In this paper a multi-objective mathematical model is proposed for multi-item EOQ model considering partial backordering and defective supply batches. In order to consider real world situations, different stochastic o...
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In this paper a multi-objective mathematical model is proposed for multi-item EOQ model considering partial backordering and defective supply batches. In order to consider real world situations, different stochastic operational constraints are considered and implemented under uncertainty. For the first time in this research, the rate of partial backordering is considered as decision variable. The aim is to determine time interval between successive supply deliveries, rate of partial backordering and filling rate from stock in order to minimize total inventory costs including holding, backordering and ordering costs, while, minimizing required warehouse space. Due to complexity and nonlinearity of the proposed mathematical model from one hand and importance of providing the decision maker with efficient Pareto optimal solutions, five meta heuristic algorithms as well as one hybrid exact solution method are utilized to solve the problem. To determine the most efficient solution method, the performance of the algorithms is investigated through a deep computational analysis considering different measures including diversity, number of Pareto optimal solutions, spacing and computation time. In addition, single factor ANOVA is utilized to determine significant difference among algorithms at ninety-five percent confidence level to demonstrate the superior algorithm.
Accurate hourly PM2.5 concentration prediction plays a key role in air quality monitoring and controlling system, especially when severe haze occurs frequently. A PM2.5 hourly prediction system is developed in this pa...
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Accurate hourly PM2.5 concentration prediction plays a key role in air quality monitoring and controlling system, especially when severe haze occurs frequently. A PM2.5 hourly prediction system is developed in this paper, based on an advanced data processing strategy, an effective feature selection technology and a novel optimization algorithm. First, the collected original sequence is decomposed into a group of filters with different wave frequencies and each filter is weighted and reconstructed to mitigate the negative impact of noisy fluctuations. Then mRMR is introduced for extracting interaction information between pollutants, further determining the input of artificial intelligence models. Whereafter, a five-component combined system is taken shape, in which BPNN, ELM, GRNN and BiLSTM are employed as foundation models while multi-objective water cycle algorithm (MOWCA) is the weight optimization model. The results of hourly PM2.5 concentration prediction simulation experiment in the Bei-Shang-Guang-Shen area make clear that the developed system with minimum forecasting error, excellent generalization capability and robust prediction performance shows a definite latent capacity and future to deal with early warning problems and to design suitable abatement strategies. (C) 2021 Elsevier B.V. All rights reserved.
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