Article
Operations Research & Management Science
Hamid Kiaei, Reza Farzipoor Saen, Reza Kazemi Matin
Summary: In this study, Network Data Envelopment Analysis models are used to identify more sources of inefficiency by considering the internal structure of production units. The aim is to assess and improve the performance of Decision Making Units using a two-stage network and a cross-efficiency approach. The main contributions include a new benevolent method in cross-efficiency evaluation and a method for setting inputs and outputs target to improve cross-evaluations.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Mohammad Izadikhah, Elnaz Azadi, Majid Azadi, Reza Farzipoor Saen, Mehdi Toloo
Summary: This paper introduces a practical technique, network data envelopment analysis (NDEA), for assessing the performance of sustainable supply chain management (SSCM). A new NDEA model is proposed to evaluate the efficiency of SSCM under uncertain conditions. The efficacy of the proposed method is demonstrated through a case study in the soft drinks industry.
ANNALS OF OPERATIONS RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Pejman Peykani, Farhad Hosseinzadeh Lotfi, Seyed Jafar Sadjadi, Ali Ebrahimnejad, Emran Mohammadi
Summary: This study provides a comprehensive and structured literature review of fuzzy chance-constrained data envelopment analysis (FCCDEA) studies from 2000 to 2020, with main contributions including a review of fuzzy chance-constrained programming, survey of FCCDEA models based on different fuzzy measures, analysis of FCCDEA applications and features, classification of FCCDEA studies, bibliometric analysis, and identification of research gaps and future research directions.
FUZZY OPTIMIZATION AND DECISION MAKING
(2022)
Article
Business
Majid Azadi, Saeed Yousefi, Reza Farzipoor Saen, Hadi Shabanpour, Fauzia Jabeen
Summary: The study proposes a network data envelopment analysis (NDEA) model and a deep learning approach for predicting the sustainability of healthcare supply chains (HSCs). Technological advances such as deep learning, artificial intelligence (AI), and Blockchain have gained importance in HSCs and are seen as competitive advantages. The use of advanced performance evaluation techniques, including DEA, has garnered considerable attention in enhancing HSCs' performance. The results highlight the top-ranking HSCs that utilize fewer facilities, have desirable outputs, and minimal undesirable outputs.
JOURNAL OF BUSINESS RESEARCH
(2023)
Article
Operations Research & Management Science
Majid Azadi, T. C. E. Cheng, Reza Kazemi Matin, Reza Farzipoor Saen
Summary: This research develops a network data envelopment analysis model to assess the resilience and sustainability of healthcare supply chains (SCs) in response to the COVID-19 pandemic outbreak. The proposed model can handle different types of data and introduces a modified directional distance function for efficiency measurement. The performance of 28 healthcare SCs is evaluated to demonstrate the applicability and capability of the approach.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Maryam Shadab, Saber Saati, Reza Farzipoor Saen, Mohammad Khoveyni, Amin Mostafaee
Summary: Research focuses on efficiency and performance issues in sustainable supply chains, identifying congestion of intermediate products as a key factor in reducing efficiency. The study explores the use of Data Envelopment Analysis (DEA) to detect congestion and proposes Network DEA (NDEA) models for analysis.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Mohammad Khoveyni, Robabeh Eslami
Summary: This study proposes an input-output-oriented linear model in network data envelopment analysis (DEA) to measure the overall efficiency of two-stage decision-making units (DMUs) with shared resources. Comparative analysis shows the superiority of our model over existing models, and an empirical example demonstrates the applicability of our proposed model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Operations Research & Management Science
Mohammad Sajjad Shahbazifar, Reza Kazemi Matin, Mohsen Khounsiavash, Fereshteh Koushki
Summary: Data envelopment analysis (DEA) is a useful mathematical tool for evaluating and ranking the performance of production units. This paper introduces a new method for evaluating group efficiency of two-stage production systems, with new DEA models and numerical examples provided, including an application in the banking industry.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jing Zhou, Yu Liu
Summary: Failure modes and effects analysis (FMEA) is widely used for risk analysis of product, system, and service failure modes. This article addresses the limitations of traditional FMEA methods by proposing a possibilistic chance-constrained DEA framework that considers uncertainty and correlation in risk factors. The framework is demonstrated to be effective through a case study.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohammad Khoveyni, Robabeh Eslami
Summary: This study proposes a new approach to finding the efficiency stability regions of efficient multi-stage production processes using network data envelopment analysis (NDEA). By defining the concepts of 'network-efficient', 'extreme network-efficient', and 'ESR' in NDEA, a linear DEA model is introduced to specify extreme network-efficient two-stage production processes and a DEA approach is proposed to find their ESRs. The research also discusses the managerial and economic implications of finding these ESRs and provides numerical examples and an empirical application to illustrate the proposed approach.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Biotechnology & Applied Microbiology
H. Arman, A. Jamshidi, A. Hadi-Vencheh
Summary: This study introduces a new methodology for examining eco-innovation and demonstrates its superiority. Empirical analysis reveals that the Czech Republic ranks highest in eco-innovation.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2021)
Article
Operations Research & Management Science
Mohammad Izadikhah, Reza Farzipoor Saen
Summary: This study presents a new stochastic two-stage DEA model for assessing the sustainability of supply chains. Unlike conventional models, this model considers intermediate products. The efficacy of the model is demonstrated through a case study.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Reza Farzipoor Saen, Balal Karimi, Amirali Fathi
Summary: This paper discusses the importance of sustainable supply chain management in the transport industry and proposes a Malmquist productivity index based on network data envelopment analysis model to evaluate the sustainability of intercity passenger transportation.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Economics
Hashem Omrani, Ali Emrouznejad, Meisam Shamsi, Pegah Fahimi
Summary: This paper extends the two-stage NDEA model with negative data and undesirable outputs. An alternative linear model based on goal programming technique is proposed to avoid complex non-linear calculations. A method is used to transform negative data into positive and undesirable outputs into desirable ones.
SOCIO-ECONOMIC PLANNING SCIENCES
(2022)
Review
Management
Camila Guimaraes Monteiro de Freitas Alves, Lidia Angulo Meza
Summary: This paper presents a literature review on network models in data envelopment analysis (DEA) with a focus on the slack-based network model called slacks-based measure-network data envelopment analysis (SBM-NDEA). A total of 97 publications from 2009 to 2021 were identified in an international database search. The publications were subjected to descriptive analysis, mapping the evolution of the literature, organizing articles into categories, and identifying the main methodological references for the models and applications used. The most frequently used models and their characteristics, as well as the analysis of applications, were described. Sectors and countries with the highest number of applications were also identified. The review indicates an increasing number of network studies using the SBM-NDEA in recent years. Finally, future research directions in SBM-NDEA are suggested.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Operations Research & Management Science
Zahra Homayouni, Mir Saman Pishvaee, Hamed Jahani, Dmitry Ivanov
Summary: This article investigates sustainability strategies for carbon regulation mechanisms and proposes a multi-objective programming model to support decision-making in supply chains. The study finds that governmental incentives for carbon cap-and-trade policies can effectively reduce pollution in supply chains.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Sajjad Rahmanzadeh, Mir Saman Pishvaee, Kannan Govindan
Summary: This paper introduces the concept of Open Supply Chain Management (OSCM), which aims to improve efficiency and flexibility in the main processes of the supply chain through resource integration. To validate OSCM, the integration of the design process with supply chain production planning is investigated, with a case study in the clothing manufacturing industry. The results show that the design cost is a small proportion of the total supply chain cost.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Hamid Kiaei, Reza Farzipoor Saen, Reza Kazemi Matin
Summary: In this study, Network Data Envelopment Analysis models are used to identify more sources of inefficiency by considering the internal structure of production units. The aim is to assess and improve the performance of Decision Making Units using a two-stage network and a cross-efficiency approach. The main contributions include a new benevolent method in cross-efficiency evaluation and a method for setting inputs and outputs target to improve cross-evaluations.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Mohammad Tavassoli, Reza Farzipoor Saen
Summary: This study proposes a novel non-radial fuzzy network data envelopment analysis (FNDEA) model for assessing the sustainability of Iranian Combined Cycle Power Plants (ICCPPs) in a fuzzy context. The proposed model can simultaneously evaluate the sustainability, operational performance, and environmental performance of ICCPPs. The results reveal that 46% of ICCPPs have a relatively good performance in terms of operational and environmental performance. Furthermore, the correlation coefficient among different types of efficiency scores highlights the significant impact of environmental efficiency on the overall efficiency of ICCPPs. Finally, suggestions to improve the sustainability of ICCPPs are provided.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Saeed Yousefi, Hadi Shabanpour, Kian Ghods, Reza Farzipoor Saen
Summary: The Covid-19 virus poses a global health threat, leading governments to take measures to control its spread. This study aims to enhance the efficiency of Covid-19 treatment centers by using Artificial Neural Network (ANN) to forecast their efficiency and provide benchmarks. The findings suggest that allocating more respiratory equipment, pulmonologists, and beds to treatment centers can reduce mortality rates and increase recovery rates.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Zahra Alidoosti, Ahmad Sadegheih, Kannan Govindan, Mir Saman Pishvaee
Summary: In a circular economy, municipal solid waste will be used as a resource, requiring the design of a product-oriented waste management network. The production of diverse bioenergies is crucial, making it necessary to study the waste management network. A sustainable waste management network was designed to extract various bioenergies, considering economic, environmental, and social sustainability under uncertain conditions. The proposed model used a multi-objective possibilistic mixed-integer non-linear programming approach and interactive fuzzy programming methods to handle uncertainty.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2023)
Article
Computer Science, Artificial Intelligence
Sajjad Rahmanzadeh, Mir Saman Pishvaee, Mohammad Reza Rasouli
Summary: This paper explores the impact of the open innovation concept on product design and supply chain master planning. The study considers complex uncertainties in co-design processes, financial challenges, and integration of innovative designs with supply chain planning. A robust fuzzy-stochastic optimization model is proposed to integrate product design and supply chain activities, accounting for different types of uncertainties. The model's contributions include integrating financial and physical flows, using a novel pricing mechanism, and considering external innovations. Results show the superiority of the model in supporting managerial decisions in mid-term planning.
Article
Engineering, Civil
Ali Katebi, Mir Saman Pishvaee, Ali Mohebalizadeh, Ashkan Pazhuhandeh, Bahareh Katebi
Summary: This study proposes a robust optimization approach to modify the programming model of earthwork allocations in road construction. The model considers uncertainties in borrow pit and disposal site capacities and earthwork costs, aiming to minimize project costs while mitigating problem-based constraints. The results show that the robust model ensures feasible solutions with minimized constraint violations and deviations from optimality when there are changes in the input within a given range.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING
(2023)
Article
Energy & Fuels
Abolfazl Rasekh, Farhad Hamidzadeh, Hadi Sahebi, Mir Saman Pishvaee
Summary: Nowadays, the world is facing a major challenge due to population growth, rising global energy demand, increasing water consumption, carbon emissions, and excessive use of fossil fuels. Biomass is considered as one of the most attractive energy sources with positive effects on the economy, environment, and society. Therefore, the design of biomass supply chain networks has gained more attention. Additionally, studying the water-energy-carbon (WEC) nexus is essential for society's sustainable development.
ENERGY SCIENCE & ENGINEERING
(2023)
Article
Operations Research & Management Science
Majid Azadi, Mehdi Toloo, Fahimeh Ramezani, Reza Farzipoor Saen, Farookh Khadeer Hussain, Hajar Farnoudkia
Summary: The rapid growth of advanced technologies such as cloud computing in the Industry 4.0 era has brought numerous advantages. Evaluating the efficiency of cloud service providers (CSPs) is a challenge for customers. This study designs a decision support system that accurately evaluates the efficiency of multiple CSPs in a supply chain, taking into account undesirable outputs, integer-valued, and stochastic data.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Amir Arabsheybani, Alireza Arshadi Khamseh, Mir Saman Pishvaee
Summary: Traditional supply chain designers have not taken environmental factors into account during the design process. However, environmental protection is an important criteria that needs to be evaluated according to global regulations. Perishable items in the food supply chain have limited shelf life and contribute to significant waste, affecting both profit and environmental criteria. Two main solutions to decrease waste are improving packaging and optimizing planning. This study proposes a mathematical model that incorporates efficient biodegradable nano-silver packaging and multi-objective optimization to maximize profit and minimize carbon emissions. The results show that reducing carbon emissions is possible without increasing costs, and using nano-silver packaging positively impacts profit and environmental sustainability.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Energy & Fuels
Shayan Mohseni, Mir Saman Pishvaee, Reza Dashti
Summary: This paper proposes a data-driven two-level transactive energy management framework to address the main issues of trading mechanism design, uncertainty treatment, and privacy protection in networked microgrids. The framework involves determining optimal strategies for internal scheduling and external trading, as well as fair allocation of trading benefits. The uncertainties of renewable energy sources are captured using a robust optimization approach with a robust kernel density estimation (RKDE) technique. The proposed model is solved in a distributed manner using the alternating direction method of multipliers (ADMM) and the augmented Lagrange-based alternating direction inexact Newton (ALADIN) algorithms.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Operations Research & Management Science
Majid Azadi, T. C. E. Cheng, Reza Kazemi Matin, Reza Farzipoor Saen
Summary: This research develops a network data envelopment analysis model to assess the resilience and sustainability of healthcare supply chains (SCs) in response to the COVID-19 pandemic outbreak. The proposed model can handle different types of data and introduces a modified directional distance function for efficiency measurement. The performance of 28 healthcare SCs is evaluated to demonstrate the applicability and capability of the approach.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Elnaz Azadi, Zohreh Moghaddas, Reza Farzipoor Saen, Abbas Mardani, Majid Azadi
Summary: Supply chains are complex systems that involve interactions, components, and flows. The rise of green supply chains has prompted policymakers and managers to consider environmental factors alongside the economic aspect. However, current models struggle to evaluate the performance of green supply chains in an integrated manner. To address this issue, this paper proposes a network data envelopment analysis (NDEA) method as an effective approach for evaluating green supply chains.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Green & Sustainable Science & Technology
Majid Azadi, Hossein Azizi, Reza Farzipoor Saen
Summary: Coordination in providing sustainable and effective transport services is beneficial for both the public and private sectors. However, research on public-private coordination is rare and performance measurement of public-private transport is less explored. This paper contributes to the transportation field by developing a novel network data envelopment analysis (NDEA) model to measure the performance and coordination of public-private transport within multimodal transport networks. The model is based on the directional distance function (DDF) and provides insights into the sustainability and resource allocation among decision-making units (DMUs) in public-private transport networks.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Green & Sustainable Science & Technology
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION
(2024)
