Relevant bibliographies by topics / Branch and bound algorithm

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

Academic literature on the topic 'Branch and bound algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Branch and bound algorithm"

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Wang, Luzhi, Shuli Hu, Mingyang Li, and Junping Zhou. "An Exact Algorithm for Minimum Vertex Cover Problem." Mathematics 7, no. 7 (July 6, 2019): 603. http://dx.doi.org/10.3390/math7070603.

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In this paper, we propose a branch-and-bound algorithm to solve exactly the minimum vertex cover (MVC) problem. Since a tight lower bound for MVC has a significant influence on the efficiency of a branch-and-bound algorithm, we define two novel lower bounds to help prune the search space. One is based on the degree of vertices, and the other is based on MaxSAT reasoning. The experiment confirms that our algorithm is faster than previous exact algorithms and can find better results than heuristic algorithms.
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Bunnag, Dhiranuch. "Combining Interval Branch and Bound and Stochastic Search." Abstract and Applied Analysis 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/861765.

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This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.
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Utama, Dana Marsetiya. "Algoritma LPT-Branch and Bound Pada Penjadwalan Flexible Flowshop untuk Meminimasi Makespan." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 2, no. 1 (June 25, 2019): 20. http://dx.doi.org/10.21070/prozima.v2i1.1527.

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This article discussed the problem of flow shop scheduling to minimize the makespan. The purpose of this article is to develop the LPT and Branch And Bound (LPT-Branch And Bound) algorithms to minimize the makespan. The proposed method is Longest Processing Time (LPT) and Branch And Bound. Stage settlement is divided into 3 parts. To proved the proposed algorithm, a numerical experiment was conducted by comparing the LPT-LN algorithm. The result of the numerical experiment shows that LPT-Branch And Bound's proposed algorithm is more efficient than the LPT-LN algorithm.
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CLAUSEN, JENS, and JESPER LARSSON TRÄFF. "DO INHERENTLY SEQUENTIAL BRANCH-AND-BOUND ALGORITHMS EXIST?" Parallel Processing Letters 04, no. 01n02 (June 1994): 3–13. http://dx.doi.org/10.1142/s0129626494000028.

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In the construction of algorithms for [Formula: see text] optimization problems the Branch-and-Bound paradigm is an essential tool. Furthermore, Branch-and-Bound algorithms are traditionally regarded as well suited for parallel implementation due to the subdivision of the problem considered into essentially independent subproblems. In this paper we present experimental results for a Branch-and-Bound algorithm for the Graph Partitioning Problem showing that the traditional parallelization of a Branch-and-Bound algorithm does not always lead to an efficient parallel algorithm. The main reason seems to be lack of meaningful work, i.e. concurrent existence of subproblems which have to be solved to ensure optimality of the solution. We support this claim with experimental results.
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Chun-Hung Cheng. "A branch and bound clustering algorithm." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 5 (May 1995): 895–98. http://dx.doi.org/10.1109/21.376504.

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Andriansyah, Andriansyah, and Prima Denny Sentia. "PENENTUAN RUTE KENDARAAN PADA SISTEM DISTRIBUSI LOGISTIK PASCA BENCANA (STUDI KASUS)." Jurnal Manajemen Industri dan Logistik 2, no. 1 (December 4, 2018): 79–89. http://dx.doi.org/10.30988/jmil.v2i1.28.

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The success indicators of disaster mitigation can be seen from the disaster logistics system. Effective and efficient distribution network can make a good disaster logistics system. The problem that related to the design of this network is the vehicle routing problem. The objective is determined optimal route of relief distribution from warehouse to victims with minimum time duration. The problem is solved by branch and bound, insertion heuristic, and local search algorithms. The results obtained by branch and bound and local search algorithm are optimal global. Time duration of vehicle using these algoritm is 1.0562 hours. However, computation time using branch and bound algorithm is very long until 22 hours while local search algorithm only takes 60 seconds. The insertion heuristic algorithm also produces a good solution. Time duration of vehicle using this algoritm is 1,1030 hours. This solution is local optimal, but the computation time is very short, only 0.001 seconds.
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Paulavičius, Remigijus, and Julius Žilinskas. "GLOBAL OPTIMIZATION USING THE BRANCH‐AND‐BOUND ALGORITHM WITH A COMBINATION OF LIPSCHITZ BOUNDS OVER SIMPLICES." Technological and Economic Development of Economy 15, no. 2 (June 30, 2009): 310–25. http://dx.doi.org/10.3846/1392-8619.2009.15.310-325.

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Many problems in economy may be formulated as global optimization problems. Most numerically promising methods for solution of multivariate unconstrained Lipschitz optimization problems of dimension greater than 2 use rectangular or simplicial branch‐and‐bound techniques with computationally cheap, but rather crude lower bounds. The proposed branch‐and‐bound algorithm with simplicial partitions for global optimization uses a combination of 2 types of Lipschitz bounds. One is an improved Lipschitz bound with the first norm. The other is a combination of simple bounds with different norms. The efficiency of the proposed global optimization algorithm is evaluated experimentally and compared with the results of other well‐known algorithms. The proposed algorithm often outperforms the comparable branch‐and‐bound algorithms. Santrauka Daug įvairių ekonomikos uždavinių yra formuluojami kaip globaliojo optimizavimo uždaviniai. Didžioji dalis Lipšico globaliojo optimizavimo metodų, tinkamų spręsti didesnės dimensijos, t. y. n > 2, uždavinius, naudoja stačiakampį arba simpleksinį šakų ir rėžių metodus bei paprastesnius rėžius. Šiame darbe pasiūlytas simpleksinis šakų ir rėžių algoritmas, naudojantis dviejų tipų viršutinių rėžių junginį. Pirmasis yra pagerintas rėžis su pirmąja norma, kitas – trijų paprastesnių rėžių su skirtingomis normomis junginys. Gautieji eksperimentiniai pasiūlyto algoritmo rezultatai yra palyginti su kitų gerai žinomų Lipšico optimizavimo algoritmų rezultatais.
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Paulavičius, Remigijus, and Julius Žilinskas. "INFLUENCE OF LIPSCHITZ BOUNDS ON THE SPEED OF GLOBAL OPTIMIZATION." Technological and Economic Development of Economy 18, no. 1 (April 10, 2012): 54–66. http://dx.doi.org/10.3846/20294913.2012.661170.

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Global optimization methods based on Lipschitz bounds have been analyzed and applied widely to solve various optimization problems. In this paper a bound for Lipschitz function is proposed, which is computed using function values at the vertices of a simplex and the radius of the circumscribed sphere. The efficiency of a branch and bound algorithm with proposed bound and combinations of bounds is evaluated experimentally while solving a number of multidimensional test problems for global optimization. The influence of different bounds on the performance of a branch and bound algorithm has been investigated.
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Yeoh, W., A. Felner, and S. Koenig. "BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm." Journal of Artificial Intelligence Research 38 (May 23, 2010): 85–133. http://dx.doi.org/10.1613/jair.2849.

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Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.
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Jiao, Hong-Wei, Feng-Hui Wang, and Yong-Qiang Chen. "An Effective Branch and Bound Algorithm for Minimax Linear Fractional Programming." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/160262.

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An effective branch and bound algorithm is proposed for globally solving minimax linear fractional programming problem (MLFP). In this algorithm, the lower bounds are computed during the branch and bound search by solving a sequence of linear relaxation programming problems (LRP) of the problem (MLFP), which can be derived by using a new linear relaxation bounding technique, and which can be effectively solved by the simplex method. The proposed branch and bound algorithm is convergent to the global optimal solution of the problem (MLFP) through the successive refinement of the feasible region and solutions of a series of the LRP. Numerical results for several test problems are reported to show the feasibility and effectiveness of the proposed algorithm.
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Dissertations / Theses on the topic "Branch and bound algorithm"

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Rahman, Mostafizur. "Branch and Bound Algorithm for Multiprocessor Scheduling." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3790.

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The multiprocessor task graph scheduling problem has been extensively studied asacademic optimization problem which occurs in optimizing the execution time of parallelalgorithm with parallel computer. The problem is already being known as one of the NPhardproblems. There are many good approaches made with many optimizing algorithmto find out the optimum solution for this problem with less computational time. One ofthem is branch and bound algorithm.In this paper, we propose a branch and bound algorithm for the multiprocessor schedulingproblem. We investigate the algorithm by comparing two different lower bounds withtheir computational costs and the size of the pruned tree.Several experiments are made with small set of problems and results are compared indifferent sections.
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Guilbeau, Jared T. "A Vector Parallel Branch and Bound Algorithm." Thesis, University of Louisiana at Lafayette, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10242153.

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Global optimization problems sometimes attain their extrema on infinite subsets of the search space, forcing mathematically rigorous programs to require large amounts of data to describe these sets. This makes these programs natural candidates for both vectorization methods and parallel computing. Here, we give a brief overview of parallel computing and vectorization methods, exploit their availability by constructing a fully distributed implementation of a mathematically rigorous Vector Parallel Branch and Bound Algorithm using MATLAB’s SPMD architecture and interval arithmetic, and analyze the performance of the algorithm across different methods of inter-processor communication.

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Mutlu, Mustafa Cagdas. "A Branch And Bound Algorithm For Resource Leveling Problem." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612259/index.pdf.

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Resource Leveling Problem (RLP) aims to minimize undesired fluctuations in resource distribution curves which cause several practical problems. Many studies conclude that commercial project management software packages can not effectively deal with RLP. In this study a branch and bound algorithm is presented for solving RLP for single and multi resource, small size networks. The algorithm adopts a depth-first strategy and stores start times of non-critical activities in the nodes of the search tree. Optimal resource distributions for 4 different types of resource leveling metrics can be obtained via the developed procedure. To prune more of the search tree and thereby reduce the computation time, several lower bound calculation methods are employed. Experiment results from 20 problems showed that the suggested algorithm can successfully locate optimal solutions for networks with up to 20 activities. The algorithm presented in this study contributes to the literature in two points. First, the new lower bound improvement method (maximum allowable daily resources method) introduced in this study reduces computation time required for achieving the optimal solution for the RLP. Second, optimal solutions of several small sized problems have been obtained by the algorithm for some traditional and recently suggested leveling metrics. Among these metrics, Resource Idle Day (RID) has been utilized in an exact method for the first time. All these solutions may form a basis for performance evaluation of heuristic and metaheuristic procedures for the RLP. Limitations of the developed branch and bound procedure are discussed and possible further improvements are suggested.
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Jariwala, Anish. "Efficient branch and bound algorithm for the dynamic layout problem." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1179426531.

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Erken, Ozgur. "A branch-and-bound algorithm for the network diversion problem." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Dec%5FErken.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, December 2002.
Thesis advisor(s): R. Kevin Wood, Matthew Carlyle. Includes bibliographical references (p. 35). Also available online.
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Turkensteen, Marcel. "Advanced analysis of branch and bound algorithms." [S.l. : [Groningen : s.n.] ; University Library Groningen] [Host], 2006. http://irs.ub.rug.nl/ppn/299139158.

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Woodcock, Andrew John. "Solving the generalized assignment problem : a hybrid Tabu search/branch and bound algorithm." Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/17881.

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The research reported in this thesis considers the classical combinatorial optimization problem known as the Generalized Assignment Problem (GAP). Since the mid 1970's researchers have been developing solution approaches for this particular type of problem due to its importance both in practical and theoretical terms. Early attempts at solving GAP tended to use exact integer programming techniques such as Branch and Bound. Although these tended to be reasonably successful on small problem instances they struggle to cope with the increase in computational effort required to solve larger instances. The increase in available computing power during the 1980's and 1990's coincided with the development of some highly efficient heuristic approaches such as Tabu Search (TS), Genetic Algorithms (GA) and Simulated Annealing (SA). Heuristic approaches were subsequently developed that were able to obtain high quality solutions to larger and more complex instances of GAP. Most of these heuristic approaches were able to outperform highly sophisticated commercial mathematical programming software since the heuristics tend to be tailored to the problem and therefore exploit its structure. A new approach for solving GAP has been developed during this research that combines the exact Branch and Bound approach and the heuristic strategy of Tabu Search to produce a hybrid algorithm for solving GAP. This approach utilizes the mathematical programming software Xpress-MP as a Branch and Bound solver in order to solve sub-problems that are generated by the Tabu Search guiding heuristic. Tabu Search makes use of memory structures that record information about attributes of solutions visited during the search. This information is used to guide the search and in the case of the hybrid algorithm to generate sub problems to pass to the Branch and Bound solver. The new algorithm has been developed, imp lemented and tested on benchmark test problems that are extremely challenging and a comprehensive report and analysis of the experimentation is reported in this thesis.
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Zhang, Weihua. "Genepart algorithm, clustering and feature selection for DNA micro-array data." Thesis, Montana State University, 2004. http://etd.lib.montana.edu/etd/2004/zhang/ZhangW1204.pdf.

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Piva, Breno. "Estudo poliedral do problema do máximo subgrafo induzido comum." reponame:Repositório Institucional da UFS, 2009. https://ri.ufs.br/handle/riufs/1654.

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O problema do Máximo Subgrafo Induzido Comum (MSIC) pertence a classe NP-difícil e possui aplicações em diversas áreas. Apesar de sua complexidade, ainda é importante conhecer soluções exatas para instâncias deste problema. Os algoritmos exatos encontrados na literatura buscam resolvê-lo através de técnicas de backtracking ou através de sua redução para o problema da Clique Máxima. Neste trabalho procuramos dar uma solução exata para o MSIC, tratando-o diretamente através da utilização de modelos de Programação Linear Inteira (PLI) e técnicas de combinatória poliédrica. Assim, realizamos um estudo teórico do poliedro do MSIC e fomos capazes de encontrar algumas desigualdades válidas fortes, inclusive com provas de que algumas delas representam facetas daquele poliedro. Adicionalmente, provamos que existe uma equivalâencia entre o modelo PLI aqui apresentado para o MSIC e uma formulação bem conhecida para o problema da Clique Máxima. Posteriormente, foram implementados algoritmos de Branch-and-Bound (B&B) e Branch-and-Cut (B&C) utilizando as desigualdades encontradas e algumas técnicas para tentar tornar os algoritmos mais eficientes. Experimentos foram executados com os algoritmos implementados neste trabalho e, também, com um algoritmo já existente para resolver o problema da Clique, chamado Cliquer. Os resultados foram comparados e, dentre os algoritmos de PLI, constatamos que o mais eficiente foi aquele que utilizou uma formulação para o MSIC que chamamos de Clique-IS, utilizando B&B e técnicas mais básicas que outros algoritmos. Este algoritmo mostrou-se mais eficiente, inclusive, que um algoritmo PLI com um modelo baseado no problema da Clique Máaxima. Este fato sugere que para uma abordagem baseada em PLI, vale a pena utilizar uma formulação do MSIC diretamente, ao invés de uma que se apóie na redução deste para o problema da Clique Máxima. Ja a comparaçao do melhor algoritmo desenvolvido neste trabalho com o Cliquer, mostrou que este último é mais eficiente. Para que um algoritmo baseado em PLI (utilizando uma formulação com as mesmas variáveis usadas por nós) tivesse alguma chance de vencer um algoritmo combinatório como o Cliquer, seria necessário conhecer mais desigualdades que estivessem ativas na solução ótima do problema._________________________________________________________________________________________ ABSTRACT: The Maximum Common Subgraph problem (MSIC) is in MV-hard and has applications in several fields. Despite its complexity, it is still important to know exact solutions for instances of this problem. The exact algorithms found in literature try to solve it through backtracking techniques or through its reduction to the Maximum Clique problem. In this work we try to give an exact solution to MSIC by addressing it directly, using Linear Integer Programming (PLI) and polyhedral combinatorics techniques. So, we performed a study of the MSIC polyhedron and we were able to find some strong valid inequalities, including some that were proven to define facets of that polyhedron. Additionally, we proved that an equivalence between the PLI model presented here for MSIC and a well known formulation for the Maximum Clique problem exists. Later, Branch-and-Bound (B&B) and Branch-and-Cut (B&C) algorithms were implemented using the inequalities found and some techniques to try to render the algorithms more efficient. Experiments were performed with the algorithms implemented in this work and, also, with an already existing algorithm to solve the Maximum Clique problem, called Cliquer. The results were compared and, among the PLI algorithms, we found that the most efficient was the one that used the formulation which we called Clique-IS, using B&B and more basic techniques than other algorithms. This algorithm was even more efficient than a PLI algorithm with a Clique-based model. This fact suggests that for a PLI approach it is worth to use a formulation based on the MSIC polyhedron instead of one based on its reduction to the Maximum Clique problem. The comparison of the best algorithm developed in this work with Cliquer, though, showed that the latest is more efficient. In order to some PLI-based algorithm (using a formulation with the same variables used by us) to have any chance of outperforming a combinatorial algorithm like Cliquer, it would be necessary to know more inequalities that are active in the problem's optimal solution.
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Hansen-Tangen, Jakob G., and Sindre Dombu Sangnes. "A Logic Branch and Bound Algorithm for Petroleum Production Optimization Based on Generalized Disjunctive Programming." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for industriell økonomi og teknologiledelse, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-24842.

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A new solution method for solving the real time production optimization (RTPO) problem for a petroleum production system is presented in this thesis. The objective function of the problem maximizes oil production and the RTPO handles decision variables at operational level. Including routing of production flows, lift gas allocation, and pressure configurations of the system. It is aimed to give decision support in a time horizon of days to weeks. Such problems require solution methods able to obtain solutions swiftly, as production planners adjust network components frequently to maintain optimal production.The problem contains binary decision variables combined with nonlinear expressions and is mathematically classified as a nonconvex mixed integer nonlinear problem (MINLP). MINLPs are in general known as computationally expensive and hard to solve to optimality, and when nonconvexities are present, few solvers can guarantee global optimality. The solution method presented deviates from traditional optimization techniques applied to such problems, and introduces logic disjunctions to substitute the binary variables of the MINLP. A specialized branch and bound algorithm (LBB) is developed to utilize the structure of these disjunctions, and as time is of paramount importance for the RTPO, it is aimed to reduce demanded computational effort for the problem. The LBB is given a high degree of user flexibility to be able to tailor the algorithm to different problems.Results of the LBB show substantial variation in solution efficiency when applied to a real petroleum production system. Only when specific problem knowledge is utilized to customize the algorithm to the current system, the algorithm provides solid reduction in computational effort compared to a recognized commercial solver. Also when applied to variations in system structure the LBB clearly outperforms the applied solver, and the effectiveness and robustness of the proposed algorithm when utilizing problem specific knowledge is confirmed. The fact that the LBB provides the same solution to the problem as the applied solver might also indicate that the nonconvexities of the problem are not as complex as expected, and that the solver is in fact able to find the global optimal solution.
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Books on the topic "Branch and bound algorithm"

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Rocktäschel, Stefan. A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29149-5.

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Moursli, Omar. Scheduling the hybrid flowshop: Branch and bound algorithms. Louvain-la-Neuve: CIACO, 1999.

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Kedia, Pradeep. Optimal solution of set covering problems using dual heuristics. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1987.

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Stephanie, Stahl, ed. Branch-and-bound applications in combinatorial data analysis. New York: Springer, 2005.

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1950-, Bushnell Michael L., ed. Efficient branch and bound search with application to computer-aided design. Boston: Kluwer Academic Publishers, 1996.

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Chen, Xinghao. Efficient Branch and Bound Search with Application to Computer-Aided Design. Boston, MA: Springer US, 1996.

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Hajian, Mozafar Taghi. Design, implementation and testing of an integrated branch and bound algorithm for piecewise linear and discrete programming problems within an LP framework. Uxbridge: Brunel University, Department of Mathematics and Statistics, 1992.

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Automatic verification of sequential infinite-state processes. Berlin: Springer, 1997.

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Automatic verification of sequential infinite-state processes. Berlin: Springer, 1997.

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Turpin, Heather Jane. The branch-and-bound paradigm. Norwich: University of East Anglia, 1990.

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Book chapters on the topic "Branch and bound algorithm"

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Dorndorf, Ulrich. "A Branch-and-Bound Algorithm." In Project Scheduling with Time Windows, 67–101. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-642-57506-8_5.

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Sprecher, Arno. "A Branch and Bound Algorithm." In Lecture Notes in Economics and Mathematical Systems, 34–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-48397-4_5.

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Huy, Toàn Phan. "A Branch-and-Bound Algorithm." In Lecture Notes in Economics and Mathematical Systems, 113–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58335-3_5.

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Scholz, Daniel. "The geometric branch-and-bound algorithm." In Deterministic Global Optimization, 15–24. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1951-8_2.

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Bárta, Jaroslav, Olga Štěpánková, and Michal Pěchouček. "Distributed Branch and Bound Algorithm in Coalition Planning." In Multi-Agent Systems and Applications II, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45982-0_8.

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Leenen, Iwin, and Iven Van Mechelen. "A Branch-and-bound Algorithm for Boolean Regression." In Classification, Data Analysis, and Data Highways, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72087-1_19.

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Rocktäschel, Stefan. "A basic Branch-and-Bound algorithm for (MOMICP)." In A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization, 17–39. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29149-5_3.

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Schwartz, Birgit. "Parallelisierung von Branch-and-Bound-Algorithmen." In Parallelverarbeitung in Rechnernetzen und betriebswirtschaftliche Planung, 103–61. Wiesbaden: Deutscher Universitätsverlag, 1994. http://dx.doi.org/10.1007/978-3-322-97688-8_5.

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Chen, Xinghao, and Michael L. Bushnell. "The Sest Algorithm." In Efficient Branch and Bound Search with Application to Computer-Aided Design, 59–73. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1329-8_6.

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Rocktäschel, Stefan. "Enhancing Algorithm 1." In A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization, 41–49. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29149-5_4.

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Conference papers on the topic "Branch and bound algorithm"

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Hong-Gui Li and Xing-Guo Li. "Image segmentation with pseudo branch and bound algorithm." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212215.

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Sze, L., and C. H. Leung. "Branch and bound algorithm for the Bayes classifier." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546914.

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Elghariani, Ali, and Michael D. Zoltowski. "Branch and bound algorithm for code spread OFDM." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319838.

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Blazewicz, Jacek, Marek Figlerowicz, and Agnieszka Rybarczyk. "Branch and bound algorithm for nonenzymatic RNA degradation." In 2008 1st International Conference on Information Technology (IT 2008). IEEE, 2008. http://dx.doi.org/10.1109/inftech.2008.4621679.

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Dimopoulos, Alexandros C., Christos Pavlatos, and George Papakonstantinou. "A General Purpose Branch and Bound Parallel Algorithm." In 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2016. http://dx.doi.org/10.1109/pdp.2016.33.

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Israel, Johannes, Andreas Fischer, and John Martinovic. "A Branch-and-Bound Algorithm for Discrete Receive Beamforming with Improved Bounds." In 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB). IEEE, 2015. http://dx.doi.org/10.1109/icuwb.2015.7324395.

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Kolpakov, Roman, and Mikhail Posypkin. "The lower bound on complexity of parallel branch-and-bound algorithm for subset sum problem." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965329.

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Yoo, John Jung-Woon, and Anirudh Aryasomayajula. "Branch-and-Bound Algorithm for Interface-Based Modular Product Design." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70712.

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Abstract:
In our earlier work we have proposed a collaboration system for modular product design. One of the main components of the system is a design repository to which suppliers can upload their component descriptions using machine-readable, interface-based component description language, so that manufacturers can refer to the descriptions during product design phases. A mathematical formulation for modular product design has been proposed based on Artificial Intelligence Planning framework. The proposed Binary Integer Programming formulation generates the optimal design of a product. The optimal design consists of multiple components that are compatible with each other in terms of input and out interfaces. However, the mathematical approach is faced with scalability issue. The development of a heuristic algorithm that generates a high quality solution within a reasonable amount of time is the final goal of the research. In this paper, we propose an algorithmic approach based on branch-and-bound method as an intermediate step for the final goal. This paper describes the details of the proposed branch-and-bound algorithm using a case study and experimental results are discussed.
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Patil, S., and P. Banerjee. "A parallel branch and bound algorithm for test generation." In the 1989 26th ACM/IEEE conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74382.74439.

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Bendjoudi, A., N. Melab, and E. G. Talbi. "Fault-Tolerant Mechanism for Hierarchical Branch and Bound Algorithm." In Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.339.

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Reports on the topic "Branch and bound algorithm"

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Miller, D. L., J. F. Pekny, and G. L. Thompson. An Exact Two-Matching Based Branch and Bound Algorithm for the Symmetric Traveling Salesman Problem. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada237878.

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Subrahmanian, V. S., Dana Nau, and C. Vago. WFS + Branch and Bound = Stable Models. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada455012.

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Washburn, Alan R. Branch and Bound Methods for Search Problems. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada294522.

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Byrd, R. H., L. Peihuang, and J. Nocedal. A limited-memory algorithm for bound-constrained optimization. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/204262.

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ECKSTEIN, JONATHAN, WILLIAM E. HART, and CYNTHIA A. PHILLIPS. PICO: An Object-Oriented Framework for Branch and Bound. Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/771506.

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Balas, Egon, and Maria C. Carrera. A Dynamic Subgradient-Based Branch and Bound Procedure for Set Covering. Revision,. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada257416.

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Trienekens, Harry W. Parallel Branch and Bound on an MIMD (Multiple Instruction Stream, Multiple Data Stream) System. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada178816.

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