Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.     

A new constraint handling method based on the modified Alopex-based evolutionary algorithm

In this paper, a new constraint handling method based on a modified AEA (Alopex-based evolutionary algorithm) is proposed. Combined with a new proposed ranking and selecting strategy, the algorithm gradually converges to a feasible region from a relatively feasible region. By introduction of an adaptive relaxation parameter μ, the algorithm fully takes into account different functions corresponding to different sizes of feasible region. In addition, an adaptive penalty function method is employed, which adaptively adjust the penalty coefficient so as to guarantee a moderate penalty. By solving 11 benchmark test functions and two engineering problems, experiment results indicate that the proposed method is reliable and efficient for solving constrained optimization problems. Also, it has great potential in handling many engineering problems with constraints, even with equations.     

Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.     

富集均三甲苯塔的模拟与优化

在满足工艺要求的基础上，通过富集均三甲苯塔模拟与优化给出最佳操作条件，用于指导实际操作和设备改造．优化生产参数。     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing

This study addresses classification methodology for the automatic inspection of a range of defects on the surface of glass substrates in thin film transistor liquid crystal display glass substrate manufacturing. The proposed methodology consisted of four stages: (1) feature extraction by calculating the wavelet co-occurrence signature from the substrate images, (2) handling of imbalanced dataset using the Synthetic Minority Over-sampling TEchnique (SMOTE), (3) reduction of the feature's dimension by principal component analysis, and (4) finally choosing the best classifier between three different methods: Classification And Regression Tree (CART), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). In training the SVM and MLP classifiers, the simulated annealing algorithm was used to obtain the optimal tuning parameters for the classifiers. From the industrial case study, the proposed feature extraction algorithm could remove the defect-irrelevant image features and SMOTE increased the accuracy of all three methods. Furthermore, the optimized SVM and MLP models were more accurate than the CART model whereas a higher accuracy of 89.5% was observed for the proposed SVM model.     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.     

A new Probe Guided Mutation operator and its application for solving the cardinality constrained portfolio optimization problem

This paper revisits the classical Polynomial Mutation (PLM) operator and proposes a new probe guided version of the PLM operator designed to be used in conjunction with Multiobjective Evolutionary Algorithms (MOEAs). The proposed Probe Guided Mutation (PGM) operator is validated by using data sets from six different stock markets. The performance of the proposed PGM operator is assessed in comparison with the one of the classical PLM with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGAII) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). The evaluation of the performance is based on three performance metrics, namely Hypervolume, Spread and Epsilon indicator. The experimental results reveal that the proposed PGM operator outperforms with confidence the performance of the classical PLM operator for all performance metrics when applied to the solution of the cardinality constrained portfolio optimization problem (CCPOP). We also calculate the True Efficient Frontier (TEF) of the CCPOP by formulating the CCPOP as a Mixed Integer Quadratic Program (MIQP) and we compare the relevant results with the approximate efficient frontiers that are generated by the proposed PGM operator. The results confirm that the PGM operator generates near optimal solutions that lie very close or in certain cases overlap with the TEF.     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.