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    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]
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    Application of industry 4.0 in the procurement processes of supply chains: a systematic literature review
    (MDPI AG, 2021) Jahani, Niloofar; Sepehri, Arash; Vandchali, Hadi Rezaei; Babaee Tirkolaee, Erfan
    Abstract Author keywords Abstract The fourth industrial revolution has significantly changed the traditional way of managing supply chains. The applications of Industry 4.0 (I4.0) technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) in different processes of supply chains have assisted companies to improve their performance. Procurement can be considered a critical process in supply chain management since it can provide novel opportunities for supply chains to improve their efficiency and effectiveness. However, I4.0 applications can be costly and may not be reasonably affordable. Therefore, the benefits of implementing these technologies should be clarified for procurement managers before investing in the digitalization of the procurement process. Despite the importance of this issue, few papers have attempted to address the effects of I4.0 technologies and smart systems in procurement. To fill this gap, a Systematic Literature Review (SLR) on the applications of I4.0 technologies in procurement has been used in this study. By reviewing 70 papers through appropriate keywords, a conceptual framework is developed to classify different value propositions provided by the different applications of I4.0 technologies in procurement processes. Results reveal nine value propositions that can provide a better understanding for the procurement department to analyze the benefits of implementing the related I4.0 technologies in different activities. Finally, findings and future study opportunities are concluded.
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    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, Samira
    In 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.
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    Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: a case study in an automotive company
    (SPRINGER, 2022) Ghadir, Amir Hossein; Vandchali, Hadi Rezaei; Fallah, Masoud; Tirkolaee, Erfan Babaee
    Supply chains have been facing many disruptions due to natural and man-made disasters. Recently, the global pandemic caused by COVID-19 outbreak, has severely hit trade and investment worldwide. Companies around the world faced significant disruption in their supply chains. This study aims to explore the impacts of COVID-19 outbreak on supply chain risks (SCRs). Based on a comprehensive literature review on supply chain risk management, 70 risks are identified and listed in 7 categories including demand, supply, logistics, political, manufacturing, financial and information. Then, a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness. The results demonstrate the efficiency of the proposed method, and according to the main findings, "insufficient information about demand quantities", "shortages on supply markets", "bullwhip effect", "loss of key suppliers", "transportation breakdowns", "suppliers", "on-time delivery", "government restrictions", "suppliers' temporary closure", "market demand change" and "single supply sourcing" are the top 10 SCRs during the COVID-19 outbreak, respectively. Finally, the practical implications are discussed and useful managerial insights are recommended.
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    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 Babaee
    Today, 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.
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    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 Babaee
    Global 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.
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    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 Babaee
    No Abstract Available.
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    Multi-objective optimization for healthcare waste management network design with sustainability perspective
    (MDPI AG, 2021) Torkayesh, Ali Ebadi; Vandchali, Hadi Rezaei; Tirkolaee, Erfan Babaee
    Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then com-pared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.