A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data

Gholizadeh, Hadi; Fazlollahtabar, Hamed; Khalilzadeh, Mohammad

doi:10.1016/j.jclepro.2020.120640

Cited by 98 publications

(56 citation statements)

References 61 publications

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“…For the LSTM expansion function, for SNNs to work, they need two things: a custom weight initialization method and the activation function, a scaled exponential linear unit (SELU). It is used on the first layer [33][34][35][36][37].…”

Section: Methodsmentioning

confidence: 99%

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Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

Bhuiyan

Mikhaylov

et al. 2021

Sustainability

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The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

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Section: Methodsmentioning

confidence: 99%

“…However, most studies can examine only a few influential factors because the more variables that they interact with, the more participants and questionnaires are required in these studies [36][37][38][39].…”

Section: Methodsmentioning

confidence: 99%

Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

Bhuiyan

Mikhaylov

et al. 2021

Sustainability

View full text Add to dashboard Cite

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“…We also insist on considering uncertainty features in all main parameters of a GSC or SSC model that is ignored in several relevant models (Chibeles-Martins et al 2016;John et al 2017;Reddy et al 2019;Franco and Alfonso-Lizarazo 2020). Finally, researchers have had less attention to compare different carbon policies and they usually focused on carbon cap-and-trade policy (see Kaur and Singh (2018) and Gholizadeh et al (2020)).…”

Section: Contributions Of the Studymentioning

confidence: 99%

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

et al. 2021

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Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

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“…e application of these information technologies produces large-scale logistics data for various scenarios and operations. e significance of big data technology lies in mining the hidden values of the big data, rather than mastering huge amounts of information [13,21].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Support System for Complicated Logistics Location Integrating Big Data

Cheng

Pan

2021

Advances in Civil Engineering

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Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two-stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set-covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self-organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

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A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data

Cited by 98 publications

References 61 publications

Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

Design of a Support System for Complicated Logistics Location Integrating Big Data

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