Scientia Iranica
Volume:30 Issue: 3, May-June 2023

Energy demand in Iran is increasing because of many reasons, for instance, low price, population and GDP growth. Its trend shows that this situation could not continue for many years, so it needs reviewing in energy policy in Iran. The aim of this paper is reviewing the whole energy system of Iran and its possible future and offering some policies for solving current and future problems. First of all, this study investigates the trend of growth of some parameters like GDP, electricity price, population, CO2 emission and electricity consumption in eight years. After that, by calculating the price of electricity generated in Iran under three different circumstances, this study is able to investigate the electricity and natural gas trade according to the price of them. Studying the renewable energy effects on CO2 emission reduction, subsidy reduction and water saving lead this study to prepare and policy offer. At the end of this paper, results show there is a lack of idea in energy economic in Iran, and two policies will be offered by considering this aspect.

So far, many ways have been provided to solve multiple criteria decision making problems with interval numbers. Most of these methods rank the alternatives according to two criteria, that is, being close to the positive ideal solution and far away from the negative ideal solution. In this paper, a method is presented for solving multiple criteria decision making problems with interval numbers, such that being close to positive ideal solution and being away from negative ideal solution have the same effect in alternatives ranking. In the proposed method, the first positive ideal solution and negative ideal solution are determined as interval numbers and distance of each alternative from positive ideal solution and negative ideal solution is calculated by extension of Euclidean distance. Then, a compromise index is defined to rank the alternatives. Three numerical examples are given to compare the proposed method with other methods presented in the literature.

The bullwhip effect is one of the most important problems in the supply chain management. It can cause large inefficiency in supply chain. Although there are many researches about bullwhip effect, few studies have investigated this phenomenon caused by product price fluctuation. In this paper we consider a two-period supply chain consisting of one supplier, one wholesaler and one retailer. The wholesale price may increase greatly in the beginning of second period. If this happens, a large number of end customers will go to purchase the product from retailer. For managing the end customers’ demands in the second period, we consider two ordering strategies available to the retailer including optimal order quantity strategy and hedging strategy with call option. For each strategy, we calculate the bullwhip effect ratio for two periods and compare the results. We found that the lower exercise price in hedging strategy compared with the wholesale price in the optimal order quantity strategy must not contribute to extra product purchase. The research provides new insights into how hedging strategy can reduce bullwhip effect.

The quality of products, maintenance operations, and transportation policies are primary concerns of managers in inventory and production planning issues. Besides that, environmental concerns and regulations are growing increasingly and attracted much attention to green production concepts such as sustainable production. Previous authors conducted a wide range of research on these problems separately. Regarding the gap of an integrated framework, we formulate a sustainable Economic Production Quantity model by considering preventive maintenance and multiple shipments of items where a portion of produced items are defective. Two different Cases are studied. In Case I, we assumed that the demand in the production period is satisfied by the produced items in the previous cycle. In another state and in Case II, simultaneous production and consumption during the production period is considered and mathematically formulated. An analytical method is presented for solving the models, and a numerical example is discussed for both Cases. Moreover, sensitivity analysis of the models is carried out, and some managerial insight is derived by changing some of the critical parameters of models. Finally, we present some directions for feature researches.

Modeling non-linear data is a common practice in data science and machine learning (ML). It is aberrant to get a natural process whose outcome varies linearly with the values of input variable(s). Arobust and easy methodology is needed for accurately and quickly fitting a sampled data set witha set of covariates assuming that the sampled data could be a complicated non-linear function. Anovel approach for estimation of finite population parameter τ , a linear combination of the population values is considered, in this article, under superpopulation setting with known basis functionsregression (BFR) models. The problems of subsets selection with single predictor under an automaticmatrix approach, and ill-conditioned regression models are discussed. Prediction error variance ofthe proposed estimator is estimated under widely used feature selection criteria in ML. Finally, theexpected squared prediction error (ESPE) of the proposed estimator and the expectation of estimatederror variance under bootstrapping as well as simulation study with different regularizers are obtainedto observe the long-run behavior of the proposed estimator.

In the presence of outliers in the data set, the utilization of robust regression tools for mean estimationis a widely established practice in survey sampling with single auxiliary variable. Abid et al. (2018),with the aid of some non-conventional location measures and traditional OLS, proposed a class of meanestimators using information on two supplementary variates under a simple random sampling framework. The utilization of non-traditional measures of location, especially in the presence of outliers,performed better than existing conventional estimators. In this study, we have proposed a new class ofestimators of mean utilizing quantile regression. The general forms of MSE and MMSE are also derived.The theoretical findings are being reinforced by different real-life data sets and simulation study.