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Öğe Answers to Comments on An integrated methodology for green human resource management in construction industry by Masoud Alimardi (https://doi.org/10.1007/s11356-023-26217-9)(Springer Heidelberg, 2023) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Aeini, Samira; Vandchali, Hadi Rezaei; Tirkolaee, Erfan Babaee[Abstract Not Available]Öğe Application of machine learning in supply chain management: a comprehensive overview of the main areas(HINDAWI LTD, 2021) Tirkolaee, Erfan Babaee; Sadeghi, Saeid; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Aeini, SamiraIn today's complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.Öğe An integrated methodology for green human resource management in construction industry(Springer, 2022) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Aeini, Samira; Vandchali, Hadi Rezaei; Tirkolaee, Erfan BabaeeToday, by increasing public awareness about environmental issues and pressures from governments and other stakeholders, companies have dealt with environmental challenges more than ever. This paper focuses on environmentally sustainable performance using an integrated methodology based on meta-synthesis, Delphi, and structural equation modeling (SEM) techniques which are utilized in diferent phases. In the frst phase, an in-depth review of green human resources management (GHRM) literature is conducted based on the meta-synthesis method, and as a result, 38 codes are extracted. Next, to adapt and customize the codes with the nature of the construction industry, 2 rounds of Delphi method are implemented to extract the expert judgment from a panel of 15 industry professionals, resulting in 21 codes in 7 categories. To validate the developed methodology, a dataset from 33 Iranian construction companies are collected along with 15 factors in 5 categories determined using SEM. The fndings reveal that among 9 main GHRM components extracted from the literature, just 5 components including green recruitment and selection, green performance management, green-reward, green-based employee empowerment, and green training have signifcant and positive relationships with GHRM. Finally, managerial insights, limitations, and future research directions are discussed.Öğe An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains(Springer, 2022) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Tomaskova, Hana; Tirkolaee, Erfan BabaeeGlobal supply chains are increasingly becoming complex by having numerous suppliers around the world. To manage this complexity, organizations must identify the optimum number of suppliers. There have been many examples in the literature that used diferent approaches to solve this problem. Despite the importance of this issue, less attention has been paid to it and managers of the companies do not know how, and based on which approach and criteria, they should determine the optimal number of suppliers which leads to lower cost and higher reliability of the production line. Therefore, in this study, a hybrid methodology is proposed to expose the process of this problem which helps managers to learn how they can determine the optimal number of suppliers. We address this gap by developing an integrated approach based on multi-criteria decision-making (MCDM) comprising best-worst method (BWM), simple additive weighting (SAW), and technique for order preference by similarity to ideal solution (TOPSIS), and simulation to determine the optimal number of suppliers. This study utilizes a comprehensive approach based on leagile and environmentally sustainable criteria to determine the optimal number of suppliers. To examine the efciency of the proposed approach, an empirical case study is conducted in an Iranian oil company. The fnal results represent that the scenario with a 1-1-1 arrangement (one supplier for each type of equipment) is the best possible scenario to determine the optimal number of leagile-sustainable suppliers. To examine the reliability and robustness of the obtained results, a sensitivity analysis based on the three most important criteria is conducted. Finally, discussions on the fndings as well as theoretical and managerial implications are presented.Öğe An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains (Jun, 10.1007/s11356-022-20214-0, 2022)(SPRINGER HEIDELBERG, 2022) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Tomaskova, Hana; Tirkolaee, Erfan BabaeeNo Abstract Available.
