Sharif University of TechnologyScientia Iranica1026-3098311820241101Combined type generalized classes of efficient estimators of mean in joint presence of measurement error and non-response using stratified two-phase sampling156715952266910.24200/sci.2022.54750.3906ENAamirSanaullahDepartment of Statistics, COMSATS University Islamabad, Lahore Campus, PakistanShaistaSabirDepartment of Statistics, COMSATS University Islamabad, Lahore Campus, PakistanJournal Article20191101In this study, three classes of generalized and more efficient combined regression-cum-ratio estimators are presented to estimate the population mean of the study variable in stratified two-phase sampling considering non-response and measurement error are present jointly. The expressions for the bias and mean square error of the three proposed generalized combined regression-cum-ratio estimators have been obtained. Optimal conditions which make the proposed generalized regression-cum-ratio estimators more efficient than modified combined regression estimator are discussed. The performance of the proposed generalized combined regression-cum-ratio estimators has been compared theoretically as well as empirically with various combined type estimators in stratified two-phase sampling including usual combined ratio estimator, usual combined exponential ratio estimator, usual combined regression estimator, and modified combined regression estimator. An empirical study shows that the proposed generalized combined regression-cum-ratio estimators perform more efficiently than all combined type ratio, exponential ratio, and regression estimators discussed in the study.https://scientiairanica.sharif.edu/article_22669_0a9e22fab0ff0401e82b70fd32b23795.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101Copula-based modeling for IBNR claim loss reserving159616052257010.24200/sci.2021.54706.3878ENSamiraZaroudiSchool of Mathematical and Statistical Sciences, Southern Illinois University Carbondale, IL 62901, USAMohammad RezaFaridrohaniDepartment of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, IranMohammad HassanBehzadiDepartment of Statistics, Science and Research Islamic Azad University, Tehran, IranHadiSafari-KatesariDepartment of Mathematical Sciences, Stevens Institute of Technology, Hoboken, NJ 07030, USAJournal Article20191127There are growing concerns for reserves estimation of incurred but not reported (IBNR) claims in actuarial<br />sciences. In this paper, we propose a copula-based dependency model to capture the relationship between<br />two main IBNR reserve variables, i.e., the “time between two successive occurrences” and “delay time”.<br />A maximum likelihood estimation (MLE) method is used to estimate the parameters of the model. A<br />simulation study is conducted to evaluate the validity of the theoretical results. Moreover, the proposed<br />method is applied to predict the number of claims for the next years of a portfolio from a major automobile<br />insurer and is compared to the classical Chain Ladder model forecasting.https://scientiairanica.sharif.edu/article_22570_c1c0ae0dfaee3b969d2c2f470183b55d.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101A possibilistic programming approach for biomass supply chain network design under hesitant fuzzy membership function estimation160616242258810.24200/sci.2021.55021.4035ENHosseinGitinavardDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave., Tehran, IranMohsenAkbarpour ShiraziDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave., Tehran, IranMohammad HosseinFazel ZarandiDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave., Tehran, IranJournal Article20191212The recognition of membership function by knowledge acquisition from experts is an important factor for many fuzzy mathematical programming models. Thus, the hesitant fuzzy membership function (HFMF) estimation could help users of the mathematical programming approaches to provide a powerful solution in continuous space problems. Therefore, this study proposes a possibilistic programming approach based on Bezier curve mechanism for estimating the HFMF. In the process of possibilistic programming approach, an optimization model is presented to tune the primary parameters of Bezier curve by the goal of minimizing the sum of the squared errors (SSE) between the empirical data and fitted HFMF. After that, the efficiency and applicability of the proposed approach is checked by proposing a novel mathematical model for biomass supply chain network design problem. In this case, the bio-products demand is declared as an imprecise parameter that follows from the estimated HFMF to increase the accuracy of the obtained results by addressing the uncertainty and unreliability of the information. Finally, a computational experiment and validation procedure about the biomass supply chain network design is provided to peruse the verification and validation of the proposed approaches.https://scientiairanica.sharif.edu/article_22588_17b4431acac862315f6bdeaf5d78a5fb.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101On use of subsampling of the non-respondents for estimation of distribution function162516372255810.24200/sci.2021.55790.4406ENJavidShabbirDepartment of Statistics, Quaid-i-Azam University, Islamabad, 45320, PakistanSatGuptaDepartment of Mathematics and Statistics, The University of North Carolina at Greensboro, NC 27412, USAJournal Article20200412In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents. We use the Hansen and Hurwitz [1] pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, Rao [2] difference estimator, Kumar et al. [3] estimator and many other recent difference type estimators by using the criterion of MSE.https://scientiairanica.sharif.edu/article_22558_531ae820049dd5422a1199a452825312.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101Developing a multiproduct three-level cold supply chain considering quality evaluation function and pricing mechanism163816582262510.24200/sci.2022.55864.4439ENAliBaradaranDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, IranSeyed Hamid RezaPasandidehDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, IranJournal Article20200506Paying attention to cold supply chains is critical in light of rising global warming and public awareness of the issue. In addition, a lack of appropriate quality control in supply chains has resulted in significant waste in the industry. This research sought to create a three-level cold supply chain (firm, distribution center, and retailer) with a quality evaluation function. The chain has been modelled for a multiplicity of products and time periods. The parameters in this model are analyzed in three separate scenarios to reflect uncertainty. The model also includes direct delivery from the firm to the store. Various factors can affect the quality evaluation variables, which in this model are assigned to two main parameters: temperature and humidity. The quality of the products in this model is used to estimate their selling price. Due to the nonlinearity of the model, the Baron approach is applied in this work.https://scientiairanica.sharif.edu/article_22625_4ca7ad1b98bea0519dace63f88f2b2a7.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101Joint determination of purchasing and production lot sizes in an unreliable production system165916732267710.24200/sci.2022.56004.4510ENMehdiDeiranlouDepartment of Industrial Engineering, University of Bojnord, P.O. Box 94531-55111, Bojnord, IranFarzadDehghanianDepartment of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, IranMohammadaliPirayeshDepartment of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, IranJournal Article20200520This paper discusses a production-inventory system under random machine breakdown. Holding safety stock is the common way to mitigate the effect of random machine breakdown on shortages that may occur during machine repair time. Since holding safety stock can be costly, especially for expensive products, this paper investigates an alternative strategy in which it is assumed that the production manager can purchase the same products from a supplier in order to meet the demands that may be lost due to the depletion of the inventory after the machine breakdown. The supplier has known lead-time and reliability with the quality assured products. Despite holding safety stock, purchasing occurs only when the machine breakdown happens. The question is about the optimal amount of production and purchasing lot sizes to minimize the total expected costs. The optimality of the model is investigated when failure and repair time follow an exponential distribution, and a computational algorithm for finding the optimal lot sizes is presented. A comparison between the purchasing strategy and holding safety stock is performed through a sensitivity analysis regarding some effective parameters. This study shows using the purchasing strategy when holding or production cost rises is more beneficial than holding safety stock.https://scientiairanica.sharif.edu/article_22677_e5b3eacb2d1755ceb371d6fa67ff3fb5.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101A dempster-shafer evidence theory for environmental risk assessment in failure modes and effects analysis of oil and gas exploitation plant167416902264310.24200/sci.2022.56162.4580ENGholamrezaShamsFaculty of Engineering, Shahrekord University, P.O. Box 115, Shahrekord, IranSeyed MortezaHatefiFaculty of Engineering, Shahrekord University, P.O. Box 115, Shahrekord, IranShahlaNematiFaculty of Engineering, Shahrekord University, P.O. Box 115, Shahrekord, IranJournal Article20200611The oil, gas, and petrochemical industries, as one of the largest sources of environmental pollutants, have different types and levels of pollution depending on the type of input materials, process steps, and output products. Various stages of exploration, extraction and processing of oil and gas have many environmental effects, such as those on soil, air, water, creatures, plants, and even humans. In this paper, a failure mode and effects analysis (FMEA) is employed to identify failures and environmental risks in an oil and gas exploitation plant. Dempster–Shafer (DS) theory of evidence is then proposed for environmental risk assessment due to its effectiveness in dealing with uncertain and subjective information. The assesments of experts and their confidence levels of their responses are employed to construct the basic probability assignments (BPA) in DS theory of evidence. Furthermore, a new weighting method is proposed to obtain the discounted BPA which reduces the uncertainty in the information sources and improves the quality of information before combining different sources of information. Finally, the proposed method is applied to an oil and gas exploitation plant to assess environmental risks.https://scientiairanica.sharif.edu/article_22643_b3e27f877f903644ebbacd943b8ab4d4.pdfSharif University of TechnologyScientia Iranica1026-3098311820241101Toward sustainability in designing an agricultural supply chain network: A case study on palm date169117092250310.24200/sci.2021.58302.5659ENAbdolrezaHamdi-AslDepartment of Industrial Engineering, Tehran Central Branch, Islamic Azad University, Tehran, IranHosseinAmoozad-KhaliliDepartment of Industrial Engineering, Nowshahr Branch, Islamic Azad University, Nowshahr, Iran0000-0001-7222-2233RezaTavakkoli-MoghaddamSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranMostafaHajiaghaei-KeshteliTecnologico de Monterrey, Escuela de Ingenieraiy Ciencias, Puebla, MexicoJournal Article20210511Nowadays, the agricultural and food supply chains have attracted both academia and industrial practitioners. This paper first considers the characteristics of the date product as one of the most well-known and rich fruits to design and address its supply chain design. Special characteristics in date products have made the design of the supply chain to be unique. Therefore, considering different customers along with the specific product flow is another contribution of this paper. Reportedly, there is no work on this topic. Several old and recent meta-heuristic algorithms are utilized in multi-objective meta-heuristics to reach better intensification and diversification trade-offs. By the Taguchi design experiment method, appropriate parameter values of the proposed algorithms are chosen. Besides, the solution quality is investigated by approaches including the relative percentage deviation (RPD) and the CPU time and the weighted LP-metric method. The results showed that a multi-objective Keshtel algorithm (MOKA) is more efficient and consistently outperforms other utilized algorithms.https://scientiairanica.sharif.edu/article_22503_21251efd7e45613eef4d17d7f2d21b72.pdf