Scientia Iranica
Volume:28 Issue: 3, May-Jun 2021

In this paper, we aim at developing a model to predict the daily average concentration of particulate matters with a diameter of less than 2.5 micrometers (PM2.5). In the introduced model, we incorporate Weather Research and Forecasting (WRF) meteorological model, Monte Carlo simulation, wavelet transform, and multilayer perceptron (MLP) neural networks. In particular, the MLP and wavelet transformation are combined for prediction. In order to predict the model’s input parameters, including wind speed, wind direction, temperature, rainfall, and temperature inversion, the WRF meteorological model is used. Finally, according to the available uncertainty in the input data and in order to achieve a more accurate prediction, the Monte Carlo simulation is utilized. In order to assess the effectiveness of the model in the real world, it has been conducted in an online mode for 35 days. Numerical results give an acceptable accuracy in terms of some widely used measures. In particular, taking into account the R measurements, it is equal to 0.831 over the set of test instances.

Transmuted distributions are skewed distributions and recently attracted a great attention of researchers due totheir applications in reliability and statistics. In this article, our main focus is on the Bayesian estimation of two-component mixture of Transmuted Weibull Distribution (TWD) under type-I right censored sampling scheme. In order to estimate the unknown parameters, non-informative and informative priors under Squared Error Loss Function (SELF), Precautionary Loss Function (PLF) and Quadratic Loss Function (QLF) are assumed when computing the posterior estimations. In addition the Bayesian credible intervals (BCI) were also constructed. Markov Chain Monte Carlo (MCMC) technique is adopted to generate samples from the posterior distributions and in turn computing different posterior summaries including Bayes estimates(BEs), posterior risks(PRs) and Bayesian credible intervals (BCI). As an illustration comparision of these Bayes estimators are made through simulated under different loss functions in terms of their respective posterior risks assuming different sample sizes and censoring rates. Two real-life examples; the first being the survival times of hepatitis B & C patientswhile the second being the hole diameter of 12 mm and the sheet thickness is 3.15 mm are also discussed to illustrate the potential application of the proposed methodology.

Statistical process control techniques are commonly used to monitor process performance. Control charting technique is the most sophisticated tool of SPC and is categorized as memory-less and memory-type control charts. Shewhart-type control charts have low efficiency in detecting the small changes in the process parameters and named as memory-less control charts, and memory-type control charts (for example cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) charts) are very sensitive to small persistent shifts. In connection with enhancing the performance of CUSUM and EWMA charts, an efficient variant of memory-type charts for the location parameter is developed based on mixing the double exponentially weighted moving average (DEWMA) chart and CUSUM chart by performing exponential smoothing twice. Performance of the proposed efficient variant is compared with existing counterparts under normal and non-normal (heavy tails and skewed) environments. The study also provides an industrial application related to the monitoring of weights of quarters made by mint machine placed into service at U.S. Mint. From theoretical and numerical studies, it is revealed that proposed variant of memory-type charts outperforms the counterparts in detecting shifts of small and moderate magnitude.

The Red Deer Algorithm (RDA) is one of recent metaheuristic algorithms inspired by the behavior of red deers during a breading season. The RDA revealed its performance for a variety of combinatorial optimization problems in different real-world applications. In this paper, the parameters and operators of RDA using some adaptive strategies have been modified to improve the performance of this optimizer. To prove the efficiency of Improved RDA (IRDA), not only some benchmarked functions are utilized but also a Direct Current (DC) brushless motor design as one of real-world engineering design issues. The results of developed IRDA have been compared with its general idea and existing algorithms from the literature. This comparative study confirms that the offered IRDA outperforms the other algorithms and provide very competitive results.

This study deals with a dual channel supply chain where selling price of each player, delivery time for direct channel and retail service dependent demand structures are considered for manufacturer and retailer. In the direct channel, the manufacturer sells the products directly to the customers with a maximum mentioned delivery time. The delivery time of the products is adjustable according to customers’ demand with extra delivery charge. In the retail channel, the customers are extra benefited by the retail service and direct connection with the products. Selling price for direct market is considered as lower than the retail market selling price. The behavior of the model under integrated system is analyzed. In the decentralized structure, vertical Nash and manufacturer Stackelberg models are also discussed. The sensitivity of the key parameters is examined to test feasibility of the model. Finally, a numerical example with graphical illustrations is provided to investigate the proposed model.

Auxiliary information is mostly used together with study variable to enhance efficiency of estimators for population mean, total and variance. Thompson introduced adaptive cluster sampling as an appropriate sampling scheme for rare and clustered populations. In present article, difference-type and difference-cum-exponential-ratio-type estimators are presented utilizing two auxiliary variables for estimation of general parameter under stratified adaptive cluster sampling. Proposed estimators utilize auxiliary information in terms of ranks, variances and means of auxiliary variables in $h^{th}$ stratum. Expressions for bias and mean square error of proposed estimators are derived using first order of approximation. Numerical study is conducted to evaluate the performance of proposed estimators.

Recently, the manufactures of complex product and its subsystems have faced a series of disruptions and troublesome behaviors in supplying goods and items. Likewise, suppliers in this area are more likely to be affected by external risks, in turn eventuating in disturbances. Selecting resilient and expedient suppliers dramatically decreases the delay time and costs and contributes to the competitiveness and development of the companies and organizations in this field. In this regard, this paper aims at proposing a bi-objective robust mathematical model to provide resilience supplier selection and order allocation for complex products and its subsystems in response to uncertainty and disruption risks. In the proposed model, a robust optimization approach is deployed, providing stable decisions for the proposed problem. Also, different resilience strategies including restoring supply from occurred disruptions, fortification of the suppliers, using backup suppliers, and utilizing the extra production capacity for suppliers have been devised to tolerate disruptions. Meanwhile, the augmented ε-constraint method is used, ensuring the optimal strong Pareto solutions and preventing the weak ones for the proposed bi-objective model. The evaluation of the effectiveness and desirability of the developed model is explored by discussing a real case study, via which helpful managerial insights are gained.

Waste products have double properties of environmental hazard and resource recovery, while recycling behavior has greater positive external effect of economic, which often results in the low enthusiasm for enterprises to engage in remanufactured activity. For price decisions on whether government subsidizes closed-loop supply chain or not, Stackelberg game model were constructed under three scenarios: none is subsidized (Model N), subsidize to manufacturer (Model M) and subsidized to recycler (Model R) to obtain the optimal government subsidy and price decision, as well as analyze the difference among the equilibriums of four scenarios. From the conclusion, we can find that the government subsidy improve the social welfare, as well as the government implement different subsidy policies based on the needs for economic and social progress.

Information Technology (IT), Management and Industrial Engineering are correlated academic disciplines which their publications rose significantly over the last decades. The aim of this study is analyzing the research evolution, determining the important topics and areas and depiction the trend of interdisciplinary topics in these domains. To accomplish this, the text mining techniques are used and the combination of bibliographic analysis and topic modeling approach are applied on their publications in the WOS repository over the last 20 years. In the topic extraction process, a heuristic function was suggested to key extraction, and some new applicable criteria were defined to compare the topics. Moreover, a novel approach was proposed to determine the high-level category for each topic. The results determined the hot-important topics and incremented, decremented and fixed topics are identified. Subsequently, comparing the high-level research areas confirmed the strong scientific relationships between them. This study presents a deep knowledge about internal research evolution of domains and illustrates the effect of topics on each other over the past 20 years. Furthermore, the methodology of this study could be applied to determine the interdisciplinary topics and observe the research evolution of other academic domains.

This paper deals with a flexible flow shop (FFS) scheduling problem with unrelated parallel machines and renewable resource shared among the stages. The FFS scheduling problem is one of the most common manufacturing environment in which there is more than a machine in at least one production stage. In such a system, to decrease the processing times, additional renewable resources are assigned to the jobs or machines, and it can lead to decrease the total completion time. For this purpose, a mixed integer linear programming (MILP) model is proposed to minimize the maximum completion time (makespan) in an FFS environment. The proposed model is computationally intractable. Therefore, a particle swarm optimization (PSO) algorithm as well as a hybrid PSO and simulated annealing (SA) algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large size test problems.