Relevant bibliographies by topics / KNAPSACK-PROBLEM

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'KNAPSACK-PROBLEM'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "KNAPSACK-PROBLEM"

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Ross, K. W., and D. H. K. Tsang. "The stochastic knapsack problem." IEEE Transactions on Communications 37, no. 7 (July 1989): 740–47. http://dx.doi.org/10.1109/26.31166.

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Han, Xin, and Kazuhisa Makino. "Online minimization knapsack problem." Theoretical Computer Science 609 (January 2016): 185–96. http://dx.doi.org/10.1016/j.tcs.2015.09.021.

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Hochbaum, Dorit S. "A nonlinear Knapsack problem." Operations Research Letters 17, no. 3 (April 1995): 103–10. http://dx.doi.org/10.1016/0167-6377(95)00009-9.

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Chung, Chia-Shin, Ming S. Hung, and Walter O. Rom. "A hard knapsack problem." Naval Research Logistics 35, no. 1 (February 1988): 85–98. http://dx.doi.org/10.1002/1520-6750(198802)35:1<85::aid-nav3220350108>3.0.co;2-d.

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Schulze, Britta, Michael Stiglmayr, Luís Paquete, Carlos M. Fonseca, David Willems, and Stefan Ruzika. "On the rectangular knapsack problem: approximation of a specific quadratic knapsack problem." Mathematical Methods of Operations Research 92, no. 1 (February 12, 2020): 107–32. http://dx.doi.org/10.1007/s00186-020-00702-0.

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Abstract In this article, we introduce the rectangular knapsack problem as a special case of the quadratic knapsack problem consisting in the maximization of the product of two separate knapsack profits subject to a cardinality constraint. We propose a polynomial time algorithm for this problem that provides a constant approximation ratio of 4.5. Our experimental results on a large number of artificially generated problem instances show that the average ratio is far from theoretical guarantee. In addition, we suggest refined versions of this approximation algorithm with the same time complexity and approximation ratio that lead to even better experimental results.
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Nair, Dr Prabha Shreeraj. "Clustered Genetic Algorithm to solve Multidimensional Knapsack Problem." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 737–45. http://dx.doi.org/10.31142/ijtsrd2237.

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Hu, Zhi Jun, and Rong Li. "Ant Colony Optimization Algorithm for the 0-1 Knapsack Problem Based on Genetic Operators." Advanced Materials Research 230-232 (May 2011): 973–77. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.973.

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0-1 knapsack problem is a typical combinatorial optimization question in the design and analysis of algorithms. The mathematical description of the knapsack problem is given in theory. The 0-1 knapsack problem is solved by ant colony optimistic algorithm that is improved by introducing genetic operators. To solve the 0-1 knapsack problem with the improved ant colony algorithm, experimental results of numerical simulations, compared with greedy algorithm and dynamic programming algorithm, have shown obvious advantages in efficiency and accuracy on the knapsack problem.
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Dang, Binh Thanh, and Tung Khac Truong. "Binary salp swarm algorithm for discounted {0-1} knapsack problem." PLOS ONE 17, no. 4 (April 7, 2022): e0266537. http://dx.doi.org/10.1371/journal.pone.0266537.

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While the classical knapsack problem has been the object to be solved by optimization algorithm proposals for many years, another version of this problem, discounted {0-1} knapsack problem, is gaining a lot of attention recently. The original knapsack problem requires selecting specific items from an item set to maximize the total benefit while ensuring that the total weight does not exceed the knapsack capacity. Meanwhile, discounted {0-1} knapsack problem has more stringent requirements in which items are divided into groups, and only up to one item from a particular group can be selected. This constraint, which does not exist in the original knapsack problem, makes discounted {0-1} knapsack problem even more challenging. In this paper, we propose a new algorithm based on salp swarm algorithm in the form of four different variants to resolve the discounted {0-1} knapsack problem. In addition, we also make use of an effective data modeling mechanism and a greedy repair operator that helps overcome local optima when finding the global optimal solution. Experimental and statistical results show that our algorithm is superior to currently available algorithms in terms of solution quality, convergence, and other statistical criteria.
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Xiao, Meng, and Yun Yao Zhou. "Discussion on Knapsack Problem Optimization Algorithm Based on Complex Network." Applied Mechanics and Materials 556-562 (May 2014): 3354–56. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3354.

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This passage is to put forward knapsack problem optimization algorithm based on complex network (KOABCN). Knapsack problem has extremely wide application in a great number of fields. For instance, knapsack problem can be applied in information coding, budget control, project choosing, material cutting, cargo loading and unloading as well as Internet information safety. Since 1950’s, knapsack problem has been one of the most heated topics in algorithm and complexity research. Therefore, knapsack will still be largely focused in the next period of research.
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Devita, Riri Nada, and Aji Prasetya Wibawa. "Teknik-teknik Optimasi Knapsack Problem." Sains, Aplikasi, Komputasi dan Teknologi Informasi 2, no. 1 (April 5, 2020): 35. http://dx.doi.org/10.30872/jsakti.v2i1.3299.

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Optimasi merupakan sebuah teknik yang identik dengan memaksimalkan sumber daya yang terbatas. Salah satunya adalah permasalahan nyata yang memperlukan teknik optimasi adalah cara mengatur barang-barang yang dimuat dalam suatu knapsack (karung/ Tas). Knapsack problem merupakan masalah dimana orang dihadapkan pada persoalan optimasi pemilihan benda yang dapat di tampung ke dalam sebuah knapsack (karung) yang memiliki keterbatasan daya dan ruang tampung. Oleh karena itu, dengan adanya optimasi dalam pemilihan barang yang akan ditampung dalam knapsak tersebut diharapkan dapat menghasilkan efisiensi yang maksimal. Oleh karena itu, paper ini bertujuan untuk mendiskusikan beberapa algoritma optimasi yang sesuai untuk masalah knapsack. Hasil dari studi ini menunjukkan bahwa algoritma Dynamic Programming adalah algoritma yang paling sesuai dalam penyelesaian masalah knapsack karena menghasilkan solusi yang optimum dan waktu running yang tidak lama.
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Dissertations / Theses on the topic "KNAPSACK-PROBLEM"

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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.
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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, and 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
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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’art
This 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 Science
Department 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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Books on the topic "KNAPSACK-PROBLEM"

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Sural, H. The bounded knapsack problem with setups. Fontainebleau: INSEAD, 1997.

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Sural, H. The bounded knapsack problem with setups. France: INSEAD, 1997.

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Csirik, J. Heuristics for the 0-1 Min-Knapsack problem. Brussels: European Institute for Advanced Studies in Management, 1990.

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Martello, Silvano. Knapsack problems: Algorithms and computer implementations. Chichester: J. Wiley & Sons, 1990.

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Knapsack Problems. Springer, 2004.

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Solving the Multidimensional Multiple Knapsack Problem with Packing constraints using Tabu Search. Storming Media, 1999.

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Seberry, Jennifer, and Luke J. O'Connor. Cryptographic Significance of the Knapsack Problem Plus Exercises and Solutions (Cryptographic Series , No 50). Aegean Park Press, 1988.

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Seberry, Jennifer, and Luke J. O'Connor. Cryptographic Significance of the Knapsack Problem Plus Exercises and Solutions (Cryptographic Series , No 50). Aegean Park Pr, 1988.

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Cashman, David. Approximate truthful mechanisms for the knapsack problem, and negative results using a stack model for local ratio algorithms. 2005.

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Cashman, David. Approximate truthful mechanisms for the knapsack problem, and negative results using a stack model for local ratio algorithms. 2005.

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Book chapters on the topic "KNAPSACK-PROBLEM"

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Bartholdi, John J. "The Knapsack Problem." In Building Intuition, 19–31. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-73699-0_2.

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Korte, Bernhard, and Jens Vygen. "The Knapsack Problem." In Algorithms and Combinatorics, 459–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24488-9_17.

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Korte, Bernhard, and Jens Vygen. "Das Knapsack-Problem." In Kombinatorische Optimierung, 485–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25401-7_17.

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Walukiewicz, Stanisław. "The Knapsack Problem." In Integer Programming, 90–108. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7945-2_5.

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Korte, Bernhard, and Jens Vygen. "The Knapsack Problem." In Algorithms and Combinatorics, 397–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-21708-5_17.

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Korte, Bernhard, and Jens Vygen. "The Knapsack Problem." In Algorithms and Combinatorics, 397–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-21711-5_17.

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Chandra Jaiswal, Umesh, Ankit Singh, Omkar Maurya, and Anil Kumar. "Unified Knapsack Problem." In Computer Networks and Information Technologies, 325–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19542-6_60.

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Beier, Rene, and Berthold Vöcking. "The Knapsack Problem." In Algorithms Unplugged, 375–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15328-0_39.

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Komm, Dennis. "The Knapsack Problem." In Texts in Theoretical Computer Science. An EATCS Series, 183–210. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42749-2_6.

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Korte, Bernhard, and Jens Vygen. "The Knapsack Problem." In Algorithms and Combinatorics, 471–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56039-6_17.

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Conference papers on the topic "KNAPSACK-PROBLEM"

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Bendali, F., J. Mailfert, E. Mole Kamga, A. Quilliot, and H. Toussaint. "A Synchronized Knapsack Problem." In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2022. http://dx.doi.org/10.1109/codit55151.2022.9803883.

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Boryczka, Urszula. "Ants and Multiple Knapsack Problem." In 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07). IEEE, 2007. http://dx.doi.org/10.1109/cisim.2007.12.

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Pedrosa, Lehilton L. C., Mauro R. C. Silva, and Rafael C. S. Schouery. "Complexidade do Positional Knapsack Problem." In Encontro de Teoria da Computação. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/etc.2022.222786.

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Neste resumo, apresentamos o Positional Knapsack Problem (PKP) e demonstramos que ele é NP-difícil. O PKP é uma variante do Knapsack Problem (KP) em que o valor de um item varia linearmente de acordo com a posição em que ele é adicionado. Essa mudança na valoração adiciona diversas propriedades não intuitivas ao problema que não valem para o KP e, de fato, o PKP não é uma generalização do KP. Para demonstrar a NP- dificuldade, reduzimos uma variante do Partition Problem a uma instância de PKP cuidadosamente construída, de forma a recuperar algumas propriedades normalmente esperadas para variantes do KP.
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"Online Knapsack Problem with Items Delay." In International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004832702130220.

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Uslu, Faruk Sukru. "Solving Knapsack Problem with Genetic Algorithm." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130016.

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Suryadi, Dedy, and Eric Kusnadi Kartika. "Viral Systems Application for Knapsack Problem." In 2011 3rd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2011). IEEE, 2011. http://dx.doi.org/10.1109/cicsyn.2011.16.

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Assi, Maram, and Ramzi A. Haraty. "A Survey of the Knapsack Problem." In 2018 International Arab Conference on Information Technology (ACIT). IEEE, 2018. http://dx.doi.org/10.1109/acit.2018.8672677.

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Sun, Bo, Lin Yang, Mohammad Hajiesmaili, Adam Wierman, John C. S. Lui, Don Towsley, and Danny H. K. Tsang. "The Online Knapsack Problem with Departures." In SIGMETRICS '23: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578338.3593576.

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Becker, Henrique, and Luciana S. Buriol. "UKP5: Solving the Unbounded Knapsack Problem." In I Encontro de Teoria da Computação. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/etc.2016.9835.

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Abstract:
Nesse resumo extendido nós apresentamos o UKP5, um algoritmo para solucionar o unbounded knapsack problem (problema da mochila com repetições). O UKP5 é baseado em programação dinâmica, mas implementado de uma forma não-tradicional: ao invés de olhar para trás para usar soluções de subproblemas previamente computados, ele armazena limites inferiores a frente. O UKP5 é consideravelmente mais simples que o EDUK2, o algoritmo do estado da arte para solucionar o problema. Nós executamos o UKP5 e o EDUK2 em uma bateria de testes contendo 4540 instâncias consideradas difíceis pelos autores do EDUK2. Os resultados mostram que o UKP5 é, em média, 47 vezes mais rápido que o EDUK2.
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Kalai, R., and D. Vanderpooten. "Lexicographic α-Robust Knapsack Problem : Complexity Results." In 2006 International Conference on Service Systems and Service Management. IEEE, 2006. http://dx.doi.org/10.1109/icsssm.2006.320662.

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