Strategic network design has significant impacts on the operational performance of transportation and telecommunications industries. The corresponding networks are typically characterized by a multicommodity ow structure where a commodity is defined by a unique origin-destination pair and an associated amount of ow. In turn, multicommodity network design and hub location models are commonly employed when designing strategic networks in transportation and telecommunications applications. In this dissertation, these two modeling approaches are integrated and generalized to address important requirements in network design for truckload transportation and long-distance telecommunications networks. To this end, we first introduce a cost effective relay network design model and then extend this base model to address the specific characteristics of these applications. The base model determines relay point (RP) locations where the commodities are relayed from their origins to destinations. In doing this, we explicitly consider distance constraints for the RP-RP and nonRPRP linkages. In truckload transportation, a relay network (RP-network) can be utilized to decrease drivers' driving distances and keep them within their domiciles. This can potentially help alleviate the high driver turnover problem. In this case, the percentage circuitry, load-imbalance, and link-imbalance constraints are incorporated into the base model to control related performance metrics that are affected by the distance constraints. When compared to the networks from other modeling approaches, the RP-network is more effective in controlling drivers' tour lengths and capable of controlling the empty mileage to low levels without adding a large amount of additional travel distance. In telecommunications, an RP-network can be beneficial in long-distance data transfers where the signals' delity must be improved/regenerated at RPs along their travel paths. For this setting, we extend the base model to include fixed link setup costs and capacities. From our computational results, our models provide better network configuration that is cost effective and facilitates a better service quality (shorter delays and better connectivity). Concerning methodology, we develop effcient exact solution algorithms based on Benders decomposition, Lagrangean decomposition, and Lagrangean relaxation. The performance of the typical solution frameworks are enhanced via numerous accelerating techniques to allow the solution of large-sized instances in reduced solution times. The accelerating techniques and solution approaches are transferable to other network design problem settings with similar characteristics.

O trabalho apresentado nesta dissertação é referente ao estudo da evacuação de pessoas, mais especificamente, a evacuação predial faseada. O objetivo é determinar, para instâncias de até 25 andares, os instantes de liberação de cada grupo de pessoas, a fim de minimizar o tempo total de evacuação do edifício. No entanto, a determinação destes instantes deve considerar o risco ao qual os diferentes grupos estão submetidos, priorizando a evacuação do andar afetado. Além disso, os conflitos de diferentes grupos por espaço nas rotas de evacuação também devem ser evitados, já que, são nessas situações que acontecem grande parte dos acidentes. Para atingir tal objetivo, foi elaborado um modelo matemático de programação linear inteira. Devido à alta complexidade do modelo, fez-se necessária a aplicação de métodos heurísticos para a obtenção de soluções. Dessa maneira, foram desenvolvidas uma heurística de busca baseada em GRASP e uma heurística lagrangeana. Apesar da heurística lagrangeana atestar a qualidade da solução (a partir da comparação do resultado obtido com o limitante inferior), a heurística de busca mostrou-se mais adequada para o problema, pois forneceu resultados de qualidade com pouco esforço computacional. / This dissertation studies the evacuation of people, more specifically, building phased evacuation. The objective of this study is to determine, for buildings of up to 25 floors, in which instants each group of people has to be released, in order to minimize the total evacuation time. Furthermore, the determination of these instants has to consider the risk to which each group of people is submitted, thus the affected floor has to be the first group to be released. In addition, conflicts for space between groups should be avoided, since such situations increase the occurrences of accidents. To achieve this goal, an integer linear programming model was designed. Due to the high complexity of the model, it was necessary to apply heuristics to obtain solutions for some instances. Therefore, a search heuristic based on GRASP and a lagrangian heuristic were developed. Despite the fact that the lagrangian heuristic attests to the quality of the solution (when it is compared to the lower bound), the search heuristic was considered more suitable for this problem because it provided quality results with lower computational efforts.

While the concept of regional organization (regionalization) of perinatal care aimed at reducing perinatal mortality has remained at the agenda of developed countries since 1970&rsquo / s, Turkey is one of the countries that does not have such a system yet. In this study, a three-level hierarchical location-allocation model is developed for the regionalization of perinatal care in an attempt to have a better distribution of maternal and perinatal health care services in Turkey. Since the mathematical model developed is difficult to solve in a reasonable time, we propose three heuristic approaches: top-down, modified top-down and Lagrangean relaxation based heuristics. These heuristics are computationally tested on a set of problem instances for networks ranging from 10 to 737 vertices. A significant result is that Lagrangean relaxation based heuristic outperforms the other two heuristics in terms of solution quality. In most of the test problems, the modified top-down heuristic outperforms the top-down heuristic in terms of solution quality. Using the proposed approaches, we solve a real life problem corresponding to the Eastern and South Eastern Anatolian Regions (the East Region) of Turkey.

In this study, we consider a capacity allocation problem in flexible manufacturing systems. We assume time and tool magazine capacities on the Numerical Controlled (NC) machines and limited number of available tools. Our problem is to allocate the available capacity of the NC machines to the required demand of the operations, so as to maximize the total weight of operation assignments. We formulate the problem as a Mixed Integer Linear Program and show that it is NP-hard in the strong sense. We solve the moderate-sized problems optimally by the available Integer Programming software. We also develop Lagrangean relaxation based upper bounds and several heuristic procedures. Our computational results have revealed that the Lagrangean upper bounds are very close to optimal solutions and the heuristic procedures produce near optimal solutions in very small solution times even when the problem sizes are large.

DIAS, Fábio Carlos Sousa. Algoritmos para o problema de localização simples baseados nas formulações clássica e canônica. 2008. 89 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2008. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-11T15:12:03Z No. of bitstreams: 1 2008_dis_fcsdias.pdf: 533140 bytes, checksum: 547c9cf8d771e2646884c423f5a39936 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-15T15:32:35Z (GMT) No. of bitstreams: 1 2008_dis_fcsdias.pdf: 533140 bytes, checksum: 547c9cf8d771e2646884c423f5a39936 (MD5) / Made available in DSpace on 2016-07-15T15:32:35Z (GMT). No. of bitstreams: 1 2008_dis_fcsdias.pdf: 533140 bytes, checksum: 547c9cf8d771e2646884c423f5a39936 (MD5) Previous issue date: 2008 / In this work, we study the Simple Plant Location Problem (SPLP). Using its classical mathematical programming formulation and another recently proposed formulation, we develop several algorithms to …nd lower and upper bounds for the problem as well as branch-and-bound algorithms. With the classical formulation, such bounds are obtained via the data correction method and dominance criteria between …xed and transportation costs. We propose a projection of this formulation that has shown to be computationally atractive. Using the new formulation, we propose and prove the correctness of several iterative procedures that attempt to …nd an optimal solution to the problem by solving a sequence of parametric sub-problems, each one obtained by removing some variables and constraints of the original formulation. At each iteration of this process, we can obtain lower and upper bounds. We also apply Lagrangean relaxation to this new formulation in order to get other bounds. We consider several possibilities of relaxing the constraints. In addition, we develop branch-and-bound algorithms based on both formulations and the obtained bounds. We evaluate the computational e¢ ciency of all proposed algorithms with hard test instances from the literature. Computational results are reported and comparisons with other algorithms from the literature are carried out. / Neste trabalho, estudamos o problema de localização simples (SPLP - Simple Plant Location Problem). Usando a formulação matemática clássica e uma outra formulação proposta recentemente, desenvolvemos vários algoritmos para encontrar limites inferiores e superiores, bem como algoritmos tipo branch-and-bound. Com a formulação clássica, tais limites são obtidos utilizando o método de correção de dados e critérios de dominância entre os custos …xos e de transporte. Propomos uma projeção dessa formulação, que se mostrou computacionalmente atrativa. Usando a nova formulação propomos e mostramos a corretude de vários procedimentos iterativos que procuram encontrar uma solução para o problema, resolvendo uma seqüência de subproblemas paramétricos obtidos com a remoção de variáveis e restrições da formulação original. Em cada iteração desse processo, podemos gerar limites inferiores e superiores. Aplicamos ainda relaxação lagrangeana a essa nova formulação para obter outros limites. Analisamos várias possibilidades de relaxação das restrições. Desenvolmento também algoritmos branch-and-bound baseados em ambas as formulações e nos limites obtidos. Avaliamos a e…ciência computacional de todos os algoritmos com instâncias de teste difíceis, disponíveis na literatura. Resultados computacionais e comparações com outros algoritmos da literatura são reportados.

O trabalho apresentado nesta dissertação é referente ao estudo da evacuação de pessoas, mais especificamente, a evacuação predial faseada. O objetivo é determinar, para instâncias de até 25 andares, os instantes de liberação de cada grupo de pessoas, a fim de minimizar o tempo total de evacuação do edifício. No entanto, a determinação destes instantes deve considerar o risco ao qual os diferentes grupos estão submetidos, priorizando a evacuação do andar afetado. Além disso, os conflitos de diferentes grupos por espaço nas rotas de evacuação também devem ser evitados, já que, são nessas situações que acontecem grande parte dos acidentes. Para atingir tal objetivo, foi elaborado um modelo matemático de programação linear inteira. Devido à alta complexidade do modelo, fez-se necessária a aplicação de métodos heurísticos para a obtenção de soluções. Dessa maneira, foram desenvolvidas uma heurística de busca baseada em GRASP e uma heurística lagrangeana. Apesar da heurística lagrangeana atestar a qualidade da solução (a partir da comparação do resultado obtido com o limitante inferior), a heurística de busca mostrou-se mais adequada para o problema, pois forneceu resultados de qualidade com pouco esforço computacional. / This dissertation studies the evacuation of people, more specifically, building phased evacuation. The objective of this study is to determine, for buildings of up to 25 floors, in which instants each group of people has to be released, in order to minimize the total evacuation time. Furthermore, the determination of these instants has to consider the risk to which each group of people is submitted, thus the affected floor has to be the first group to be released. In addition, conflicts for space between groups should be avoided, since such situations increase the occurrences of accidents. To achieve this goal, an integer linear programming model was designed. Due to the high complexity of the model, it was necessary to apply heuristics to obtain solutions for some instances. Therefore, a search heuristic based on GRASP and a lagrangian heuristic were developed. Despite the fact that the lagrangian heuristic attests to the quality of the solution (when it is compared to the lower bound), the search heuristic was considered more suitable for this problem because it provided quality results with lower computational efforts.

The purpose of this research is to develop a model and solution methodology for selecting and scheduling timely and cost-effective maintenance, rehabilitation, and reconstruction activities (M & R) for each pavement section in a highway network and allocating the funding levels through a finite multi-period horizon within the constraints imposed by budget availability in each period, frequency availability of activities, and specified minimum pavement quality requirements. M & R is defined as a chronological sequence of reconstruction, rehabilitation, and major/minor maintenance, including a "do nothing" activity. A procedure is developed for selecting an M & R activity for each pavement section in each period of a specified extended planning horizon. Each activity in the sequence consumes a known amount of capital and generates a known amount of effectiveness measured in pavement quality. The effectiveness of an activity is the expected value of the overall gains in pavement quality rating due to the activity performed on a highway network over an analysis period. It is assumed that the unused portion of the budget for one period can be carried over to subsequent periods. Dynamic Programming (DP) and Branch-and-Bound (B-and-B) approaches are combined to produce a hybrid algorithm for solving the problem under consideratioin. The algorithm is essentially a DP approach in the sense that the problem is divided into smaller subproblems corresponding to each single period problem. However, the idea of fathoming partial solutions that could not lead to an optimal solution is incorporated within the algorithm to reduce storage and computational requirements in the DP frame using the B-and-B approach. The imbedded-state approach is used to reduce a multi-dimensional DP to a one-dimensional DP. For bounding at each stage, the problem is relaxed in a Lagrangean fashion so that it separates into longest-path network model subproblems. The values of the Lagrangean multipliers are found by a subgradient optimization method, while the Ford-Bellman network algorithm is employed at each iteration of the subgradient optimization procedure to solve the longest-path network problem as well as to obtain an improved lower and upper bound. If the gap between lower and upper bound is sufficiently small, then we may choose to accept the best known solutions as being sufficiently close to optimal and terminate the algorithm rather than continue to the final stage.

In this study, a Lagrangean heuristic based on Lagrangean relaxation and subgradient optimization is proposed for the two-stage modular capacitated facility location problem. The objective is to minimize the cost of locating and operating plants and warehouses, plus the cost of transporting goods at both echelons to satisfy the demand of customers. The difference of our study from the two-stage capacitated facility location problem is the existence of multiple capacity levels as a candidate for each plant in the problem. Each capacity level has a minimum production capacity which has to be satisfied to open the relevant capacity level. Obviously, a single capacity level can be selected for an opened facility location. In the second echelon, the warehouses are capacitated and have unique fixed and variable costs for opening and operating. Multiple sourcing is allowed in both transportation echelons. Firstly, we develop a mixed integer linear programming model for the two-stage modular capacitated facility location problem. Then we develop a Lagrangean heuristic to solve the problem efficiently. Our Lagrangean heuristic consists of three main components: Lagrangean relaxation, subgradient optimization and a primal heuristic. Lagrangean relaxation is employed for obtaining the lower bound, subgradient optimization is used for updating the Lagrange multipliers at each iteration, and finally a three-stage primal heuristic is created for generating the upper bound solutions. At the first stage of the upper bound heuristic, global feasibility of the plants and warehouses is inspected and a greedy heuristic is executed, if there is a global infeasibility. At the next stage, an allocation heuristic is used to assign customers to warehouses and warehouses to plants sequentially. At the final stage of the upper bound heuristic, local feasibilities of the plants are investigated and infeasible capacity levels are adjusted if necessary. In order to show the efficiency of the developed heuristic, we have tested our heuristic on 280 problem instances generated randomly but systematically. The results of the experiments show that the developed heuristic is efficient and effective in terms of solution quality and computational effort especially for large instances.

The purpose of this research is to develop a model and solution methodology for selecting and scheduling timely and cost-effective maintenance, rehabilitation, and reconstruction activities (M & R) for each pavement section in a highway network and allocating the funding levels through a finite multi-period horizon within the constraints imposed by budget availability in each period, frequency availability of activities, and specified minimum pavement quality requirements. M & R is defined as a chronological sequence of reconstruction, rehabilitation, and major/minor maintenance, including a "do nothing" activity. A procedure is developed for selecting an M & R activity for each pavement section in each period of a specified extended planning horizon. Each activity in the sequence consumes a known amount of capital and generates a known amount of effectiveness measured in pavement quality. The effectiveness of an activity is the expected value of the overall gains in pavement quality rating due to the activity performed on a highway network over an analysis period. It is assumed that the unused portion of the budget for one period can be carried over to subsequent periods. Dynamic Programming (DP) and Branch-and-Bound (B-and-B) approaches are combined to produce a hybrid algorithm for solving the problem under consideratioin. The algorithm is essentially a DP approach in the sense that the problem is divided into smaller subproblems corresponding to each single period problem. However, the idea of fathoming partial solutions that could not lead to an optimal solution is incorporated within the algorithm to reduce storage and computational requirements in the DP frame using the B-and-B approach. The imbedded-state approach is used to reduce a multi-dimensional DP to a one-dimensional DP. For bounding at each stage, the problem is relaxed in a Lagrangean fashion so that it separates into longest-path network model subproblems. The values of the Lagrangean multipliers are found by a subgradient optimization method, while the Ford-Bellman network algorithm is employed at each iteration of the subgradient optimization procedure to solve the longest-path network problem as well as to obtain an improved lower and upper bound. If the gap between lower and upper bound is sufficiently small, then we may choose to accept the best known solutions as being sufficiently close to optimal and terminate the algorithm rather than continue to the final stage.

This study is concerned with the inventory routing problem with deterministic, dynamic demand and order-up-to level inventory policy. The problem mainly arises in the supply chain management context. It incorporates simultaneous decision making on inventory management and vehicle routing with the purpose of gaining advantage from coordinated decisions. An integrated mathematical model that represents the features of the problem is presented. Due to the magnitude of the model, lagrangean relaxation solution procedures that identify upper bounds and lower bounds for the problem are developed. Satisfactory computational results are obtained with the solution procedures suggested on the test instances taken from the literature.