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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, August 2015
1 
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.

2 
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.

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
References
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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 
On the multiserver machine interference with modified Bernoulli vacation
Volume 10, Number 4, Pages: 1191  1208, 2014
TzuHsin Liu
and JauChuan Ke
Abstract
References
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We study the multiserver machine interference problem with repair
pressure coefficient and a modified Bernoulli vacation. The repair
rate depends on the number of failed machines waiting in the system.
In congestion, the server may increase the repair rate with pressure
coefficient $\theta$ to reduce the queue length. At each repair completion of
a server, the server may go for a vacation of random length with
probability $p$ or may continue to repair the next failed machine, if
any, with probability $1p$. The entire system is modeled as a
finitestate Markov chain and its steady state distribution is
obtained by a recursive matrix approach. The major performance
measures are evaluated based on this distribution. Under a cost
structure, we propose to use the QuasiNewton method and
probabilistic global search Lausanne method to search for the global
optimal system parameters. Numerical examples are presented to
demonstrate the application of our approach.

6 
Relaxed augmented Lagrangianbased proximal point algorithms for convex optimization with linear constraints
Volume 10, Number 3, Pages: 743  759, 2013
Yuan Shen,
Wenxing Zhang
and Bingsheng He
Abstract
References
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The classical augmented Lagrangian method (ALM) is an efficient method for solving convex optimization with linear constraints.
However, the efficiency of ALM, to some extent, is hinged by the computational efforts on solving the resulting subproblems.
For the convex optimization with some favorable structures, e.g., either the objective function is separable or the matrices in linear constraints are wellposed, a relaxation to the subproblems of ALM can substantially result in solutions with closedform. Unfortunately, the relaxation skill can not be extended directly to the generic convex optimization without special structures,
particularly for the case of objective function with coupled variables. In this paper, by further relaxing the resulting subproblems of ALM, we propose several novel augmented Lagrangianbased proximal point algorithms. Algorithmically, the next iterate is produced by integrating the predictor, which is obtained in either primaldual or dualprimal order, with the current iterate. Numerical results demonstrate the promising performances of the proposed algorithms.

7 
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.

8 
Information sharing in a maketostock supply chain
Volume 10, Number 4, Pages: 1169  1189, 2014
Juliang Zhang
and Jian Chen
Abstract
References
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This paper addresses how different coordination mechanisms affect the
information sharing behavior in a supply chain. We study information sharing
in a maketostock supply chain under wholesale contract and revenue sharing
contract. Under wholesale contract, we show that information sharing is
always beneficial to the supplier and identify the conditions ensuring that information sharing is beneficial to the
retailer. Under revenue sharing contract, information sharing is beneficial
to the supplier, the retailer and the supply chain. This research indicates
that whether sharing the demand information is beneficial depends on the
coordination mechanism and parameters.

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 
On minimax fractional programming problems involving generalized $(H_p,r)$invex functions
Volume 10, Number 4, Pages: 1001  1018, 2014
Anurag Jayswal,
Ashish Kumar Prasad
and Izhar Ahmad
Abstract
References
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Related Articles
In the present paper, we move forward in the study of minimax fractional programming problem and establish sufficient
optimality conditions under the assumptions of generalized $(H_p,r)$invexity. Weak, strong and strict converse duality
theorems are also derived for two types of dual models related to minimax fractional programming problem involving aforesaid
invex functions. In order to show the existence of introduced class of functions, examples are given.

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