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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 peerreviewed, high quality, original papers on the nontrivial 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 Curtin University and Zhejiang University.
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TOP 10 Most Read Articles in JIMO, March 2017
1 
Supply chain inventory management via a Stackelberg equilibrium
Volume 2, Number 1, Pages: 81  94, 2006
YeongCheng Liou,
Siegfried Schaible
and JenChih Yao
Abstract
Full Text
Related Articles
In this paper we consider onebuyer, oneseller, finite horizon,
multiperiod inventory models in which the economic order quantity
is integrated with the economic production quantity (EOQEPQ 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.

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 nonsmooth
function, a lot of good optimal algorithms can't be used to find
the solution. In order to overcome this model's nonsmooth
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
threeorder spline function is used to smooth the objective
function and a threeorder 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 NewtonArmijo 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 
Cluster synchronization for linearly coupled complex
networks
Volume 7, Number 1, Pages: 87  101, 2011
Xiwei Liu,
Tianping Chen
and Wenlian Lu
Abstract
References
Full Text
Related Articles
In this paper, the cluster synchronization for an array of linearly
coupled identical chaotic systems is investigated. New coupling
schemes (or coupling matrices) are proposed, by which global cluster
synchronization of linearly coupled chaotic systems can be realized.
Here, the number and the size of clusters (or groups) can be
arbitrary. Some sufficient criteria to ensure global cluster
synchronization are derived. Moreover, for any given coupling
matrix, new coupled complex networks with adaptive coupling
strengths are proposed, which can synchronize coupled chaotic
systems by clusters. Numerical simulations are finally given to show
the validity of the theoretical results.

4 
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.

5 
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) 2334). We then
provide appropriate modifications to rectify this deficiency.

6 
A smoothing scheme for optimization problems with MaxMin 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 maxmin constraint (i.e., a minmaxmin problem). More specifically, we first rewrite the minmaxmin problem as an optimization problem with several minconstraints and then approximate each minconstraint function by a smooth function. As a result, the original minmaxmin 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 minmaxmin optimization problem and that of the approximate smooth problem. Under some conditions, we show that the limit points of the firstorder (secondorder) stationary points of the smooth optimization problems are firstorder (secondorder) stationary points of the original minmaxmin optimization problem.

7 
Time delayed optimal control problems with multiple characteristic time points:
Computation and industrial applications
Volume 5, Number 4, Pages: 705  718, 2009
Ling Yun Wang,
Wei Hua Gui,
Kok Lay Teo,
Ryan Loxton
and Chun Hua Yang
Abstract
Full Text
Related Articles
In this paper, we consider a class of optimal control problems
involving time delayed dynamical systems and subject to continuous
state inequality constraints. We show that this type of problem can
be approximated by a sequence of time delayed optimal control
problems subject to inequality constraints in canonical form and
with multiple characteristic time points appearing in the cost and
constraint functions. We derive formulae for the gradient of the
cost and constraint functions of the approximate problems. On this
basis, each approximate problem can be solved using a gradientbased
optimization technique. The computational method obtained is then
applied to an industrial problem arising in the study of
purification process of zinc sulphate electrolyte. The results are
highly satisfactory.

8 
Human resource management using working time accounts with expiry of hours
Volume 5, Number 3, Pages: 569  584, 2009
Albert Corominas,
Amaia Lusa
and Rafael Pastor
Abstract
Full Text
Related Articles
Herein is presented a human resource management system based on a working time account (WTA) in which accumulated hours
expire after a certain date, whether those owed by the employee to the company, or vice versa. The condition of
hoursexpiry limits flexibility but protects workers. The consideration of this feature enables modelling of many
current industrial scenarios, at the expense of complicating the use of WTAs and hugely increasing the size of the
models. A staff planning problem from the services industry is modelled and solved through mathematical programming,
and the approach is shown to be efficient for realistic staff sizes. Lastly, a variety of scenarios are presented, for
which the financial benefit generated by WTAs is calculated and possible compensations for workers are explored.

9 
Optimal control of
piecewise affine systems with piecewise affine state feedback
Volume 5, Number 4, Pages: 737  747, 2009
Changzhi Wu,
Kok Lay Teo
and Volker Rehbock
Abstract
Full Text
Related Articles
In this paper, we consider a class of optimal control problems involving
piecewise affine (PWA) systems with piecewise affine state feedback. We
first show that if the piecewise affine state feedback control is assumed to
be continuous at the switching boundaries, then the number of switching
amongst PWA systems is finite. On this basis, this optimal control problem
is transformed into a discrete valued optimal control problem. For this
discrete valued optimal control problem, we introduce the time scaling
transform to convert it into an equivalent constrained optimal parameter
selection problem, for which it can be solved by existing optimal control
techniques for optimal parameter selection problems. A numerical example is
solved so as to illustrate the proposed method.

10 
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.

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