för att ta itu med problemet med en exakt kommersiell lösare och kvaliteten på lösningarna har utvärderats med verklig upphämtning och leveranser från det deltagande företaget. Den primära målfunktionen är formulerad för att minimera reskostnaden genom att hitta den kortaste vägen, och resultaten jämförs med aktuella ruttoperationsdata. KPI:er utvecklas och utvärderas utifrån fakta och siffror från de erhållna resultaten av experimenten. De två scenarierna, stora fordon och små fordon, är också utvecklade och utvärderade för att hitta den bästa ruttoptimeringsmöjligheten för företagen. Resultaten visar att den optimerade driften kan minska leveransavståndet med 36,72% och 37,13% och leveranstiden med 43,65% och 47,08% för stora och små fordonsoperationer, jämfört med nuvarande ruttoperationer. En tur och retur kan genomföras inom en definierad tidsram för att undvika att batteriet tar slut under en rutt. Energibegränsningar visar att användning av elfordon avsevärt minskar betydande mängder CO2-utsläpp från miljön.