In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are d...
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In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are deterministic and production setup times are sequence-dependent. The common cycle time and integer multipliers policies are adapted as replenishment policies for synchronization throughout the supply chain. A new mixed integer nonlinear programming model is developed for both policies, the objective of which is the minimization of inventory, transportation, and production setup costs. We propose a new hybrid algorithm including a modified imperialist competitive algorithm which is purposed to the assimilation policy of imperialist competitive algorithm and teaching learning-based optimization which is added to improve local search. A hybrid modified imperialist competitive algorithm and teaching learning-based optimization is applied to find a near-optimum solution of mixed integer nonlinear programming in large-sized problems. The results denoted that our proposed algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium-and large-sized problems as compared to imperialist competitive algorithm, modified imperialist competitive algorithm, and other methods reported in the literature.
The authors' aim was to present new models based on artificial neural network (ANN) and two optimization algorithms including cuckoo optimization algorithm (COA) and teachinglearningbasedoptimization (TLBO) to ...
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The authors' aim was to present new models based on artificial neural network (ANN) and two optimization algorithms including cuckoo optimization algorithm (COA) and teachinglearningbasedoptimization (TLBO) to predict the pure and impure CO2 MMP. Thirty-four and 11 training and testing data sets were used to develop these models with following inputs: reservoir temperature, the mole percent of volatile oil components (C-1 and N-2), mole percent of intermediate oil components (C-2-C-4, CO2, and H2S), molecular weight of C5+ fraction in oil phase (MWC5+) and mole percentage of CO2, N-2, C-1, C-4, and H2S in the injected gas. Statistical comparisons show that although two models yield acceptable results, the ANN-TLBO model has better performance with the lower mean absolute percentage error (2.6%) and standard deviation (3.37%) and the higher coefficient of determination (0.993). Moreover, among the available correlations, the Cronquist's (1978;corrected by Sebastian etal., 1985) correlations have better performance. Finally, the sensitivity analysis on the ANN-TLBO showed that MWC5+ and reservoir temperature are the most influential parameters in determining the CO2 MMP, respectively.
In this paper, a population-based robust enhanced teaching learning-based optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital infinite-impulse resp...
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In this paper, a population-based robust enhanced teaching learning-based optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital infinite-impulse response (IIR) filters in a multiobjective framework. Furthermore, a decision-making methodology based on fuzzy set theory is applied to handle nonlinear and multimodal design problems of the IIR digital filter. The original teaching learning-based optimization (TLBO) algorithm has been remodeled by merging the concepts of opposition-basedlearning and migration for the selection of good candidates and to maintain diversity, respectively. A multiobjective IIR digital filter design problem takes into consideration magnitude and phase response of the filter simultaneously, while satisfying stability constraints on the coefficients of the filter. The order of the filter is controlled by a control gene whose value is also along with filter coefficients, to obtain the optimum order of the designed IIR filter. Results illustrate that ETLBO is more capable and efficient in comparison to other optimization methods for the design of all types of filter, i.e. high-pass, low-pass, band-stop, and band-pass IIR digital filters.
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