• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• Management Science and Financial Engineering
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• v.16 no.1
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.81-98
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• 1997

Given a rooted tree T with profits and node demands, the capacitated subtree of a tree problem (GSTP) consists of finding a rooted subtree of maximum profit, subject to having total demand no larger than the given capacity H. We first define the so-called critical item for CSTP and find an upper bound on the optimal value of CSTP in O(n$^{2}$) time, where n is the number of nodes in T. We then present our branch and bound algorithm for solving CSTP and illustrate the algiruthm by using an example. Finally, we implement our branch-and-bound algorithm and compare the computational results with those for both CPLEX and a dynamic programming algorithm. The comparison shows that our branch-and-bound algorithm performs much better than both CPLEX and the dynamic programming algorithm, where n and H are the range of [50, 500] and [5000, 10000], respectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.69-85
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• 2006

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1-8
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• 2002

A Branch-and-Cut algorithm is a branch-and-bound algorithm in which rutting planes are generated throughout the branch-and-bound tree. It is now one of the most widespread and successful methods for solving mixed integer programming problems. In this paper we presents efficient implementation techniques of branch-and-cut algorithm for miked integer programming problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.1-9
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• 2010

In this paper, we consider a two-machine re-entrant permutation flowshop scheduling problem with the objective of minimizing total flowtime, and suggest branch and bound algorithms for the scheduling problem. In this scheduling problem, each job must be processed twice on each machine, that is, each job should be processed on the two machines in the order of machine 1, machine 2 and then machine 1 and machine 2. In this research, based on the results of existing researches for re-entrant permutation flowshop scheduling problems, various dominance properties, lower bound and heuristic algorithm are suggested for the problem, and those are used to develop branch and bound algorithms. In the computational experiments for evaluation of the performance of the algorithms, the suggested branch and bound algorithms are tested on randomly generated test problems and results are reported.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.9-18
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• 1996

Many algorithms have been developed for optimizing the asymmectric traveling salesman problem known as a representative NP-Complete problem. The most efficient ones of them are branch and bound algorithms based on the subtour elimination approach. To increase efficiency of the branch and bound algorithm. number of decision nodes should be decreased. For this the minimum bound that is more close at the optimal solution should be found or an effective bounding strategy should be used. If the optimal solution has been known, we may apply it usefully to branching. Because a good feasible solution should be found as soon as possible and have similar features of the optimal solution. By the way, the upper bound solution in branch and bound algorithm is most close at the optimal solution. Therefore, the upper bound solution can be used instead of the optimal solution and information of which can be applied to new branching criteria. As mentioned above, this paper will propose an effective branching rule using the information of the upper bound solution in the branch and bound algorithm. And superiority of the new branching rule will be shown by comparing with Bellmore-Malone's one and carpaneto-Toth's one that were already proposed.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.2
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• pp.73-81
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• 1975

In an earlier paper ('Scheduling Jobs on a Number of Identical Machines' by Elmaghraby and Park, March 1974, AIIE Transactions) a branch-and-bound algorithm was developed for the sequencing problem when all jobs are available to process at time zero and are independet (i.e., there are not a priori precedence relationships among jobs.). However, the amount of computation required by the algorithm was not considered to be short if more than 50 jobs were processed. As an effort to improve the algorithm, the present paper modifies the implicit enumeration procedure in the algorithm so that moderately large problems can be treated with what appears to be a short computational time. Mainly this paper is concerned with improving the lower bound in the implicit enumeration procedure. The computational experiences with this new branch-and-bound algorithm are given.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.61-70
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• 2005

In this Paper, we develop an improved branch and bound algorithm for the (un)weighted unconstrained two-dimensional cutting problem. In the proposed algorithm, we improve the branching strategies of the existing exact algorithm and reduce the size of problem by removing the dominated pieces from the problem. We apply the newly Proposed definition of dominated cutting pattern and it can reduce the number of nodes that must be searched during the algorithm procedure. The efficiency of the proposed algorithm is presented through comparison with the exact algorithm known as the most efficient.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.48-56
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• 2024

We examine a single machine scheduling problem with step-improving jobs in which job processing times decrease step-wisely over time according to their starting times. The objective is to minimize total completion time which is defined as the sum of completion times of jobs. The total completion time is frequently considered as an objective because it is highly related to the total time spent by jobs in the system as well as work-in-progress. Many applications of this problem can be observed in the real world such as data gathering networks, system upgrades or technological shock, and production lines operated with part-time workers in each shift. Our goal is to develop a scheduling algorithm that can provide an optimal solution. For this, we present an efficient branch and bound algorithm with an assignment-based node design and tight lower bounds that can prune branch and bound nodes at early stages and accordingly reduce the computation time. In numerical experiments well designed to consider various scenarios, it is shown that the proposed algorithm outperforms the existing method and can solve practical problems within reasonable computation time.