AIMS

Journal of Industrial and Management Optimization (JIMO)

ISSN 1547-5816(print) ISSN 1553-166X(online)	Current volume Go	Journal archive Go	Advanced Search
	JIMO is covered in Science Citation Index Expanded, CompuMath Citation Index, Current Contents/Engineering, Computing and Technology ISI Alerting Services. JIMO is an international journal devoted to publishing peer-reviewed, high quality, original papers on the non-trivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques. It is particularly hoped that the study of these practical problems will lead to the discovery of new ideas and the development of novel methodologies in optimization. JIMO is published by AIMS and sponsored by the Department of Mathematics and Statistics, Curtin University and the Department of Mathematics, Zhejiang University.

TOP 10 Most Read Articles in JIMO, May 2012

1	A nonsmooth Newton's method for discretized optimal control problems with state and control constraints Volume 4, Number 2, Pages: 247 - 270, 2008 Matthias Gerdts and Martin Kunkel Abstract Full Text Related Articles We investigate a nonsmooth Newton's method for the numerical solution of discretized optimal control problems subject to pure state constraints and mixed control-state constraints. The infinite dimensional problem is discretized by application of a general one-step method to the differential equation. By use of the Fischer-Burmeister function the first order necessary conditions for the discretized problem are transformed into an equivalent nonlinear and nonsmooth equation. This nonlinear and nonsmooth equation is solved by a globally convergent nonsmooth Newton's method. Numerical examples for the minimum energy problem and the optimal control of a robot conclude the article.
2	Spline function smooth support vector machine for classification Volume 3, Number 3, Pages: 529 - 542, 2007 Yubo Yuan, Weiguo Fan and Dongmei Pu Abstract Full Text Related Articles Support vector machine (SVM) is a very popular method for binary data classification in data mining (machine learning). Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of good optimal algorithms can't be used to find the solution. In order to overcome this model's non-smooth property, Lee and Mangasarian proposed smooth support vector machine (SSVM) in 2001. Later, Yuan et al. proposed the polynomial smooth support vector machine (PSSVM) in 2005. In this paper, a three-order spline function is used to smooth the objective function and a three-order spline smooth support vector machine model (TSSVM) is obtained. By analyzing the performance of the smooth function, the smooth precision has been improved obviously. Moreover, BFGS and Newton-Armijo algorithms are used to solve the TSSVM model. Our experimental results prove that the TSSVM model has better classification performance than other competitive baselines.
3	A new exact penalty function method for continuous inequality constrained optimization problems Volume 6, Number 4, Pages: 895 - 910, 2010 Changjun Yu, Kok Lay Teo, Liansheng Zhang and Yanqin Bai Abstract References Full Text Related Articles In this paper, a computational approach based on a new exact penalty function method is devised for solving a class of continuous inequality constrained optimization problems. The continuous inequality constraints are first approximated by smooth function in integral form. Then, we construct a new exact penalty function, where the summation of all these approximate smooth functions in integral form, called the constraint violation, is appended to the objective function. In this way, we obtain a sequence of approximate unconstrained optimization problems. It is shown that if the value of the penalty parameter is sufficiently large, then any local minimizer of the corresponding unconstrained optimization problem is a local minimizer of the original problem. For illustration, three examples are solved using the proposed method. From the solutions obtained, we observe that the values of their objective functions are amongst the smallest when compared with those obtained by other existing methods available in the literature. More importantly, our method finds solution which satisfies the continuous inequality constraints.
4	A smoothing scheme for optimization problems with Max-Min constraints Volume 3, Number 2, Pages: 209 - 222, 2007 X. X. Huang, Xiaoqi Yang and K. L. Teo Abstract Full Text Related Articles In this paper, we apply a smoothing approach to a minimization problem with a max-min constraint (i.e., a min-max-min problem). More specifically, we first rewrite the min-max-min problem as an optimization problem with several min-constraints and then approximate each min-constraint function by a smooth function. As a result, the original min-max-min optimization problem can be solved by solving a sequence of smooth optimization problems. We investigate the relationship between the global optimal value and optimal solutions of the original min-max-min optimization problem and that of the approximate smooth problem. Under some conditions, we show that the limit points of the first-order (second-order) stationary points of the smooth optimization problems are first-order (second-order) stationary points of the original min-max-min optimization problem.
5	A penalty function algorithm with objective parameters for nonlinear mathematical programming Volume 5, Number 3, Pages: 585 - 601, 2009 Zhiqing Meng, Qiying Hu and Chuangyin Dang Abstract Full Text Related Articles In this paper, we present a penalty function with objective parameters for inequality constrained optimization problems. We prove that this type of penalty functions has good properties for helping to solve inequality constrained optimization problems. Moreover, based on the penalty function, we develop an algorithm to solve the inequality constrained optimization problems and prove its convergence under some conditions. Numerical experiments show that we can obtain a satisfactorily approximate solution for some constrained optimization problems as the same as the exact penalty function.
6	Supply chain inventory management via a Stackelberg equilibrium Volume 2, Number 1, Pages: 81 - 94, 2006 Yeong-Cheng Liou, Siegfried Schaible and Jen-Chih Yao Abstract Full Text Related Articles In this paper we consider one-buyer, one-seller, finite horizon, multi-period inventory models in which the economic order quantity is integrated with the economic production quantity (EOQ-EPQ in short). We introduce the Stackelberg equilibrium framework in which the objective is to maximize the vendor's total benefit subject to the minimum total cost that the buyer is willing to incur. Some existence results, optimality conditions and the optimal replenishment policy under the Stackelberg equilibrium concept are obtained and a numerical algorithm and examples are presented to find the optimal replenishment policy in practice.
7	On second order symmetric duality in nondifferentiable multiobjective programming Volume 5, Number 4, Pages: 697 - 703, 2009 Xinmin Yang Abstract Full Text Related Articles In this paper, we point out an inconsistency between assumptions and results on the second order strong and converse duality in a recent paper of I. Ahmad ( Information Sciences 173 (2005) 23-34). We then provide appropriate modifications to rectify this deficiency.
8	Global optimization algorithm for solving bilevel programming problems with quadratic lower levels Volume 6, Number 1, Pages: 177 - 196, 2009 Paul B. Hermanns and Nguyen Van Thoai Abstract Full Text Related Articles In this article, we propose a method for finding the global optimum of a class of nonlinear bilevel programming problems. The main idea of this method is to construct iteratively a sequence of points either ending at an optimal solution of the equivalent problem with a complementarity constraint, or converging to an optimal solution. The construction of such a sequence is performed by using a branch-and-bound scheme, together with some relaxation techniques, which are successfully applied in global optimization. Some illustrative examples and results on computational experiments are reported.
9	A smoothing approach for semi-infinite programming with projected Newton-type algorithm Volume 5, Number 1, Pages: 141 - 151, 2008 Zhi Guo Feng, Kok Lay Teo and Volker Rehbock Abstract Full Text Related Articles In this paper we apply the projected Newton-type algorithm to solve semi-infinite programming problems. The infinite constraints are replaced by an equivalent nonsmooth function which is then approximated by a smoothing function. The KKT system is formulated as a nonsmooth equation. We then apply the projected Newton-type algorithm to solve this equation and show that the accumulation point satisfies the KKT system. Some numerical results are presented for illustration.
10	A filled function method for constrained nonlinear integer programming Volume 4, Number 2, Pages: 353 - 362, 2008 Yongjian Yang, Zhiyou Wu and Fusheng Bai Abstract Full Text Related Articles A filled function method is presented in this paper to solve constrained nonlinear integer programming problems. It is shown that for a given non-global local minimizer, a better local minimizer can be obtained by local search staring from an improved initial point which is obtained by locally solving a box-constrained integer programming problem. Several illustrative numerical examples are reported to show the efficiency of the present method.

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