The Knapsack Problems are among the simplest integer programs which are NP-hard. Problems in this class are typically concerned with selecting from a set of given items, each with a specified weight and value, a subset of items whose weight sum does not exceed a prescribed capacity and whose value is maximum. The specific problem that arises depends on the number of knapsacks (single or multiple) to be filled and on the number of available items of each type (bounded or unbounded). The classical 0-1Knapsack Problem arises when there is one knapsack and one item of each type.Knapsack Problems have been intensively studied over the past forty years because of their direct application to problems arising in industry (for example, cargo loading, cutting stock, and budget control) and also for their contribution to the solution methods for integer programming problems.Several exact algorithms based on branch and bound and dynamic programming have been proposed to solve the Knapsack Problems. For some types of data instances, very efficient algorithms have been found. However, a number of hard knapsack instances have been identified. For example, subset sum and strongly correlated data types. This has motivated some researchers to develop specialized algorithms for particular hard problems.This thesis considers a number of hard Knapsack Problems with a single constraint. A number of specialized algorithms are developed. Our work focuses on some hard instances of the 0-1Knapsack Problem, the Bounded Knapsack Problem, the Unbounded Knapsack Problem and the Change-Making Problem.Chapter 1 provides the background of the Knapsack Problem including some important Knapsack Problems and standard types of data instances, terminology, and a summary of our work. Chapter 2 gives a review of the applications and the solution methods that have been proposed. The remaining chapters ++ / present the details of our specialized algorithms.Chapter 3 proposes algorithms for the hard 0-1Knapsack Problems instances of subset sum, strongly correlated, and inverse strongly correlated. The algorithms for the Bounded Knapsack Problem instances of strongly correlated and subset sum are also presented. Extensive computational results show that our algorithms are able to solve large problems of size up to one million variables in less than 7 seconds.Chapter 4 proposes algorithms for some hard Unbounded Knapsack Problems. Two algorithms one for the Unbounded Strongly Correlated Knapsack Problem (algorithm CKU1) and one for the Unbounded Subset Sum Problem (algorithm CKU2) are presented. Extensive computational results establish that our two algorithms are able to solve large problems with up to one million variables in less than 0.3 second.Finally, Chapter 5 proposes exact algorithms for the Change-Making Problem. The problem is a particular type of single Knapsack Problems. This chapter proposes two exact algorithms: algorithm CKUC for the Unbounded Change-Making Problem (UCMP) and algorithm CKBC for the Bounded Change-Making Problem (BCMP). The algorithms can solve large-sized problems, when the item types are generated in small ranges, in less than 51 milliseconds for UCMP and less than 3.5 seconds for BCMP.

We propose a generic algorithm based on branch-and-bound to generate all efficient solutions of multiobjective combinatorial optimization (MOCO) problems. We present an algorithm specific to multiobjective 0-1 Knapsack Problem based on the generic algorithm. We test the performance of our algorithm on randomly generated sample problems against IBM ILOG CPLEX and we obtain better performance using a problem specific algorithm. We develop a heuristic algorithm by incorporating memory limitations at the expense of solution quality to overcome memory issues of the exact algorithm.

The thesis deals with knapsack problems variants and possibility of their solving, furthermore with the impact of particular task (instance) special structure on the effciency of tested approach. The thesis also proposes conversion possibility between described tasks and their continuous extension (continuous relaxation). It describes L3 algorithm and superdecreasing knapsack problem solving from the common sort of algorithms and Monte Carlo Method, simulated annealing and genetic algorithms from the sort of probability ones. Other possibilities are also discussed. Integral part of this thesis is the accompanying application, which was used to create groundwork used in the text and which can be also used to solve other instances.

O Problema da Mochila Compartimentada é uma extensão do Problema da Mochila, em que os itens solicitados são divididos em classes, de modo que a mochila deve ser subdividida em compartimentos, os quais têm capacidades limitadas e são carregados com itens da mesma classe. Além disso, a construção de um compartimento tem um custo fixo e ocasiona uma perda no espaço da mochila. O objetivo consiste em maximizar a soma dos valores dos itens, descontado o custo fixo de inclusão de compartimentos. Neste trabalho, são abordados dois métodos de solução. A primeira abordagem é uma heurística, que consiste na combinação de duas heurísticas da literatura. A segunda abordagem é o método Geração de Colunas, que além de fornecer um novo limitante superior para o Problema da Mochila Compartimentada, ao final do método o problema mestre foi resolvido com as variáveis definidas como inteiras, obtendo uma solução factível. Em ambos os métodos, o modelo não-linear é decomposto em dois modelos lineares, no qual, um gera compartimentos e o outro os seleciona. Os resultados obtidos com as duas abordagens foram comparados com um limitante superior e se mostraram bastante satisfatórios / The Compartmentalized Knapsack Problem is an extension of the classical Knapsack Problem, where the ordered items are partitioned into classes, in such way that the knapsack must be divided into compartments, each one having limited capacity. In addition, the building of a compartment has a fixed cost and involves a loss of the overall capacity. The objective is to maximize the sum of the items utility value, minus the fixed costs of the compartments. This dissertation presents two solving methods. The first approach is a heuristic method, which is a combination of two heuristics from the literature. The second approach is a Column Generation method, that apart from it gives a new upper bound to the Compartmentalized Knapsack Problem, in the end of the method the master problem was solved with the variables defined as integer, that supplies a feasible solution. In both methods, the mathematical non linear model is decomposed into two linear models, one generates the compartments, and the other selects them to compose the knapsack. The results obtained with these two approaches were compared with an upper bound and they showed very efficient

O Problema da Mochila Compartimentada é uma extensão do Problema da Mochila, em que os itens solicitados são divididos em classes, de modo que a mochila deve ser subdividida em compartimentos, os quais têm capacidades limitadas e são carregados com itens da mesma classe. Além disso, a construção de um compartimento tem um custo fixo e ocasiona uma perda no espaço da mochila. O objetivo consiste em maximizar a soma dos valores dos itens, descontado o custo fixo de inclusão de compartimentos. Neste trabalho, são abordados dois métodos de solução. A primeira abordagem é uma heurística, que consiste na combinação de duas heurísticas da literatura. A segunda abordagem é o método Geração de Colunas, que além de fornecer um novo limitante superior para o Problema da Mochila Compartimentada, ao final do método o problema mestre foi resolvido com as variáveis definidas como inteiras, obtendo uma solução factível. Em ambos os métodos, o modelo não-linear é decomposto em dois modelos lineares, no qual, um gera compartimentos e o outro os seleciona. Os resultados obtidos com as duas abordagens foram comparados com um limitante superior e se mostraram bastante satisfatórios / The Compartmentalized Knapsack Problem is an extension of the classical Knapsack Problem, where the ordered items are partitioned into classes, in such way that the knapsack must be divided into compartments, each one having limited capacity. In addition, the building of a compartment has a fixed cost and involves a loss of the overall capacity. The objective is to maximize the sum of the items utility value, minus the fixed costs of the compartments. This dissertation presents two solving methods. The first approach is a heuristic method, which is a combination of two heuristics from the literature. The second approach is a Column Generation method, that apart from it gives a new upper bound to the Compartmentalized Knapsack Problem, in the end of the method the master problem was solved with the variables defined as integer, that supplies a feasible solution. In both methods, the mathematical non linear model is decomposed into two linear models, one generates the compartments, and the other selects them to compose the knapsack. The results obtained with these two approaches were compared with an upper bound and they showed very efficient

<p>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.</p><p>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.</p>

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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.

Orientador: Eduardo Candido Xavier / Dissertação (mestrado) - Universidade Estadual de Campinas, Insituto de Computação / Made available in DSpace on 2018-08-17T16:49:45Z (GMT). No. of bitstreams: 1 DelValle_AlineMarques_M.pdf: 1217777 bytes, checksum: 66f10d1b6b4533727cbe82431f97660d (MD5) Previous issue date: 2010 / Resumo: Nesta dissertação, estudamos problemas de empacotamento com itens irregulares. Estamos particularmente interessados no Problema da Mochila Bidimensional: dados um recipiente de tamanho W x H e uma lista de itens bidimensionais, o objetivo é empacotar um subconjunto dos itens de forma a maximizar a área dos itens empacotados. Existem diversos trabalhos que lidam com problemas para itens e recipientes bidimensionais com forma regular (retangular). No entanto, são poucos os estudos que tratam de itens com formas irregulares. Nós propomos algoritmos de empacotamento para itens irregulares em recipientes limitados baseados no uso de No-Fit-Polygon (NFP). Este trabalho apresenta uma heurística GRASP para a versão restrita do Problema da Mochila: uma solução inicial gulosa é gerada e, em seguida, utiliza-se um algoritmo de busca local para melhorar solução atual. Uma estratégia híbrida também foi proposta para versão irrestrita do Problema da Mochila. Ela divide-se em passos de empacotamento de itens irregulares e empacotamento de itens regulares. Testamos os algoritmos com instâncias adaptadas do problema de Strip Packing. O GRASP obteve empacotamentos ótimos para várias instancias testadas e, mesmo para as instâncias em que o algoritmo não obteve resultados ótimos, os empacotamentos obtidos tiveram boa taxa de ocupação, com valores relativamente próximos do ótimo. O tempo de execução do algoritmo foi razoável. Na estratégia híbrida, obtiveram-se empacotamentos bons para a maioria das instâncias, com taxa de ocupação acima de 90% e tempos de execução relativamente baixos / Abstract: In this work, we study packing problems dealing with two dimensional irregular items. We are particularly interested in the knapsack version of the problem: given a container with size W x H and a list of two dimensional items, the goal is to pack a subset of items such that the total area of packed items is maximized. There are several works that deal with problems for the case where items and containers have regular shapes (rectangular). However, only a few studies deal with items with irregular shapes. We propose algorithms for packing irregular items in limited containers based on the use of No-Fit-Polygon (NFP). This work presents a GRASP algorithm for the restricted version of the Knapsack Problem: first, a greedy initial solution is generated, then, the local search algorithm is used to improve the current solution. A hybrid strategy has also been proposed for the unrestricted version of the Knapsack Problem. It is divided into steps of packing irregular items and packing regular items. We tested the algorithms using adapted instances for the Strip Packing problem. The GRASP algorithm achieved optimal packings for several of the tested instances, and, even for those that the algorithm did not, the obtained packings had a good occupancy rate, with values relatively close to the optimum. The runtime of the algorithm was reasonable. In the hybrid strategy, we obtained good packings for most of the instances, with occupancy rates above 90% and relatively low execution times / Mestrado / Teoria da Computação / Mestre em Ciência da Computação

This dissertation introduces new heuristic methods for the 0-1 multi-dimensional knapsack problem (0-1 MKP). 0-1 MKP can be informally stated as the problem of packing items into a knapsack while staying within the limits of different constraints (dimensions). Each item has a profit level assigned to it. They can be, for instance, the maximum weight that can be carried, the maximum available volume, or the maximum amount that can be afforded for the items. One main assumption is that we have only one item of each type, hence the problem is binary (0-1). The single dimensional version of the 0-1 MKP is the uni-dimensional single knapsack problem which can be solved in pseudo-polynomial time. However the 0-1 MKP is a strongly NP-Hard problem. Reduced cost values are rarely used resources in 0-1 MKP heuristics; using reduced cost information we introduce several new heuristics and also some improvements to past heuristics. We introduce two new ordering strategies, decision variable importance (DVI) and reduced cost based ordering (RCBO). We also introduce a new greedy heuristic concept which we call the "sliding concept" and a sub-branch of the "sliding concept" which we call "sliding enumeration". We again use the reduced cost values within the sliding enumeration heuristic. RCBO is a brand new ordering strategy which proved useful in several methods such as improving Pirkul's MKHEUR, a triangular distribution based probabilistic approach, and our own sliding enumeration. We show how Pirkul's shadow price based ordering strategy fails to order the partial variables. We present a possible fix to this problem since there tends to be a high number of partial variables in hard problems. Therefore, this insight will help future researchers solve hard problems with more success. Even though sliding enumeration is a trivial method it found optima in less than a few seconds for most of our problems. We present different levels of sliding enumeration and discuss potential improvements to the method. Finally, we also show that in meta-heuristic approaches such as Drexl's simulated annealing where random numbers are abundantly used, it would be better to use better designed probability distributions instead of random numbers.