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1
Aslan, Murat. "The Cardinality Constrained Multiple Knapsack Problem". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610131/index.pdf.
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The classical multiple knapsack problem selects a set of items and assigns each to one of the knapsacks so as to maximize the total profit. The knapsacks have limited capacities. The cardinality constrained multiple knapsack problem assumes limits on the number of items that are to be put in each knapsack, as well. Despite many efforts on the classical multiple knapsack problem, the research on the cardinality constrained multiple knapsack problem is scarce.
In this study we consider the cardinality constrained multiple knapsack problem. We propose heuristic and optimization procedures that rely on the optimal solutions of the linear programming relaxation problem. Our computational results on the large-sized problem instances have shown the satisfactory performances of our algorithms.
2
Suri, Bharath. "Accelerating the knapsack problem on GPUs". Thesis, Linköpings universitet, ESLAB - Laboratoriet för inbyggda system, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70406.
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The knapsack problem manifests itself in many domains like cryptography, financial domain and bio-informatics. Knapsack problems are often inside optimization loops in system-level design and analysis of embedded systems as well. Given a set of items, each associated with a profit and a weight, the knapsack problem deals with how to choose a subset of items such that the profit is maximized and the total weight of the chosen items is less than the capacity of the knapsack. There exists several variants and extensions of this knapsack problem. In this thesis, we focus on the multiple-choice knapsack problem, where the items are grouped into disjoint classes. However, the multiple-choice knapsack problem is known to be NP-hard. While many different heuristics and approximation schemes have been proposed to solve the problem in polynomial-time, such techniques do not return the optimal solution. A dynamic programming algorithm to solve the problem optimally is known, but has a pseudo-polynomial running time. This leads to high running times of tools in various application domains where knapsack problems must be solved. Many system-level design tools in the embedded systems domain, in particular, would suffer from high running when such a knapsack problem must be solved inside a larger optimization loop. To mitigate the high running times of such algorithms, in this thesis, we propose a GPU-based technique to solve the multiple-choice knapsack problem. We study different approaches to map the dynamic programming algorithm on the GPU and compare their performance in terms of the running times. We employ GPU specific methods to further improve the running times like exploiting the GPU on-chip shared memory. Apart from results on synthetic test-cases, we also demonstrate the applicability of our technique in practice by considering a case-study from system-level design. Towards this, we consider the problem of instruction-set selection for customizable processors.
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Islam, Mohammad Tauhidul y University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for minimum knapsack problem". Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2009, 2009. http://hdl.handle.net/10133/1304.
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Knapsack problem has been widely studied in computer science for years. There exist several
variants of the problem, with zero-one maximum knapsack in one dimension being
the simplest one. In this thesis we study several existing approximation algorithms for the
minimization version of the problem and propose a scaling based fully polynomial time approximation
scheme for the minimum knapsack problem. We compare the performance of
this algorithm with existing algorithms. Our experiments show that, the proposed algorithm
runs fast and has a good performance ratio in practice. We also conduct extensive experiments
on the data provided by Canadian Pacific Logistics Solutions during the MITACS
internship program.
We propose a scaling based e-approximation scheme for the multidimensional (d-dimensional)
minimum knapsack problem and compare its performance with a generalization of a greedy
algorithm for minimum knapsack in d dimensions. Our experiments show that the e-
approximation scheme exhibits good performance ratio in practice.x, 85 leaves ; 29 cm
4
Becker, Henrique. "The unbounded knapsack problem : a critical review". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163413.
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Uma revisão dos algoritmos e conjuntos de instâncias presentes na literatura do Problema da Mochila com Repetições (PMR) é apresentada nessa dissertação de mestrado. Os algoritmos e conjuntos de instâncias usados são brevemente descritos nesse trabalho, afim de que o leitor tenha base para entender as discussões. Algumas propriedades bem conhecidas e específicas do PMR, como a dominância e a periodicidade, são explicadas com detalhes. O PMR é também superficialmente estudado no contexto de problemas de avaliação gerados pela abordagem de geração de colunas aplicada na relaxação contínua do Bin Packing Problem (BPP) e o Cutting Stock Problem (CSP). Múltiplos experimentos computacionais e comparações são realizadas. Para os conjuntos de instâncias artificiais mais recentes da literatura, um simples algoritmo de programação dinâmica, e uma variante do mesmo, parecem superar o desempenho do resto dos algoritmos, incluindo aquele que era estado-da-arte. O modo que relações de dominância é aplicado por esses algoritmos de programação dinâmica têm algumas implicações para as relações de dominância previamente estudadas na literatura. O autor dessa dissertação defende a tese de que a escolha dos conjuntos de instâncias artificiais definiu o que foi considerado o melhor algoritmo nos trabalhos anteriores. O autor dessa dissertação disponibilizou publicamente todos os códigos e conjuntos de instâncias referenciados nesse trabalho.A review of the algorithms and datasets in the literature of the Unbounded Knapsack Problem (UKP) is presented in this master's thesis. The algorithms and datasets used are brie y described in this work to provide the reader with basis for understanding the discussions. Some well-known UKP-speci c properties, such as dominance and periodicity, are described. The UKP is also super cially studied in the context of pricing problems generated by the column generation approach applied to the continuous relaxation of the Bin Packing Problem (BPP) and Cutting Stock Problem (CSP). Multiple computational experiments and comparisons are performed. For the most recent arti cial datasets in the literature, a simple dynamic programming algorithm, and its variant, seems to outperform the remaining algorithms, including the previous state-of-the-art algorithm. The way dominance is applied by these dynamic programming algorithms has some implications for the dominance relations previously studied in the literature. In this master's thesis we defend that choosing sets of arti cial instances has de ned what was considered the best algorithm in previous works. We made available all codes and datasets referenced in this master's thesis.
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McMillen, Brandon. "The Knapsack Problem, Cryptography, and the Presidential Election". Youngstown State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1340654189.
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Chen, Yuning. "Hybrid Metaheuristics for Quadratic Knapsack Problems Iterated responsive threshold search for the quadratic multiple knapsack problem A “reduce and solve” approach for the multiple-choice multidimensional knapsack problem". Thesis, Angers, 2016. http://www.theses.fr/2016ANGE0062.
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Cette thèse considère quatre problèmes d’optimisation combinatoire connus sous le nom de Problèmes de Sac-à-Dos Quadratiques : le problème de sac-à-dos quadratique (QSP), le problème de sac-à-dos multiple quadratique (QMSP), le problème de sac-à-dos multiple quadratique généralisé (GQMSP) et le nouveau problème de sac-à-dos multiple quadratique bi-objectif (BO-QMSP) présenté dans cette thèse. Parmi eux, le QSP est le modèle de base tandis que les trois autres introduisent des contraintes ou des fonctions objectives supplémentaires. Ces problèmes ont de nombreuses applications pratiques. Étant donné qu’ils appartiennent à la famille NP-difficile, il est difficile de les résoudre dans le cas général. Pour cette raison, cette thèse est consacrée à la création d’approches métaheuristiques hybrides efficaces pour résoudre ces quatre problèmes difficiles. Plus précisément, nous développons une approche itérative d’exploration hyperplane pour le QSP, deux algorithmes hybrides ("Iterative responsive threshold search" et "Evolutionary path relinking") pour le QMSP, un algorithme mémétique pour le GQMSP et une approche hybride en deux étapes pour le BO-QMSP. Ces algorithmes partagent certains ingrédients fondamentaux (e.g., les opérateurs de mouvement de base et les heuristiques gloutonnes) qui avec quelques adaptations sont généralement applicables à d’autres problèmes de sac-à-dos quadratiques. Ils possèdent également un certain nombre de conceptions spécifiques aux problèmes étudiés. Tous les algorithmes ont été expérimentalement démontrés être en mesure de rivaliser favorablement avec les méthodes de l’état de l’artThis thesis considers four combinatorial optimization problems known under the name Quadratic Knapsack Problems: the quadratic (single) knapsack problem (QKP), the quadratic multiple knapsack problem (QMKP), the generalized quadratic multiple knapsack problem (GQMKP) and the new bi-objective quadratic multiple knapsack problem (BO-QMKP) introduced in this thesis. Among them, the QKP is the most basic model while the other three generalize upon it by introducing additional constraints or objective functions. These problems have a wide range of practical applications. Given that they belong to the NP-hard family, it is computationally difficult to solve them in the general case. For this reason, this thesis is devoted to developing effective hybrid metaheuristic approaches to tackle these four challenging problems. Specifically, we develop an iterated hyperplane exploration approach for the QKP, two hybrid metaheuristic algorithms (iterated responsive threshold search and evolutionary path relinking) for the QMKP, an effective memetic algorithm for the GQMKP and a hybrid two-stage approach for the BO-QMKP. These algorithms share some fundamental ingredients (e.g., move operators and greedy heuristics) which with small adaptations are generally applicable to other Quadratic Knapsack Problems. They also possess a number of problem-specific designs. All algorithms were experimentally demonstrated to be able to compete favourably with state-of-the-art methods
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Yang, Yanchun Bulfin Robert L. "Knapsack problems with setup". Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Dissertations/YANG_YANCHUN_31.pdf.
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Dreiding, Rebecca. "Allocating Homeland Security Screening Resources Using Knapsack Problem Models". VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2289.
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Since the events of September 11, 2001, the federal government is focused on homeland security and the fight against terrorism. This thesis addresses the idea of terrorist groups smuggling nuclear weapons through the borders of the United States. Security screening decisions are analyzed within maritime and aviation domains using discrete optimization models, specifically knapsack problems. The focus of the maritime chapters involves a risk-based approach for prescreening intelligence classifications for primary and secondary screening decisions given limited budget and resources. Results reveal that screening decisions are dependent on prescreening classification and the efficacy of the screening technologies. The screening decisions in the aviation security chapter highlight different performance measures to quantify the effectiveness of covering flights with the intent of covering targets. Results reveal that given scarce resources, such as screening devices capacities and budget, flights and targets can be covered with minimal expense to the system.
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Talamantes, Alonso. "Lifted equality cuts for the multiple knapsack equality problem". Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35516.
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Master of ScienceDepartment of Industrial and Manufacturing Systems Engineering
Todd W. Easton
Integer programming is an important discipline in operation research that positively impacts society. Unfortunately, no algorithm currently exists to solve IP's in polynomial time. Researchers are constantly developing new techniques, such as cutting planes, to help solve IPs faster. For example, DeLissa discovered the existence of equality cuts limited to zero and one coefficients for the multiple knapsack equality problem (MKEP). An equality cut is an improper cut because every feasible point satisfies the equality. However, such a cut always reduces the dimension of the linear relaxation space by at least one. This thesis introduces lifted equality cuts, which can have coefficients greater than or equal to two. Two main theorems provide the conditions for the existence of lifted equalities. These theorems provide the foundation for The Algorithm of Lifted Equality Cuts (ALEC), which finds lifted equality cuts in quadratic time. The computational study verifies the benefit of lifted equality cuts in random MKEP instances. ALEC generated millions of lifted equality cuts and reduced the solution time by an average of 15%. To the best of the author's knowledge, ALEC is the first algorithm that has found over 30.7 million cuts on a single problem, while reducing the solving time by 18%.
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Escobar, Alvaro E. "The multidimensional 0-1 knapsack problem an empirical analysis /". Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1990. http://www.kutztown.edu/library/services/remote_access.asp.
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Mouser, Philip Samuel. "Decomposition methods for the quadratic assignment and multidimensional knapsack problem". Thesis, University of East Anglia, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437831.
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Krishnan, Bharath Kumar. "A multiscale approximation algorithm for the cardinality constrained knapsack problem". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34612.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (leaves 83-86).
I develop a multiscale approximation algorithm for the cardinality constrained knapsack problem. The algorithm consists of three steps: a rounding and reduction step where a hierarchical representation of the problem data ranging from coarse to fine is generated, a solution step where a coarse solution is computed, and a refinement step where the accuracy of the solution is improved by refining the problem representation. I demonstrate that the algorithm is fully polynomial with a runtime complexity that improves upon the previous best known fully polynomial approximation scheme. Through an extensive computational study, I show that the running times of the algorithm is less than or equal to that of a commercial integer programming package with little loss in solution accuracy.
by Bharath Kumar Krishnan.
Ph.D.
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Chakka, Varun Raj. "Models and algorithms for the Multiple Knapsack problem with conflicts". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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In the Multiple Knapsack Problem with Conflicts, we are given a set of items, each with its own weight and profit, as well as a set of multiple knapsacks, each with its own capacity. The goal is to maximize the total profit of the items inserted in the knapsacks, while respecting the knapsack capacity and the incompatibility constraints between items..
The thesis is based on Basnet2018 research article. I developed some heuristic algorithms and tested them with various instances with a maximum case of 500 items and 15 knapsacks. The results of the computations are reported.
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PALMEIRA, MARCIO DE MORAES. "ALGORITHM RELAX-AND-CUT FOR THE 0-1 QUADRATIC KNAPSACK PROBLEM". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7404@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOConsideramos o Problema Quadrático da Mochila 0-1 (QKP), que consiste em maximizar uma função booleana quadrática sujeito a uma restrição de capacidade linear. O problema possui aplicações em várias áreas, como por exemplo, telecomunicações. engenharia financeira, problemas de localização e teoria dos grafos (clique máximo). Propomos um algoritmo de Branch-and-Bound para resolver exatamente QKP, baseado em Relaxação Lagrangeana. Inicialmente, linearizamos a formulação do problema acima, e em seguida, aplicamos a técnica de relax-and-cut dinamicamente à relaxação contínua do problema, utilizando algumas classes de desigualdades válidas. O método do subgradiente é usado neste processo. Propomos também uma nova heurística primal para QKP, que obtém soluções melhores do que heurísticas propostas anteriormente, encontrando a solução ótima em todas as instâncias que consideramos. A boa qualidade dos limites superior e inferior é traduzida em gap`s pequenos no nó raiz da árvore de enumeração (em geral, menor do que 1%, inclusive para instâncias difíceis). Isto, aliado a testes de fixação de variáveis, permite resolver exatamente QKP em poucos nós da árvore de enumeração. Introduzimos uma maneira de gerar instâncias aleatórias mais difíceis do que as instâncias na literatura. Apresentamos resultados computacionais para instâncias geradas aleatoriamente (instâncias da literatura, e as novas instâncias mais difíceis) para QKP de tamanhos e densidades diferentes; e também para instâncias conhecidas do problema de clique máxima.
We consider the 0-1 Quadratic Knapsack Problem (QKP), which consists of maximizing a quadratic Boolean function subject to a linear capacity constraint. The problem has applications in several areas such as telecommunications, financial engineering, location problems, graph theory (Max Clique). We propose a Branch-and-Bound algorithm to solve the QKP to optimality based on lagrangian Relaxation. Initially, we linearize the formulation of the problem given above and then we relax-and-cut dinamicaly its continous relaxation using a few classes of valid inequalities. In the process the Subgradient Method is applied. We also propose a new primal heuristic for the QKP that has improved upon previous approaches, and finds an optimal solution for all of the instances we considered. The good quality of our upper and lower bounds is translated into small gaps at the root node of the enumeration tree (usually below 1%, even for difficult instances). That, coupled with tests for fixing variables, allowed optimality to be proven within only a few nodes of the enumeration tree. We provide a way to randomly generate instances of the QKP harder than those in the literature. We report computational results for randomly generated instances (the ones in the literature and the new harder ones) of QKP with different densities and sizes; and also for Known instances of Max Clique problems.
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Tu, Zhiqi. "Enhancements of the Non-linear Knapsack Cryptosystem". Thesis, University of Canterbury. Computer Science and Software Engineering, 2006. http://hdl.handle.net/10092/1080.
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Nowadays all existing public key cryptosystems are classified into three categories relied on different mathematical foundations. The first one is based on the difficulty of factoring the product of two big prime numbers. The representatives are the RSA and the Rabin cryptosystems. The second one such as the ElGamal cryptosystem is based on the discrete logarithm problem. The last one is based on the NP-completeness of the knapsack problem. The first two categories survived crypto attacks, whereas the last one was broken and there has been no attempt to use such a cryptosystem. In order to save the last category, Kiriyama proposed a new public key cryptosystem based on the non-linear knapsack problem, which is an NP-complete problem. Due to the non-linear property of the non-linear knapsack problem, this system resists all known attacks to the linear knapsack problem. Based on his work, we extend our research in several ways. Firstly, we propose an encrypted secret sharing scheme. We improve the security of shares by our method over other existing secret sharing schemes. Simply speaking, in our scheme, it would be hard for outsiders to recover a secret even if somehow they could collect all shares, because each share is already encrypted when it is generated. Moreover, our scheme is efficient. Then we propose a multiple identities authentication scheme, developed on the basis of the non-linear knapsack scheme. It verifies the ownership of an entity's several identities in only one execution of our scheme. More importantly, it protects the privacy of the entities from outsiders. Furthermore, it can be used in resource-constrained devices due to low computational complexity. We implement the above schemes in the C language under the Linux system. The experimental results show the high efficiency of our schemes, due to low computational complexity of the non-linear knapsack problem, which works as the mathematical foundation of our research.
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Kilincli, Taskiran Gamze. "An Improved Genetic Algorithm for Knapsack Problems". Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1270598986.
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Cherbaka, Natalie Stanislaw. "Solving Single and Multiple Plant Sourcing Problems with a Multidimensional Knapsack Model". Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/29802.
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This research addresses sourcing decisions and how those decisions can affect the management of a company's assets. The study begins with a single-plant problem, in which one facility chooses, from a list of parts, which parts to bring in-house. The selection is based on maximizing the value of the selected parts, while remaining within the plant's capacity. This problem is defined as the insourcing problem and modeled as a multidimensional knapsack problem (MKP). The insourcing model is extended to address outsourcing and multiple plants. This multi-plant model, also modeled as an MKP, enables the movement of parts from one plant to another and consideration of a company-wide objective function (as opposed to a single-plant objective function as in the insourcing model).
The sourcing problem possesses characteristics that distinguish it from the standard MKP. One such characteristic is what we define as multiple attributes. To understand the multiple attribute characteristic, we compare the various dimensions in the multidimensional knapsack problem. A classification is given for an MKP as either having a single attribute (SA) or multiple attributes (MA). Mathematically, the problems of each attribute classification can be modeled in the same way with simply a different interpretation of the knapsack constraints. However, experimentation indicates that the MA-MKP is more difficult to solve than the SA-MKP. For small problems, with 100 variables and 5 constraints, the CPU time required to find the optimal solution for MA-MKP to SA-MKP problems has a ratio of 32:1.
To determine effective methods for addressing the MA-MKP, standard mixed integer programming techniques are tested. The results of this testing are that the exact approaches are not successful in dramatically reducing the solution time to the level of the SA problems. However, a simple heuristic that performs very well on the MA-MKP is presented. The heuristic utilizes variations on the benefit-to-cost ratio and strongest surrogate constraints. The results from experimentation for MA-MKP problem sets, generated using the methods for standard MKP test data sets in the literature, are presented and indicate that the heuristic performs well and improves with larger problems. The average gap between the heuristic solution and the optimal solution is 1.39% for 200-part problems and is reduced to 0.69% when the size of the problem is increased to 298 parts.
Although the MA characteristic reflects the sourcing problem, the actual data used in the eperimentation is generated with techniques presented in the literature for standard MKP test problems. Therefore, to more accurately represent the sourcing problem, industry data from a manufacturing facility is studied to identify further sourcing problem characteristics. As a result, industry-motivated data sets are generated that reflect the characteristics of industry data, yet maintain the structure of literature data sets to allow for easy comparison. It is found that both industry and industry-motivated data sets, although possessing the MA characteristic, are much easier to solve than SA problems. Indicators of difficulty appear to be the constraint tightness and a measure of the matrix sparsity. The sparsity is a significant factor because industry data tends to be very sparse, while data sets generated in the literature are completely dense. Another interesting result from the industry-motivated data sets with the single-plant problem is the tendency for a facility to prefer currently produced parts over insourcing new parts from outside the facility.
It is not uncommon for a company to have more than one facility with a particular capability. Therefore, the sourcing model is extended to include multiple facilities. With multiple-facilities, effectively all the parts are removed to form one list, and then each part is assigned to one of the facilities or outsourced externally. The multi-facility model is similar to the single-facility model with the addition of assignment constraints enforcing that each part can be assigned to only one facility. Experimentation is performed for the two-, three-, and four-facility models. The problem gets easier to solve as the number of facilities increases. With a greater number of facilities, it is likely that for each part one of facilities will dominate as the best option. Therefore, other solutions can quickly be eliminated and the problem solved more quickly. The two-facility problem is the most difficult; however, the heuristic performs well with an average gap of 0.06% between the heuristic and optimal solutions.
We conclude with a summary on experiences with modeling and solving the sourcing problem for a sheet metal fabrication facility. The model solved for this problem had over 1857 parts with 19 machines, which translates to over 70,000 variables and 38 constraints. Although extremely large compared to problems solved in the literature, this problem was solvable because of the unique structure of industry data. Our work with the facility saved the parent organization up to $4.16M per year and provided a tool that encourages a systematic and quantitative process for evaluating decisions related to sheet metal fabrication capacity.Ph. D.
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Richard, Jean-Philippe P. "Lifted inequalities for 0-1 mixed integer programming". Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/24590.
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Dall'Olio, Giacomo. "Solving the logically constrained multiple choice multiple knapsack problem: A real case". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
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In questa tesi viene analizzato un problema di ottimizzazione proposto da alcuni esercizi commerciali che hanno la necessita` di selezionare e disporre i propri ar- ticoli in negozio. Il problema nasce dall’esigenza di massimizzare il profitto com- plessivo atteso dei prodotti in esposizione, trovando per ognuno una locazione sugli scaffali. I prodotti sono suddivisi in dipartimenti, dai quali solo un ele- mento deve essere selezionato ed esposto. In oltre si prevede la possibilita` di esprimere vincoli sulla locazione e compatibilita` dei prodotti. Il problema risul- tante `e una generalizzazione dei gia` noti Multiple-Choice Knapsack Problem e Multiple Knapsack Problem. Dopo una ricerca esaustiva in letteratura si `e ev- into che questo problema non `e ancora stato studiato. Si `e quindi provveduto a formalizzare il problema mediante un modello di programmazione lineare intera. Si propone un algoritmo esatto per la risoluzione del problema basato su column generation e branch and price. Sono stati formulati quattro modelli differenti per la risoluzione del pricing problem su cui si basa il column generation, per individuare quale sia il piu` efficiente. Tre dei quattro modelli proposti hanno performance comparabili, mentre l’ultimo si `e rivelato piu` inefficiente. Dai risul- tati ottenuti si evince che il metodo risolutivo proposto `e adatto a istanze di dimensione medio-bassa.
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Simms, Amy E. "A Stochastic Approach to Modeling Aviation Security Problems Using the KNAPSACK Problem". Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36806.
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Designers, operators, and users of multiple-device, access
control security systems are challenged by the false alarm,
false clear tradeoff. Given a particular access control
security system, and a prespecified false clear standard,
there is an optimal (minimal) false alarm rate that can be
achieved. The objective of this research is to develop
methods that can be used to determine this false alarm rate.
Meeting this objective requires knowledge of the joint
conditional probability density functions for the security
device responses. Two sampling procedures, the static grid
estimation procedure and the dynamic grid estimation
procedure, are proposed to estimate these functions. The
concept of a system response function is introduced and the
problem of determining the optimal system response function
that minimizes the false alarm rate, while meeting the false
clear standard, is formulated as a decision problem and
proven to be NP-complete. Two heuristic procedures, the
Greedy algorithm and the Dynamic Programming algorithm, are
formulated to address this problem. Computational results
using simulated security data are reported. These results
are compared to analytical results, obtained for a
prespecified system response function form. Suggestions for
future research are also included.Master of Science
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Li, Yaxian. "Lower bounds for integer programming problems". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48959.
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Solving real world problems with mixed integer programming (MIP) involves efforts in modeling and efficient algorithms. To solve a minimization MIP problem, a lower bound is needed in a branch-and-bound algorithm to evaluate the quality of a feasible solution and to improve the efficiency of the algorithm. This thesis develops a new MIP model and studies algorithms for obtaining lower bounds for MIP.
The first part of the thesis is dedicated to a new production planning model with pricing decisions. To increase profit, a company can use pricing to influence its demand to increase revenue, decrease cost, or both. We present a model that uses pricing discounts to increase production and delivery flexibility, which helps to decrease costs. Although the revenue can be hurt by introducing pricing discounts, the total profit can be increased by properly choosing the discounts and production and delivery decisions. We further explore the idea with variations of the model and present the advantages of using flexibility to increase profit.
The second part of the thesis focuses on solving integer programming(IP) problems by improving lower bounds. Specifically, we consider obtaining lower bounds for the multi- dimensional knapsack problem (MKP). Because MKP lacks special structures, it allows us to consider general methods for obtaining lower bounds for IP, which includes various relaxation algorithms. A problem relaxation is achieved by either enlarging the feasible region, or decreasing the value of the objective function on the feasible region. In addition, dual algorithms can also be used to obtain lower bounds, which work directly on solving the dual problems.
We first present some characteristics of the value function of MKP and extend some properties from the knapsack problem to MKP. The properties of MKP allow some large scale problems to be reduced to smaller ones. In addition, the quality of corner relaxation bounds of MKP is considered. We explore conditions under which the corner relaxation is
tight for MKP, such that relaxing some of the constraints does not affect the quality of the lower bounds. To evaluate the overall tightness of the corner relaxation, we also show the worst-case gap of the corner relaxation for MKP.
To identify parameters that contribute the most to the hardness of MKP and further evaluate the quality of lower bounds obtained from various algorithms, we analyze the characteristics that impact the hardness of MKP with a series of computational tests and establish a testbed of instances for computational experiments in the thesis.
Next, we examine the lower bounds obtained from various relaxation algorithms com- putationally. We study methods of choosing constraints for relaxations that produce high- quality lower bounds. We use information obtained from linear relaxations to choose con- straints to relax. However, for many hard instances, choosing the right constraints can be challenging, due to the inaccuracy of the LP information. We thus develop a dual heuristic algorithm that explores various constraints to be used in relaxations in the Branch-and- Bound algorithm. The algorithm uses lower bounds obtained from surrogate relaxations to improve the LP bounds, where the relaxed constraints may vary for different nodes. We also examine adaptively controlling the parameters of the algorithm to improve the performance.
Finally, the thesis presents two problem-specific algorithms to obtain lower bounds for MKP: A subadditive lifting method is developed to construct subadditive dual solutions, which always provide valid lower bounds. In addition, since MKP can be reformulated as a shortest path problem, we present a shortest path algorithm that uses estimated distances by solving relaxations problems. The recursive structure of the graph is used to accelerate the algorithm. Computational results of the shortest path algorithm are given on the testbed instances.
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Hiremath, Chaitr. "New Heuristic And Metaheuristic Approaches Applied To The Multiple-choice Multidimensional Knapsack Problem". Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1203960454.
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Fukasawa, Ricardo. "Single-row mixed-integer programs : theory and computations /". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24660.
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Thesis (Ph.D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.Committee Chair: William J. Cook; Committee Member: Ellis Johnson; Committee Member: George Nemhauser; Committee Member: Robin Thomas; Committee Member: Zonghao Gu
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DeLissa, Levi. "The existence and usefulness of equality cuts in the multi-demand multidimensional knapsack problem". Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17399.
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Master of ScienceDepartment of Industrial and Manufacturing Systems Engineering
Todd Easton
Integer programming (IP) is a class of mathematical models useful for modeling and optimizing many theoretical and industrial problems. Unfortunately, IPs are NP-complete, and many integer programs cannot currently be solved. Valid inequalities and their respective cuts are commonly used to reduce the effort required to solve IPs. This thesis poses the questions, do valid equality cuts exist and can they be useful for solving IPs? Several theoretical results related to valid equalities are presented in this thesis. It is shown that equality cuts exist if and only if the convex hull is not full dimensional. Furthermore, the addition of an equality cut can arbitrarily reduce the dimension of the linear relaxation. In addition to the theory on equality cuts, the idea of infeasibility conditions are presented. Infeasibility conditions introduce a set of valid inequalities whose intersection is the empty set. infeasibility conditions can be used to rapidly terminate a branch and cut algorithm. Applying the idea of equality cuts to the multi-demand multidimensional knapsack problem resulted in a new class of cutting planes named anticover cover equality (ACE) cuts. A simple algorithm, FACEBT, is presented for finding ACE cuts in a branching tree with complexity O(m n log n). A brief computational study shows that using ACE cuts exist frequently in the MDMKP instances studied. Every instance had at least one equality cut, while one instance had over 500,000. Additionally, computationally challenging instances saw an 11% improvement in computational effort. Therefore, equality cuts are a new topic of research in IP that is beneficial for solving some IP instances.
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Dhamankar, Sunil Yashwant. "An efficient group-theoretic algorithm for an assignment problem with a single knapsack constraint". Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055344390.
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Lo, Man-Hon. "Evolution optimization : solving crypto-arithmetic problems and the knapsack problem using adaptive genetic algorithms /". View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202003%20LO.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.Includes bibliographical references (leaves 69-70). Also available in electronic version. Access restricted to campus users.
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McAdoo, Michael John. "Three set inequalities in integer programming". Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/476.
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Hinze, Thomas y Monika Sturm. "A universal functional approach to DNA computing and its experimental practicability". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-100882.
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The rapid developments in the field of DNA computing reflects two substantial questions: 1. Which models for DNA based computation are really universal? 2. Which model fulfills the requirements to a universal lab-practicable programmable DNA computer that is based on one of these models? This paper introduces the functional model DNA-HASKELL focussing its lab-practicability. This aim could be reached by specifying the DNA based operations in accordiance to an analysis of molecular biological processes. The specification is determined by an abstraction level that includes nucleotides and strand end labels like 5'-phosphate. Our model is able to describe DNA algorithms for any NP-complete problem - here exemplified by the knapsacik problem - as well as it is able to simulate some established mathematical models for computation. We point out the splicing operation as an example. The computational completeness of DNA-HASKELL can be supposed. This paper is based on discussions about the potenzial and limits of DNA computing, in particular the practicability of a universal DNA computer.
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Young, William Albert II. "A Team-Compatibility Decision Support System to Model the NFL Knapsack Problem: An Introduction to HEART". Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1273158239.
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Sharma, Kamana. "Simultaneously lifting sets of variables in binary Knapsack problems". Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/469.
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Visagie, S. E. "Algoritmes vir die maksimering van konvekse en verwante knapsakprobleme /". Link to the online version, 2007. http://hdl.handle.net/10019.1/1082.
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Bark, Ondřej. "Analysis of tracing and capacity utilization by handlers in production". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-206141.
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The diploma thesis focuses on tracing in layout by handlers between assembly lines in new plant for corporation Continental Automotive Czech Republic ltd, where boosters are produced. The theoretical part involves definitions of logistics, supply chain, material flow and handling equipment. Furthermore, methods of mathematic programming and software equipment are described, such as quadratic assignment problem, knapsack problem, travelling salesman problem from graph theory. In the practical part the situation in corporation has been analyzed and the data prepared for further examination. Then layout of plant and internal processes are evaluated and an appropriate model or concept of solution is selected. Subsequently, application in MS Excel is created with support of VBA scripts (3 kinds of layouts). The user manipulates with application followed by Solver for implementation of a new solution into practice. Finally, the models are interpreted and verified by Lingo. The focus of the thesis is the design of a layout change of a new plant including the description of tracing.
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Hinze, Thomas y Monika Sturm. "A universal functional approach to DNA computing and its experimental practicability". Technische Universität Dresden, 2000. https://tud.qucosa.de/id/qucosa%3A26319.
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The rapid developments in the field of DNA computing reflects two substantial questions: 1. Which models for DNA based computation are really universal? 2. Which model fulfills the requirements to a universal lab-practicable programmable DNA computer that is based on one of these models? This paper introduces the functional model DNA-HASKELL focussing its lab-practicability. This aim could be reached by specifying the DNA based operations in accordiance to an analysis of molecular biological processes. The specification is determined by an abstraction level that includes nucleotides and strand end labels like 5'-phosphate. Our model is able to describe DNA algorithms for any NP-complete problem - here exemplified by the knapsacik problem - as well as it is able to simulate some established mathematical models for computation. We point out the splicing operation as an example. The computational completeness of DNA-HASKELL can be supposed. This paper is based on discussions about the potenzial and limits of DNA computing, in particular the practicability of a universal DNA computer.
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Bijinemula, Sandeep Kumar. "An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87403.
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Engine-triggered tasks are real-time tasks that are released when the crankshaft arrives at certain positions in its path of rotation. This makes the rate of release of these jobs a function of the crankshaft's angular speed and acceleration. In addition, several properties of the engine triggered tasks like the execution time and deadlines are dependent on the speed profile of the crankshaft. Such tasks are referred to as adaptive-variable rate (AVR) tasks. Existing methods to calculate the worst-case demand of AVR tasks are either inaccurate or computationally intractable. We propose a method to efficiently calculate the worst-case demand of AVR tasks by transforming the problem into a variant of the knapsack problem. We then propose a framework to systematically narrow down the search space associated with finding the worst-case demand of AVR tasks. Experimental results show that our approach is at least 10 times faster, with an average runtime improvement of 146 times for randomly generated task sets when compared to the state-of-the-art technique.Master of Science
Real-time systems require temporal correctness along with accuracy. This notion of temporal correctness is achieved by specifying deadlines to each of the tasks. In order to ensure that all the deadlines are met, it is important to know the processor requirement, also known as demand, of a task over a given interval. For some tasks, the demand is not constant, instead it depends on several external factors. For such tasks, it becomes necessary to calculate the worst-case demand. Engine-triggered tasks are activated when the crankshaft in an engine is at certain points in its path of rotation. This makes their activation rate dependent on the angular speed and acceleration of the crankshaft. In addition, several properties of the engine triggered tasks like the execution time and deadlines are dependent on the speed profile of the crankshaft. Such tasks are referred to as adaptive-variable rate (AVR) tasks. Existing methods to calculate the worst-case demand of AVR tasks are either inaccurate or computationally intractable. We propose a method to efficiently calculate the worst-case demand of AVR tasks by transforming the problem into a variant of the knapsack problem. We then propose a framework to systematically narrow down the search space associated with finding the worst-case demand of AVR tasks. Experimental results show that our approach is at least 10 times faster, with an average runtime improvement of 146 times for randomly generated task sets when compared to the state-of-the-art technique.
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Brommesson, Peter. "Solving the Generalized Assignment Problem by column enumeration based on Lagrangian reduced costs". Thesis, Linköping University, Department of Mathematics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5583.
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In this thesis a method for solving the Generalized Assignment Problem (GAP) is described. It is based on a reformulation of the original problem into a Set Partitioning Problem (SPP), in which the columns represent partial solutions to the original problem. For solving this problem, column generation, with systematic overgeneration of columns, is used. Conditions that guarantee that an optimal solution to a restricted SPP is optimal also in the original problem are given. In order to satisfy these conditions, not only columns with the most negative Lagrangian reduced costs need to be generated, but also others; this observation leads to the use of overgeneration of columns.
The Generalized Assignment Problem has shown to be NP-hard and therefore efficient algorithms are needed, especially for large problems. The application of the proposed method decomposes GAP into several knapsack problems via Lagrangian relaxation, and enumerates solutions to each of these problems. The solutions obtained from the knapsack problems form a Set Partitioning Problem, which consists of combining one solution from each knapsack problem to obtain a solution to the original problem. The algorithm has been tested on problems with 10 agents and 60 jobs. This leads to 10 knapsack problems, each with 60 variables.
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Wei, Xin. "An Optimal Solution on Screening and Treatment of Chlamydia Trachomatis and Neisseria Gonorrhoeae". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/35.
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We propose a resource allocation model for the management of the fund for the screening and treatment of women infected by Chlamydia trachomatis and Neisseria gonorrhoeae. The goal is to maximize the number of infected women cured of Chlamydia trachomatis and Neisseria gonorrhoeae infections. The population going for screening is divided into groups by ages and races. The group number is dynamic. Dierent groups have dierent infection rates. There are four possible test assays and four possible treatments. We employed a two-phase algorithm to solve the problem. The first phase is small so an exhaustive method is applied, while the second phase is transformed to a knapsack problem and a dynamic programming method is applied.
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Molnárová, Marika. "Metody dynamického programování v logistice a plánování". Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-11211.
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The thesis describes the principles of dynamic programming and it's application to concrete problems. (The travelling salesman problem, the knapsack problem, the shortest path priblem,the set covering problem.)
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Hallbäck, Sofia y Ellen Paulsson. "Reducing waste with an optimized trimming model in production planning". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173253.
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In which ways can the process of trimming dispersion coated board products be optimized so as to reduce material waste and increase production efficiency? This is the question that this master thesis report seeks to answer. In paper production, alot of waste is generated when cutting production reels into customer reels. Some material waste are necessary in order to ensure good quality, however a large amount of the wastecould be reduced if the cutting process was to be optimized. During this project, carried out at a forest company, a mathematical optimization model was developed in order to reduce waste and save costs. This model is based on a cutting stock problem using column generation approach. It provides as its output cutting patterns and an optimal allocation of rolls for production purposes, which implies minimizing the number production rolls needed.The visualization of the results could also be used to achieve optimal stock levels, and easier keep track on how to use the material available in stock. Findings show that there are potential savings to be done. Simulations suggest an implementation of this model could result in material savings of around 7 %. This could also translateto environmental savings in CO2, where every decrease of one tonne material equals to adecrease in CO2emissions of 500 kg
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Gupte, Akshay. "Mixed integer bilinear programming with applications to the pooling problem". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45761.
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Solution methodologies for mixed integer bilinear problems (MIBLP) are studied in this dissertation. This problem class is motivated using the pooling problem, a multicommodity network flow problem that typically arises in chemical engineering applications. Stronger than previously known results are provided to compare the strengths of polyhedral relaxations of the pooling problem. A novel single node flow relaxation, defined by a bilinear equality constraint and flow balance, is proposed for the pooling problem. Linear valid inequalities in the original space of variables are derived using a well-known technique called lifting. Mixed integer linear (MILP) formulations are proposed for generating feasible solutions to the pooling problem. Some of these MILP models arise from variable discretizations while others possess a network flow interpretation. The effectiveness of these MILP models is empirically validated on a library of medium and large-scale instances. General MIBLPs, not necessarily pooling problems, are solved using extended MILP reformulations. The reformulation is obtained by writing binary representation for each general integer variable. Facet-defining inequalities are provided for the reformulation of each bilinear term. New valid inequalities are also proposed for bilinear terms with a nontrivial upper bound. The proposed reformulation and cutting planes are compared against a global solver on five different classes of MIBLPs.
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Shim, Sangho. "Large scale group network optimization". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31737.
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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010.Committee Chair: Ellis L. Johnson; Committee Member: Brady Hunsaker; Committee Member: George Nemhauser; Committee Member: Jozef Siran; Committee Member: Shabbir Ahmed; Committee Member: William Cook. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Beyer, Carrie Austin. "Exact synchronized simultaneous uplifting over arbitrary initial inequalities for the knapsack polytope". Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/8782.
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Master of ScienceDepartment of Industrial & Manufacturing Systems Engineering
Todd W. Easton
Integer programs (IPs) are mathematical models that can provide an optimal solution to a variety of different problems. They have been used to reduce costs and optimize organizations. Additionally, IPs are NP-complete resulting in many IPs that cannot be solved. Cutting planes or valid inequalities have been used to decrease the time required to solve IPs. Lifting is a technique that strengthens existing valid inequalities. Lifting inequalities can result in facet defining inequalities, which are the theoretically strongest valid inequalities. Because of these properties, lifting procedures are used in software to reduce the time required to solve an IP. The thesis introduces a new algorithm for exact synchronized simultaneous uplifting over an arbitrary initial inequality for knapsack problems. Synchronized Simultaneous Lifting (SSL) is a pseudopolynomial time algorithm requiring O(nb+n[superscript]3) effort to solve. It exactly uplifts two sets simultaneously into an initial arbitrary valid inequality and creates multiple inequalities of a particular form. This previously undiscovered class of inequalities generated by SSL can be facet defining. A small computational study shows that SSL is quick to execute, requiring on average less than a quarter of a second. Additionally, applying SSL inequalities to a knapsack problem enabled commercial software to solve problems that it could not solve without them.
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Visagie, Stephan E. "Algoritmes vir die maksimering van konvekse en verwante knapsakprobleme". Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/1082.
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Thesis (PhD (Logistics))--University of Stellenbosch, 2007.In this dissertation original algorithms are introduced to solve separable resource allocation problems (RAPs) with increasing nonlinear functions in the objective function, and lower and upper bounds on each variable. Algorithms are introduced in three special cases. The first case arises when the objective function of the RAP consists of the sum of convex functions and all the variables for these functions range over the same interval. In the second case RAPs with the sum of convex functions in the objective function are considered, but the variables of these functions can range over different intervals. In the last special case RAPs with an objective function comprising the sum of convex and concave functions are considered. In this case the intervals of the variables can range over different values. In the first case two new algorithms, namely the fraction and the slope algorithm are presented to solve the RAPs adhering to the conditions of the case. Both these algorithms yield far better solution times than the existing branch and bound algorithm. A new heuristic and three new algorithms are presented to solve RAPs falling into the second case. The iso-bound heuristic yields, on average, good solutions relative to the optimal objective function value in faster times than exact algorithms. The three algorithms, namely the iso-bound algorithm, the branch and cut algorithm and the iso-bound branch and cut algorithm also yield considerably beter solution times than the existing branch and bound algorithm. It is shown that, on average, the iso-bound branch and cut algorithm yields the fastest solution times, followed by the iso-bound algorithm and then by die branch and cut algorithm. In the third case the necessary and sufficient conditions for optimality are considered. From this, the conclusion is drawn that search techniques for points complying with the necessary conditions will take too long relative to branch and bound techniques. Thus three new algorithms, namely the KL, SKL and IKL algorithms are introduced to solve RAPs falling into this case. These algorithms are generalisations of the branch and bound, branch and cut, and iso-bound algorithms respectively. The KL algorithm was then used as a benchmark. Only the IKL algorithm yields a considerable improvement on the KL algorithm.
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Cerqueus, Audrey. "Bi-objective branch-and-cut algorithms applied to the binary knapsack problem : surrogate bound sets, dynamic branching strategies, generation and exploitation of cover inequalities". Nantes, 2015. https://archive.bu.univ-nantes.fr/pollux/show/show?id=fdf0e978-37d8-4290-8495-a3fd67de78f7.
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Dans ce travail, nous nous intéressons à la résolution de problèmes d’optimisation combinatoire multi-objectif. Ces problèmes ont suscité un intérêt important au cours des dernières décennies. Afin de résoudre ces problèmes, particulièrement difficiles, de manière exacte et efficace, les algorithmes sont le plus souvent spécifiques au problème traité. Dans cette thèse, nous revenons sur l’approche dite de branch-and-bound et nous en proposons une extension pour obtenir un branch-and-cut, dans un contexte bi-objectif. Les problèmes de sac-à-dos sont utilisés comme support pour ces travaux. Trois axes principaux sont considérés : la définition de nouveaux ensembles bornants, l’élaboration d’une stratégie de branchement dynamique et la génération d’inégalités valides. Les ensembles bornants définis sont basés sur la relaxation surrogate, utilisant un ensemble de multiplicateurs. Des algorithmes sont élaborés, à partir de l’étude des différents multiplicateurs, afin de calculer efficacement les ensembles bornants surrogate. La stratégie de branchement dynamique émerge de la comparaison de différentes stratégies de branchement statiques, issues de la littérature. Elle fait appel à une méthode d’apprentissage par renforcement. Enfin, des inégalités de couverture sont générées et introduites, tout au long de la résolution, dans le but de l’accélérer. Ces différents apports sont validés expérimentalement et l’algorithme de branch-and-cut obtenu présente des résultats encourageantsIn this work, we are interested in solving multi-objective combinatorial optimization problems. These problems have received a large interest in the past decades. In order to solve exactly and efficiently these problems, which are particularly difficult, the designed algorithms are often specific to a given problem. In this thesis, we focus on the branch-and-bound method and propose an extension by a branch-and-cut method, in bi-objective context. Knapsack problems are the case study of this work. Three main axis are considered: the definition of new upper bound sets, the elaboration of a dynamic branching strategy and the generation of valid inequalities. The defined upper bound sets are based on the surrogate relaxation, using several multipliers. Based on the analysis of the different multipliers, algorithms are designed to compute efficiently these surrogate upper bound sets. The dynamic branching strategy arises from the comparison of different static branching strategies from the literature. It uses reinforcement learning methods. Finally, cover inequalities are generated and introduced, all along the solving process, in order to improve it. Those different contributions are experimentally validated and the obtained branch-and-cut algorithm presents encouraging results
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Bazzazian, Navid. "Applications of the Law of Large Numbers in Logistics". Thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18457.
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One of the most remarkable theories in probability and statistics is the law of large numbers. Law of large numbers describes the behavior of random phenomena when they are reiterated infinitely or in very large trials. Apart from the mathematical exposition of the law of large numbers, its theory and applications have been widely used in gambling houses, financial sectors, and healthcare insurance where uncertainties deteriorate prediction and financial strength. However, the applications of the law of large numbers are not confined to the referred sectors and could be widely applied to industrial organizations and service provider companies in which large number of stochastic phenomena incorporate in their planning. In this thesis, the applications of the law of large numbers are studied in relation to logistics and transportation under conditions of operating in large networks. The results of this study assert that transportation companies can benefit from operating in large networks to increase the filling performance of their vehicles, fleet, etc. Equivalently, according to the law of large numbers the inferior capacity utilization in unit loads, containers, etc. converges to 0 with probability 1 as the size of the network grows.Uppsatsnivå: D
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Heydrich, Sandy [Verfasser] y Rob van [Akademischer Betreuer] Stee. "A tale of two packing problems : improved algorithms and tighter bounds for online bin packing and the geometric knapsack problem / Sandy Heydrich ; Betreuer: Rob van Stee". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1164012193/34.
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Kim, Gitae. "Solving support vector machine classification problems and their applications to supplier selection". Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8719.
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Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems Engineering
Chih-Hang Wu
Recently, interdisciplinary (management, engineering, science, and economics) collaboration research has been growing to achieve the synergy and to reinforce the weakness of each discipline. Along this trend, this research combines three topics: mathematical programming, data mining, and supply chain management. A new pegging algorithm is developed for solving the continuous nonlinear knapsack problem. An efficient solving approach is proposed for solving the ν-support vector machine for classification problem in the field of data mining. The new pegging algorithm is used to solve the subproblem of the support vector machine problem. For the supply chain management, this research proposes an efficient integrated solving approach for the supplier selection problem. The support vector machine is applied to solve the problem of selecting potential supplies in the procedure of the integrated solving approach. In the first part of this research, a new pegging algorithm solves the continuous nonlinear knapsack problem with box constraints. The problem is to minimize a convex and differentiable nonlinear function with one equality constraint and box constraints. Pegging algorithm needs to calculate primal variables to check bounds on variables at each iteration, which frequently is a time-consuming task. The newly proposed dual bound algorithm checks the bounds of Lagrange multipliers without calculating primal variables explicitly at each iteration. In addition, the calculation of the dual solution at each iteration can be reduced by a proposed new method for updating the solution. In the second part, this research proposes several streamlined solution procedures of ν-support vector machine for the classification. The main solving procedure is the matrix splitting method. The proposed method in this research is a specified matrix splitting method combined with the gradient projection method, line search technique, and the incomplete Cholesky decomposition method. The method proposed can use a variety of methods for line search and parameter updating. Moreover, large scale problems are solved with the incomplete Cholesky decomposition and some efficient implementation techniques. To apply the research findings in real-world problems, this research developed an efficient integrated approach for supplier selection problems using the support vector machine and the mixed integer programming. Supplier selection is an essential step in the procurement processes. For companies considering maximizing their profits and reducing costs, supplier selection requires seeking satisfactory suppliers and allocating proper orders to the selected suppliers. In the early stage of supplier selection, a company can use the support vector machine classification to choose potential qualified suppliers using specific criteria. However, the company may not need to purchase from all qualified suppliers. Once the company determines the amount of raw materials and components to purchase, the company then selects final suppliers from which to order optimal order quantities at the final stage of the process. Mixed integer programming model is then used to determine final suppliers and allocates optimal orders at this stage.
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Goodwin, Michelle. "Lattices and Their Application: A Senior Thesis". Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1317.
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Lattices are an easy and clean class of periodic arrangements that are not only discrete but associated with algebraic structures. We will specifically discuss applying lattices theory to computing the area of polygons in the plane and some optimization problems. This thesis will details information about Pick's Theorem and the higher-dimensional cases of Ehrhart Theory. Closely related to Pick's Theorem and Ehrhart Theory is the Frobenius Problem and Integer Knapsack Problem. Both of these problems have higher-dimension applications, where the difficulties are similar to those of Pick's Theorem and Ehrhart Theory. We will directly relate these problems to optimization problems and operations research.
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Patel, Chirag B. "A multi-objective stochastic approach to combinatorial technology space exploration". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29647.
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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009.Committee Chair: Dr. Dimitri N. Mavris; Committee Member: Dr. Brian J. German; Committee Member: Dr. Daniel P. Schrage; Committee Member: Dr. Frederic Villeneuve; Committee Member: Dr. Michelle R. Kirby; Committee Member: Ms. Antje Lembcke. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Leão, Aline Aparecida de Souza. "Extensões em problemas de corte: padrões compartimentados e problemas acoplados". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-03052013-162852/.
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Nesta tese é abordado o problema da mochila compartimentada e o problema de corte de estoque unidimensional acoplado ao problema dimensionamento de lotes. Para o problema da mochila compartimentada é apresentada a versão unidimensional e proposta a versão bidimensional, denominados como problema da mochila compartimentada unidimensional e problema da mochila compartimentada bidimensional, respectivamente. Para o problema de corte de estoque acoplado ao dimensionamento de lotes são apresentadas três variações: uma máquina para produzir um tipo de objeto; uma máquina para produzir vários tipos de objetos; múltiplas máquinas para produzir vários tipos de objetos. Algumas formulações matemáticas de programação inteira e inteira-mista, decomposições dos problemas em problema mestre e subproblemas e heurísticas baseadas no método geração de colunas são propostas para os problemas da mochila compartimenta e o problema acoplado. Em específico, para o problema acoplado são aplicadas decomposições Dantzig-Wolfe, que podem ser por período, por máquina ou por período e máquina. Além disso, uma heurística baseada em grafo E/OU é proposta para o problema da mochila compartimentada bidimensionalIn this thesis we present the constrained compartmentalized knapsack problem and the one dimensional cutting stock problem integrated with the capacitated lot sizing problem. For the constrained compartmentalized knapsack problem, the one dimensional version is presented and the two dimensional version is proposed, called one-dimensional compartmentalized knapsack problem and two-dimensional compartmentalized knapsack problem, respectively. For the cutting stock problem integrated with the capacitated lot sizing problem three variations are considered: one machine to produce one type of object; one machine to produce multiple types of objects; multiple machines to produce multiple types of objects. Some integer and mixed programming formulations, decompositions of the problems in master problem and subproblems and heuristics based on column generation method are proposed for the compartmentalized knapsack problem and the cutting stock problem integrated with the capacitated lot sizing problem. In particular, the period, the machine, and the period and machine Dantzig- Wolfe decompositions are applied for the integrated problem. Moreover, a heuristic based on the graph AND/OR is proposed for the two-dimensional compartmentalized knapsack problem. Computational results show that these mathematical formulations and methods provide good solutions
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Ravelo, Santiago Valdes. "Modelos matemáticos e algoritmos para problemas combinatórios". Universidade Federal de Goiás, 2011. http://repositorio.bc.ufg.br/tede/handle/tede/5354.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
This work considers three relevant NP-hard problems. The firstone is the one-dimensional cutting stock problem in which the non-used material in the cutting patterns may be used in the future. For this problem we analyze the existing mathematical models, propose new models, design a heuristic and two metaheuristic approaches, being their performances improved by using parallel programming, and solve instances, practical and randomly generated, from the literature. The computational experiments were quite good for all tested instances. The second problem we consider is the stable roommates problem (a variant of the stable matching problem). For this we give two mathematical programming models, sequential and parallel implementations of a Tabu Search, and a Branch-andBound. Also, we report computational experiments to instances of the problem. The last problem we consider is the compartmentalized knapsack problem (a generalization of the knapsack problem) for which we analyze a quadratic integer model and give a linear integer model. We design a greedy heuristic and a GRASP algorithm, that uses path-relinking, and solve randomly generated instances. All parallel implementations use Graphics Processing Units (GPUs).
Este trabalho considera três problemas, NP-difíceis, relevantes de estudo em otimização combinatória. O primeiro deles é o problema de corte uni-dimensional de objetos, onde o material não usado pelos padrões de corte pode ser usado no futuro. Para este problema analisamos os modelos matemáticos existentes, propomos novos modelos, projetamos uma heurística construtiva e duas metaheurísticas, sendo seus desempenhos melhorados com programação paralela, e resolvemos instâncias, práticas e aleatórias, encontradas na literatura; sendo os experimentos computacionais muito bons para todas as intânciastestadas.Osegundoproblemaqueconsideramoséoproblemadoscompanheiros estáveis (stable roommates problem), uma variante do problema de emparelhamento estável (stable matching problem). Para este propomos dois modelos matemáticos, uma implementação sequencial e uma paralela de uma Tabu Search, e um Branch-andBound. Também reportamos experimentos computacionais para instâncias do problema. O último problema considerado é o da mochila compartimentada (uma generalização do problema clássico da mochila), para o qual analisamos uma modelagem quadrática inteira e propomos um modelo linear inteiro; também projetamos uma heurística gulosa, um algoritmo GRASP, que usa path-relinking, e resolvemos intâncias geradas aleatóriamente. Todas as implementações em paralelo usam unidades de processamento gráfico (Graphics Processing Units, GPUs).