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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, January 2017
1 
Theory and applications of optimal control problems with multiple timedelays
Volume 10, Number 2, Pages: 413  441, 2013
Laurenz Göllmann
and Helmut Maurer
Abstract
References
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In this paper we study optimal control problems with multiple time delays in control and state and
mixed type controlstate constraints. We derive necessary
optimality conditions in the form of a Pontryagin type Minimum Principle.
A discretization method is presented by which the delayed control problem is transformed
into a nonlinear programming problem. It is shown that the associated Lagrange multipliers provide a consistent
numerical approximation for the adjoint variables of the delayed optimal control problem. We illustrate
the theory and numerical approach on an analytical example and an optimal control model from immunology.

2 
Solving structural engineering design optimization problems using an artificial bee colony algorithm
Volume 10, Number 3, Pages: 777  794, 2013
Harish Garg
Abstract
References
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The main goal of the present paper is to solve structural engineering design optimization problems with nonlinear resource constraints. Real world problems in engineering domain are generally large scale or nonlinear or constrained optimization problems. Since heuristic methods are powerful than the traditional numerical methods, as they don't requires the derivatives of the functions and provides near to the global solution. Hence, in this article, a penalty guided artificial bee colony (ABC) algorithm is presented to search the optimal solution of the problem in the feasible region of the entire search space. Numerical results of the structural design optimization problems are reported and compared. As shown, the solutions by the proposed approach are all superior to those best solutions by typical approaches in the literature. Also we can say, our results indicate that the proposed approach may yield better solutions to engineering problems than those obtained using current algorithms.

3 
A hybrid method combining genetic algorithm and
HookeJeeves method for constrained global optimization
Volume 10, Number 4, Pages: 1279  1296, 2014
Qiang Long
and Changzhi Wu
Abstract
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A new global optimization method combining genetic
algorithm and HookeJeeves method to solve a class of constrained
optimization problems is studied in this paper. We first introduce
the quadratic penalty function method and the exact penalty function
method to transform the original constrained optimization problem
with general equality and inequality constraints into a sequence
of optimization problems only with box constraints. Then, the
combination of genetic algorithm and HookeJeeves method is
applied to solve the transformed optimization problems. Since
HookeJeeves method is good at local search, our proposed method
dramatically improves the accuracy and convergence rate of genetic
algorithm. In view of the derivativefree of HookeJeeves method,
our method only requires information of objective function value
which not only can overcome the computational difficulties caused
by the illcondition of the square penalty function, but also can
handle the nondifferentiability by the exact penalty function.
Some wellknown test problems are investigated. The numerical
results show that our proposed method is efficient and robust.

4 
Humanitarian logistics planning for natural disaster response with Bayesian information updates
Volume 10, Number 3, Pages: 665  689, 2013
Nan Liu
and Yong Ye
Abstract
References
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The current study proposes a sequential approach for humanitarian logistics in natural disasters based on the Bayesian group information updates (GIU). First, a dynamic timedependent nonlinear model without GIU is proposed. Then, two losses are addressed to explain the influence of a disaster on supply, demand, and humanitarian logistics. The two losses include losses caused by the mismatch between supply and demand in affected areas and the time losses caused by logistics processes under emergency conditions. Therefore, a multiperiod humanitarian logistics planning model with GIU is established based on the model without GIU using Bayesian theory. Then, the model with GIU is revised into a singleobjective model, and then a matrixcodingbased genetic algorithm is developed to solve the revised model. Finally, the proposed methodology is applied to the humanitarian logistics problems of emergency response encountered during the Wenchuan Earthquake in China. Computational results show that the proposed methodology can generate specific logistics plans for allocating relief resources according to updated information. Therefore, emergency planners can gain insights for humanitarian logistics planning in natural disaster response by inputting their own sets of data.

5 
CVaR proxies for minimizing scenariobased ValueatRisk
Volume 10, Number 4, Pages: 1109  1127, 2014
Helmut Mausser
and Oleksandr Romanko
Abstract
References
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Minimizing VaR, as estimated from a set of scenarios, is a difficult integer programming problem. Solving the problem to optimality may demand using only a small number of scenarios, which leads to poor outofsample performance. A simple alternative is to minimize CVaR for several different quantile levels and then to select the optimized portfolio with the best outofsample VaR. We show that this approach is both practical and effective, outperforming integer programming and an existing VaR minimization heuristic. The CVaR quantile level acts as a regularization parameter and, therefore, its ideal value depends on the number of scenarios and other problem characteristics.

6 
Dynamic optimization models in finance: Some extensions to the framework, models, and computation
Volume 10, Number 4, Pages: 1129  1146, 2014
Bruce D. Craven
and Sardar M. N. Islam
Abstract
References
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Both mathematical characteristics
and computational aspects of dynamic optimization in finance
have potential for extensions.
Various proposed extensions are presented in this paper for
dynamic optimization modelling in finance, adapted from developments in other
areas of economics and mathematics. They show the need and potential for
further areas of study and extensions in financial modelling.
The extensions discussed and made concern (a) incorporation of the elements of a dynamic optimization
model, (b) an improved model including physical capital, (c) some computational
experiments. These extensions make dynamic financial optimisation relatively
more organized, coherent and coordinated. These extensions are relevant for applications of financial models
to academic and practical exercises. This paper reports initial efforts in
providing some useful extensions; further work is necessary to complete the
research agenda.

7 
Determining optimal price, replenishment lot size and number of shipments for an EPQ model with rework and multiple shipments
Volume 11, Number 4, Pages: 1059  1071, 2015
Ata Allah Taleizadeh,
Solaleh Sadat Kalantari
and Leopoldo Eduardo CárdenasBarrón
Abstract
References
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In a regular manufacturing process at the end of each production
cycle defective items are identified. Then some defective items are
considered scrap and the others, after a reworking process with a
defective rate, could convert in a perfect quality items. In this
direction, this research work deals with the problem of the joint
determination of selling price, replenishment lot size and the
number of shipments for an economic production quantity (EPQ) model
with rework of defective items when multishipment policy is
used. After proving concavity of the longrun average benefit
function, a practical algorithm is developed to find the optimal
price, replenishment lot size and number of shipments in order to
maximize the average longrun benefit function. A special case when
there is no scrap is identified and explained. Furthermore, in order
to show the practical usage of the proposed algorithm a case study
from real world is presented and solved.

8 
Finitetime optimal consensus control for secondorder multiagent systems
Volume 10, Number 3, Pages: 929  943, 2013
Rui Li
and Yingjing Shi
Abstract
References
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We propose an optimal consensus design method for solving a finitetime optimal control problem involving a secondorder multiagent system. With this method, the optimal consensus problem can be modeled as an optimal parameter selection problem with continuous state inequality constraints and free terminal time. By virtue of the constraint transcription method and a time scaling transform method, a gradientbased optimization algorithm is developed to solve this optimal parameter selection problem. Furthermore, a new consensus protocol is designed, by which the consensus value of the system velocity can be chosen to be an arbitrary value. For illustration, simulation studies are carried out to demonstrate the proposed method.

9 
Linear programming technique for solving intervalvalued constraint matrix games
Volume 10, Number 4, Pages: 1059  1070, 2014
JiangXia Nan
and DengFeng Li
Abstract
References
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The purpose of this paper is to propose an effective linear programming technique for solving matrix games in which the payoffs are expressed with intervals and the choice of strategies for players is constrained, i.e., intervalvalued constraint matrix games. Because the payoffs of the intervalvalued constraint matrix game are intervals, its value is an interval as well. In this methodology, the value of the intervalvalued constraint matrix game is regarded as a function of values in the payoff intervals, which is proven to be monotonous and nondecreasing. By the duality theorem of linear programming, it is proven that both players always have the identical intervaltype value and hereby the intervalvalued constraint matrix game has an intervaltype value. A pair of auxiliary linear programming models is derived to compute the upper bound and the lower bound of the value of the intervalvalued constraint matrix game by using the upper bounds and the lower bounds of the payoff intervals, respectively. Validity and applicability of the linear programming technique proposed in this paper is demonstrated with a numerical example of the market share game problem.

10 
BarzilaiBorweinlike methods for the extreme eigenvalue problem
Volume 11, Number 3, Pages: 999  1019, 2014
Huan Gao,
YuHong Dai
and XiaoJiao Tong
Abstract
References
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We consider numerical methods for the extreme eigenvalue problem of large scale symmetric positive definite matrices. By the variational principle, the extreme eigenvalue can be obtained by minimizing some unconstrained optimization problem. Firstly, we propose two adaptive nonmonotone BarzilaiBorweinlike methods for the unconstrained optimization problem. Secondly, we prove the global convergence of the two algorithms under some conditions. Thirdly, we compare our methods with eigs
and the power method for the standard test problems from the UF Sparse Matrix Collection. The primary numerical experiments indicate that the two algorithms are promising.

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