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In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multi-stop routing problem. In a multi-stop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In this case, it is called time-dependent traveling salesman problem. This research proposes new improvements on genetic algorithm to solve each of these two optimization problems. First, the Travelling Salesman Problem (TSP) is one of the most important and attractive combinatorial optimization problems. There are many meta-heuristic algorithms that can solve this problem. In this paper, we use a Genetic Algorithm (GA) to solve it. GA uses different operators: selection, crossover, and mutation. Sequential Constructive Crossover (SCX) and Bidirectional Circular Constructive Crossover (BCSCX) are efficient to solve TSP. Here, we propose a modification to these crossovers. The experimental results show that our proposed adjustment is superior to SCX and BCSCX as well as to other conventional crossovers (e.g. Order Crossover (OX), Cycle Crossover (CX), and Partially Mapped Crossover (PMX)) in term of solution quality and convergence speed. Furthermore, the GA solver, that is improved by applying inexpensive local search operators, can produce solutions that have much better quality within reasonable computational time. Second, the Time-Dependent Traveling Salesman Problem (TDTSP) is an interesting problem and has an impact on real-life applications such as a delivery system. In this problem, time among destinations fluctuates during the day due to traffic, weather, accidents, or other events. Thus, it is important to recommend a tour that can save driver’s time and resources. In this research, we propose a Multi-Population Genetic Algorithm (MGA) where each population has different crossovers. We compare the proposed MG against Single-Population Genetic Algorithm (SGA) in terms of tour time solution quality. Our finding is that MGA outperforms SGA. Our method is tested against real-world traffic data [1] where there are 200 different instances with different numbers of destinations. For all tested instances, MGA is superior on average by at least 10% (for instances with size less than 50) and 20% (for instances of size 50) better tour time solution compared to SGA with OX and SGA with PMX operators, and at least 4% better tour time compared toga with SCX operator.

This study deals with chocolate production line scheduling. The particular production line allows producing multiple items at the same time. Another distinguishing property affecting the planning methodology is that an item can have different production capacities when produced in different product combinations which are called production patterns in this study. Planning is done on a 12 weeks rolling horizon. There are 21 products and 103 production patterns covering all the production possibilities. The subject of the study is to construct an algorithm that gives 12 weeks&rsquo / production values of each product and to construct the shift based scheduling of the first week of the planning horizon. The first part is Master Production Scheduling (MPS) and the objective is minimizing the shortage and overage costs. A mathematical modeling approach is used to solve the MPS problem. The second part is the scheduling part which aims to arrange the production patterns obtained from the MPS module within the shifts for the first week of the planning horizon considering the setup times. The MPS module is a large integer programming model. The challenge is finding a reasonable lower bound whenever possible. If it is not possible, finding a reasonable upper bound and seeking solutions better than that is the main approach. The scheduling part, after solving MPS, becomes a TSP and the setup times are sequence independent. In this part, the challenge is solving TSP with an appropriate objective function.

In this study, a new selective and time-window routing problem is defined for the first time in the literature, which is called the campaign routing problem (CRP). The two special cases of the CRP correspond to the two real-life problems, namely political campaign routing problem (PCRP) and the experiments on wheels routing problem (EWRP). The PCRP is based on two main decision levels. In the first level, a set of campaign regions is selected according to a given criteria subject to the special time-window constraints. In the second level, a pair of selected regions or a single region is assigned to a campaign day. In the EWRP, a single selected region (school) is assigned to a campaign day. These two problems are modeled using classical mathematical programming and bi-level programming methods, and a two-step heuristic approach is developed for the solution of the problems. Implementation of the solution methods is done using the test instances that are compiled from the real-life data. Computational results show that the solution methods developed generate good solutions in reasonable time.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2009. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 5, 2010) Includes bibliographical references (p. 152-159).

The traveling salesperson or salesman problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. In addition to vehicle routing, many other applications, e.g., computer wiring, cutting wallpaper, job sequencing or several data visualization techniques, require the solution of a TSP. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Thesis (Ph. D.)--Mathematics, Georgia Institute of Technology, 2008. / Committee Chair: Thomas, Robin; Committee Co-Chair: Cook, William J.; Committee Member: Dvorak, Zdenek; Committee Member: Parker, Robert G.; Committee Member: Yu, Xingxing.

Thesis (M.S.)--Texas State University--San Marcos, 2008. / Vita. Includes bibliographical references (leaves 50-51). Also available on microfilm.

Made available in DSpace on 2014-06-11T19:26:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-07-01Bitstream added on 2014-06-13T18:30:54Z : No. of bitstreams: 1 carretero_mm_me_sjrp.pdf: 795127 bytes, checksum: 64b07e80db6689945e91fc1c317deb3c (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Em alguns setores, o planejamento da produção envolve dois aspectos: o dimensionamento do tamanho dos lotes e a programação da produção (sequenciamento dos lotes). O primeiro problema consiste em determinar o tamanho dos lotes de produção de cada item a ser produzido em uma ou mais máquinas em cada período ao longo de um horizonte de planejamento finito. O segundo problema consiste em encontrar a ordem em que os lotes devem ser produzidos em um dado conjunto de máquinas. Estes dois aspectos do planejamento da produção podem ser tratados de forma independente: em um estágio é resolvido o problema de dimensionamento dos lotes e no outro, realizado antes ou depois, é resolvido o problema de seqüenciamento. No entanto, uma tendência recente na literatura são trabalhos que apresentam modelos matemáticos que capturam simultaneamente as relações entre os dois problemas. Na literatura pode-se encontrar modelos integrados que incluem restrições de eliminação de subrotas, propostas para o Problema do Caixeiro Viajante (PCV), para formular as restrições de sequenciamento. No entanto, alguns dos modelos propostos usam restrições de ordem polinomial que fornecem uma relaxação linear fraca. O objetivo desse trabalho é avaliar o uso de inequações válidas, propostas na literatura, para obtenção de formulações mais fortes para o problema integrado de dimensionamento e sequenciamento da produção. Resultados computacionais usando exemplares aleatórios e exemplares da literatura mostram que as reformulações propostas são eficientes para cenários em que o modelo original não é eficiente. / Often, the production planning involves the lot sizing and scheduling of items. The first problem is to determine the lot size of each item to be produced in one or more machines in each period over a finite planning horizon. The second problem is to find the order in which the items will be produced. These two aspects of the production planning can be treated independently: in one stage the lot sizing problem is solved, and in the other, that can be executed before or after, the scheduling problem is solved. A recent trend in the literature is to propose mathematical models that capture the relationships between these two problems. In the literature one can find integrated models that include subtour elimination constraints, proposed for the Traveling Salesman Problem, to formulate the scheduling decisions. However, in some of these models, constraints of polynomial order, that provides a weak linear relaxation, are used.The purpose of this study is to evaluate the use of valid inequalities proposed in the literature to obtain stronger formulations to the lot and scheduling problem. Computational results using random instances and instances from the literature show that the proposed formulations have a better performance in scenarios where the original model is not efficient.

Orientador: Luziane Ferreira de Mendonça / Dissertação ( mestrado profissional) - Universidade Estadual de Campionas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-17T15:32:08Z (GMT). No. of bitstreams: 1 Tavora_RogerioCarvalhoMendes_M.pdf: 4616708 bytes, checksum: 3a393a830c9e1127f5694b0a56d9fcd9 (MD5) Previous issue date: 2011 / Resumo: Comemorando os 200 anos de Academia Militar no Brasil a partir de março de 2011, estão previstas várias implementações e melhorias na estrutura de visitação da AMAN que, consequentemente, vão gerar um aumento substancial no número de grupos visitantes no ano de seu bicentenário. Diante dos fatos percebe-se a necessidade de um modelo matemático eficiente cuja finalidade seja permitir aos grupos visitantes percorrerem trajetos otimizados, ou seja, que passem pelos pontos principais de visitação no menor tempo e distância possíveis. O modelo matemático a ser adotado neste trabalho é o Problema do Caixeiro Viajante (Traveling Salesman Problem - TSP), um clássico da Otimização Combinatória pertencente 'a classe de problemas NP - difícil, que já possui eficientes algoritmos desenvolvidos. Serão utilizadas heurísticas próprias para a resolução do TSP com o intuito de se obter numericamente itinerários ótimos de visitação, considerando os diferentes grupos visitantes e suas dificuldades de acesso, dentre outras particularidades. / Abstract: Celebrating 200 years of the Military Academy in Brazil from March 2011, provides a lot of implementations and improvements in the structure of visitation of AMAN, consequently, will generate substantial growth in the number of visiting groups in the year of its bicentennial. Given the facts we see the need for an efficient mathematical model whose purpose is to allow visitors to wander paths optimized groups, ie passing through the main points of visitation in the shortest possible time and distance. The mathematical model to be adopted in this work is TSP (Traveling Salesman Problem - TSP), a classic combinatorial optimization class of problems NP - hard, that have already efficient algorithms. We will use own heuristics for solving the TSP in order to obtain numerically optimal routes for visitors, considering the various visiting groups and their difficulties of access, among other features. / Mestrado / Mestre em Matemática