Awsan Mohammed

Scopus Publications

Machine learning models integrated with strategic theories for predicting delivery time in supply chains
Awsan Mohammed, Musab AlRehaili, Ahmad Al-Hanbali, Ahmed M Attia, Mohamed Noomane Darghouth, Ahmed Ghaithan, Mohammad Abdel-Aal, Omar Alsawafy
Results in Engineering, 2026
• Identify factors influencing delivery time of materials in supply chain • Purpose machine learning algorithms to estimate the delivery time of goods • Evaluate the performance of the proposed models based on a real case study • Offer predictive models to reduce transportation, inventory, and shortage costs The delivery time of goods in the oil and gas supply chain is a critical enabler for achieving overall supply chain excellence and business success. However, the uncertainty of the delivery times of goods is a significant issue, making it critical for businesses to develop effective logistics and shipping strategies. Consequently, this paper proposes machine learning models to predict delivery times of the items in oil and gas supply chain and enhance its responsiveness. The proposed approach integrates predictive analytics with the Resource-Based View (RBV) and Dynamic Capabilities View (DCV) to align operational forecasting with strategic objectives. Key factors influencing delivery time are identified through a combination of literature review and expert input. Several machine learning models are trained and tested using real-world oil and gas supply chain data. The results indicate that transportation mode, item complexity, and supplier location are the most influential predictors of delivery time. Among the evaluated models, the ensemble approach demonstrates the best performance, achieving prediction accuracy exceeding 85% and exhibiting strong generalization capability. The proposed models equip supply chain managers with actionable decision-support tools to enhance scheduling, reduce uncertainty, and improve inventory planning and logistics coordination for better disruption response. From a strategic perspective, integrating machine learning with RBV and DCV strengthens organizational responsiveness and supports sustained competitive advantage in dynamic supply chain environments.
Predicting schedule delays of construction projects in the oil and gas industry: comparative study
Awsan Mohammed, Anas Bahatheq, Ahmed Ghaithan, Adel Alshibani, Khwaja Mateen Mazher, Ahmed Alrashidi
Built Environment Project and Asset Management, 2026
Purpose Delays in oil and gas construction projects present major challenges due to the sector's complex operations, high capital investment and strict timelines. Such disruptions affect project execution and can have serious socioeconomic impacts. The purpose of this paper is to identify key factors contributing to schedule delays and develop predictive models using artificial neural networks (ANN), decision trees (DTs) and multiple linear regression (MLR) to estimate delay percentages. Design/methodology/approach This study adopts a mixed-methods approach, combining qualitative and quantitative techniques. Expert interviews and a literature review identified and prioritized key delay factors. Data from completed oil and gas construction projects selected through purposive sampling to reflect varied sizes, complexities and locations were analyzed using correlation and outlier tests to ensure reliability. Predictive models: ANNs, DTs and MLR, were then developed, trained and validated with real project data. Findings Design consulting experience, project location and scope changes are assessed as the leading factors influencing delay percentages in oil and gas construction projects. The MLR demonstrated the highest accuracy, exceeding 96%, outperforming both ANNs and DTs. Originality/value This paper focused on the quantitative models rather than the qualitative aspects of predicting schedule delays in construction projects in the oil and gas industry. The proposed models support planning stages by enabling more realistic project schedules and decision-making processes. The proposed models also empower project stakeholders to optimize project outcomes.
Risk Analysis Framework for Airport Infrastructure Systems: A Novel Integrated Approach
Ahmed M. Ghaithan, Faisal Al-Omran, Awsan Mohammed, Khwaja Mateen Mazher
Journal of Infrastructure Systems, 2026
Airports are complex infrastructure systems characterized by diverse equipment, activities, and departments that must seamlessly integrate to fulfill their roles with minimal faults. Enhancing the reliability of airport systems is vital to maintain their operational efficiency. The purpose of this study is to develop a risk analysis framework based on integrated fuzzy-failure mode and effects analysis for assessing risks of airport systems. The potential failure modes associated with each system and failure causes are identified based on historical incidents and interviewing experts. The fuzzy approach is used to prioritize failure modes based on severity, occurrence, and detectability. The risk priority numbers of integrated fuzzy-failure mode and effects analysis (fuzzy-FMEA) are benchmarked with the traditional failure mode and effects analysis to highlight the benefits of considering uncertainties using fuzzy approach. The findings showed that the most critical failure modes are jet fuel tank rupture, runway approach light failure, weather station readings transmission failure, flickering approach lights, and instrument landing system glideslope deviation. The study indicates the appropriateness of the integrated fuzzy-FMEA approach for assessing risks of airport systems. It highlights that proactive maintenance practices, robust infrastructure, and continuous monitoring are essential and effective practices for ensuring the safety and reliability of airport operations.
From blocks to benchmarks: How research in blockchain and operations management aligns with policy making and SDGs
Nokhaiz Tariq Khan, Hira Tahir, Awsan Mohammed, Ahmed Ghaithan, Muhammad Tayyab, Sami El-Ferik
Results in Engineering, 2026
This study maps how blockchain research in operations management (OM) diffuses into policy, quantifies the association between academic visibility and policy uptake, and identifies the core themes of blockchain-OM research that influence policy development. We combine bibliometric mapping of Scopus records with topic modeling (NMF + TF-IDF) of policy-cited papers and policy-topic extraction from Overton. The top five topics are aligned with the subject areas of policy documents and the Sustainable Development Goals (SDGs). The results reveal that policy-cited papers accrue, on average, more than 60 additional Scopus citations compared to non-cited ones. Open-access articles are both more numerous and more cited, while review papers attract disproportionately high citation counts. Academic and policy impact exhibit a non-linear relationship, indicating that high academic visibility does not necessarily translate into policy relevance. Policy themes cluster around five domains: technology, economy, and sustainability; supply-chain risk; agrifood traceability with health and trade linkages; circular economy and decarbonization; and crisis management and pandemic resilience. By integrating bibliometrics, policy-topic mapping, and effect-size inference, this study uncovers where blockchain-OM scholarship intersects with policy agendas and highlights persisting translation gaps between academic prominence and policy uptake. Practically, to enhance policy reach, researchers should prioritize open access, pair studies with actionable policy briefs or tools, engage regulators, and target sectoral pilots such as traceability, circular credit systems, and emergency logistics.
Barriers to learning from incidents in the construction industry of Saudi Arabia
Waleed Imtiaz Usman, Khwaja Mateen Mazher, Firas M. Tuffaha, Ahmed M. Ghaithan, Mubashir Aziz, Hafiz Zahoor, Abubakar Sharafat, Awsan Mohammed
Ksce Journal of Civil Engineering, 2026
Despite advances in safety performance over the last 30 years, globally the construction sector continues to have a five-fold higher accident and sickness rate than the industry average. Research suggests that learning from incidents (LFI) in the construction industry is low and this remains a chronic issue. Barriers to LFI were identified from literature and interviews with six construction experts in the local construction industry. This was followed by a questionnaire survey of the health and safety professionals in multiple construction organizations in Saudi Arabia, resulting in 75 sets of valid responses. The primary factor that contributes to learning problems is ‘poor quality of actions’ (mitigation strategies) which suggests that construction employees’ involvement is limited in developing recommendations to prevent similar incidents in the future. Another major barrier to LFI is ‘limited dissemination of findings’ i.e., incidents reports are not shared across the organization. Other major barriers include ‘lack of commitment by management’, ‘lack of understanding of how and what to report’ – while recording an incident, and ‘limited quality of incident reports’. Application of interpretive structural molding (ISM) showed that ‘lack of commitment by management’ is the most influential barrier to LFI. Participants identified the ‘intervening phase’ in the LFI model as the most significant phase in terms of possible loss of learning from incidents in construction organizations.
A Fuzzy FMEA Framework for Risk-Based Reliability Assessment of Chiller Plants in Educational Facilities
Syed Ilyas, Awsan Mohammed, Mohammed Alhaji Mohammed, Mohammad S. Al-Homoud
Arabian Journal for Science and Engineering, 2026
Assessing mental fatigue in mobile crane operators using non-invasive wearable sensor: A study on construction site
Abdulrehman Shahid, Adel Alshibani, Maged S. Al-Quraishi, Waleed Umer, Awsan Mohammed
Advanced Engineering Informatics, 2026
Enhancing the reliability of steam turbine-driven air blowers in industrial processing units: a maintenance-centric approach
Awsan Mohammed, Talal AlShamrani, Adel Alshibani, Ahmed Ghaithan, Ahmed M. Attia
Journal of Quality in Maintenance Engineering, 2026
Purpose This paper aims to enhance the reliability of steam turbine systems in sulfur recovery units (SRUs) within the gas processing industry. These turbines are essential for driving air blower systems in the Claus process, yet they frequently encounter reliability challenges such as high vibration, misalignment and component degradation. The paper seeks to identify critical failure points and propose a robust improvement strategy. Design/methodology/approach A quantitative reliability framework is applied using ten years of historical maintenance data. Component failure behavior is modeled using an exponential failure distribution. System reliability is evaluated through reliability block diagram (RBD) modeling for series–parallel configurations, while availability is assessed using MTBF and MTTR metrics. A quantitative risk-based criticality index combining failure rate and repair duration is employed to prioritize failure-prone subsystems and guide reliability improvement efforts. Findings The results identify the steam turbine (ST), air blower (AB), overspeed mechanical trip mechanism (OMM) and coupling (CT) as the most critical components impacting system reliability. The proposed optimized reliability model achieves system availability of 99%, contributing to enhancing operational continuity, reducing maintenance costs and improving overall system safety. Originality/value This research provides a novel, component-level reliability assessment model for SRU steam turbines. It offers practical guidance for improving turbine performance and supports broader industry efforts toward predictive maintenance and operational excellence by integrating risk-based prioritization and reliability metrics.
Exploring emerging barriers to BIM adoption for sustainable project tracking and control readiness in Saudi Arabia: an ISM-based approach
Mohamed Mudawi, Naji Osman, Adel Alshibani, Mohammad A. Hassanain, Awsan Mohammed
Engineering Construction and Architectural Management, 2026
Purpose Although BIM has advanced globally, its implementation still faces resistance and practical challenges. This paper examines barriers to implementing building information modeling (BIM) for project tracking and control readiness from the perspectives of architectural/engineering (A/E) firms and contractors in Saudi Arabia. Design/methodology/approach Literature review was integrated with expert interviews. Two approaches were utilized for ranking barriers to BIM adoption for sustainable project tracking and control readiness. The first was the relative importance index (RII) to assess obstacles facing A/E firms and contractors. The second was the interpretive structural modeling (ISM) to analyze interdependence and key influencing barriers. Findings The top barriers for A/E firms were a lack of client demand, insufficient software skills, poor data quality, reliance on traditional practices, and budget constraints. For contractors, the main challenges included budget limitations, lack of client demand, inadequate software skills, early contractor involvement and project type. The ISM analysis highlighted incomplete adoption across stakeholders as a key dependent variable, driven by fundamental barriers. Practical implications This study points out the main barriers to adopting BIM as a new digital technology for tracking and controlling ongoing construction projects during the construction stage. Originality/value An integrated TOE–RII–ISM approach was proposed for identifying and prioritizing the key barriers to adopting BIM in project tracking and control, from two perspectives (A/E and Contractors). The findings offer valuable insights into current practices by providing a quantitative method that uses advanced models to determine the relationships among key barriers.
Fuzzy-based model for developing a digitalization index for evaluating construction project digitalization levels in Saudi Arabia
Awsan Mohammed, Yazeed AlSalamah, Ahmed Ghaithan, Ali Shash, Adel Alshibani
Construction Innovation, 2026
Purpose This paper aims to propose a digitalization index (DI) based on a fuzzy approach for evaluating the digitalization level of construction projects in the Saudi Arabian construction sector. It also aims to identify and rank potential digitalization activities and evaluate these activities using modified technology acceptance measures. This provides a quantitative framework for assessing digitalization levels in uncertain environments. Design/methodology/approach The research identifies potential digitalization activities within construction projects in the Saudi Arabian construction sector and ranks them based on their digitalization level. A fuzzy-based model is then developed to construct a DI and evaluate the degree of digitalization. The model is validated using data from a real project in Saudi Arabia. Findings The findings revealed significant differences in project activities based on the DI within the Saudi construction sector. Deviation tracking, document system updates and energy consumption improvements were the most digitalized activities. The proposed fuzzy model accurately assessed digitalization, aligning with actual project data, highlighting the sector’s generally low digital maturity and the need for targeted digital transformation in Saudi construction management. Practical implications The proposed model offers a practical tool for construction stakeholders to assess and quantify digitalization activities. The model helps stakeholders address challenges in digital transformation and prioritize activities for improving digital integration in construction projects. Originality/value This research proposes a DI as an innovative approach to assess the digitalization levels in construction projects, using a fuzzy-based model to deal with uncertainty. It incorporates modified technology acceptance measures and provides an innovative method for quantifying digital transformation in an uncertain environment.
Optimizing inspection and maintenance decisions in degrading systems with quality-loss
Ahmad Al Hanbali, Mohamed Noomane Darghouth, Ahmed Mohammed Ali Atiah, Ahmed Mansoor Hussein Ghaithan, Awsan Mohammed, Omar Alsawafy, Mohammad A. M. Abdel-Aal
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 2026
An optimization approach for improving steam production of heat recovery steam generator
Awsan Mohammed, Moath Al-Mansour, Ahmed M. Ghaithan, Adel Alshibani
Scientific Reports, 2025
Digital technologies and sustainability barriers in heavy construction: A structural equation modeling study on triple-bottom-line outcomes
Omar S. Taha, Adel Alshibani, Ahmed S. AlTuraik, Mohamed A. Mahmoud, Awsan Mohammed, Mohammad A. Hassanain
Results in Engineering, 2025
Optimization of Integrated Hybrid Renewable Energy Systems for Hydrogen Refueling Stations
Awsan Mohammed, Abbas Alabbas, Ahmed M. Ghaithan, Mohamed Noomane Darghouth, Ahmed M. Attia, Ahmad Al Hanbali
Arabian Journal for Science and Engineering, 2025
Design optimization of standalone photovoltaic systems for enhanced energy density
Ahmed M. Attia, Mohamed N. Darghouth, Ahmed M. Ghaithan, Awsan Mohammed, Mohammad A.M. Abdel-Aal, Ahmad Al Hanbali, Omar G. Alsawafy
Renewable Energy, 2025
A Mixed Integer Linear Programming Model for Optimal Sizing of Hydrogen Refueling Station Powered Using a PV-Grid System
Ahmed M. Ghaithan, Awsan Mohammed, Ahmad Al Hanbali, Adel Alshibani
Arabian Journal for Science and Engineering, 2025
Investigating the Effects of H2 Additions to Helium and Argon Shielding Gases on TIG-Welded AISI 316L Stainless Steel
Samir Khrais, Tariq Darabseh, Awsan Mohammed, Ahmad Abdel Al
Journal of Composites Science, 2025
Optimal Risk Management in Grid-Connected Photovoltaic System Design Under Environmental and Economic Changes: A Case Study in Saudi Arabia’s Climate
Ahmed M. Attia, Mohamed N. Darghouth, Awsan Mohammed, Mohammed A. M. Abdel-Aal
Process Integration and Optimization for Sustainability, 2025
Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations
Awsan Mohammed
Process Integration and Optimization for Sustainability, 2025
An optimization of hybrid renewable energy system for seawater desalination in Saudi Arabia
A. Mohammed, F. Alsagheer, A. M. Ghaithan, K. M. Mazher
International Journal of Environmental Science and Technology, 2025
Machine learning-based models for predicting calibration cost of flowmeters in the oil industry
Awsan Mohammed, Abdullah Javaid, Ahmed Ghaithan, Osamah AlDhafer, Maged S. Al-shaibani, Adel Alshibani
Neural Computing and Applications, 2025
A REGRESSION-BASED MODEL FOR PARAMETRIC COST ESTIMATION OF INDUSTRIAL STEEL STRUCTURES
Adel Alshibani, Osama Almuhtaseb, Awsan Mohammed, Ahmed M. Ghaithan
Journal of Civil Engineering and Management, 2025
Identifying the barriers and enablers of blockchain adoption in Saudi Arabian last-mile logistics using principal component analysis
Awsan Mohammed, Nokhaiz Tariq Khan, Omar Alkerishan, Ahmed Ghaithan
Technology Analysis and Strategic Management, 2025
Data-driven model for delay estimation in construction projects
Awsan Mohammed, Abdulrahman Al-Qahtani, Ahmed Ghaithan, Adel Alshibani
Journal of the Chinese Institute of Engineers Transactions of the Chinese Institute of Engineers Series A, 2025
Life-Cycle Sustainability for Vision 2030: The Impact of Life-cycle Sustainability Practices on Sustainability Performance of Saudi Oil and Gas Sector
Ahmed M. Ghaithan, Nokhaiz Tariq Khan, Hisham Alhunfush, Awsan Mohammed, Adel Alshibani
Arabian Journal for Science and Engineering, 2025
A multi-objectives optimisation model for the joint design of statistical process control and engineering process control
Salih O. Duffuaa, Omar Dehwah, Abdul Wahid Al Saif, Anas Alghazi, Awsan Mohammed
European Journal of Industrial Engineering, 2025
A Green Manufacturing Model: Joint Optimization of Maintenance, Scheduling, and Sustainability
Proceedings of International Conference on Computers and Industrial Engineering CIE, 2025
An integrated gray relational analysis – FMEA approach for risk assessment of petrochemical projects during the design stage
Awsan Mohammed, Ahmed Al-Baqami, Adel Alshibani, Laith A. Hadidi, Ahmed Ghaithan, Abbas Al-Abbas
Journal of Modelling in Management, 2025
Complex artificial intelligence models for energy sustainability in educational buildings
Rasikh Tariq, Awsan Mohammed, Adel Alshibani, Maria Soledad Ramírez-Montoya
Scientific Reports, 2024
Hybrid Framework for Investigating Digital Transformation Barriers in the Oil and Gas Sector
Adel Alshibani, Saleh Musleh Alkhathami, Mohammad A. Hassanain, Firas Tuffaha, Djamel Ouis, Awsan Mohammed
Energies, 2024
Fuzzy Logic-Based Method for Forecasting Project Final Cost
Adel Alshibani, Badr Eddin Hafez, Mohammad A. Hassanain, Awsan Mohammed, Mohammed Al-Osta, Ashraf Bahraq
Buildings, 2024
A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia
Adel Alshibani, Youssef Ahmed El Ghazzawi, Awsan Mohammed, Ahmed M. Ghaithan, Mohammad A. Hassanain
Journal of Financial Management of Property and Construction, 2024
A MILP Optimization Model for Sizing a Hybrid Concentrated Solar Power-Wind System Considering Energy Allocation
Ahmed M. Ghaithan, Ahmad Al Hanbali, Awsan Mohammed, Mohammad Abdel-Aal
Process Integration and Optimization for Sustainability, 2024
Dynamic material flow analysis of construction materials in Saudi Arabia: Strategies for sustainability of residential buildings
Awsan Mohammed, Ahmed AlSufyani, Ahmed Ghaithan, Adel Alshibani
Building and Environment, 2024
Dynamic Fleet Configuration Model for Optimizing Earthmoving Operations Using Mixed Integer Linear Programming
Zaid Khallaf, Adel Alshibani, Omar Alsawafy, Awsan Mohammed, Abdulaziz Bubshait
Journal of Construction Engineering and Management, 2024
Proactive framework for obsolescence management of electrical equipment in oil and gas industry
Ahmed Ghaithan, Osamah AlShamrani, Awsan Mohammed, Adel Alshibani
Journal of Quality in Maintenance Engineering, 2024
Optimal design of concentrated solar power-based hydrogen refueling station: Mixed integer linear programming approach
Ahmed M. Ghaithan, Musab Kondkari, Awsan Mohammed, Ahmed M. Attia
International Journal of Hydrogen Energy, 2024
Advancing sustainability: An integrated decision support framework for fleet selection in open pit mining construction
Adel Alshibani, Bahaa Elmaghraby, Abdulaziz Bubshait, Ahmed M. Ghaithan, Awsan Mohammed, Mohammad A. Hassanain
Results in Engineering, 2024
Feasibility Analysis of Integration Renewable Energy for Industrial Center in Neom City
Hussain AlHaydar, Awsan Mohammed, Ahmed Ghaithan
Process Integration and Optimization for Sustainability, 2024
Techno-economic assessment of concentrated solar power generation in Saudi Arabia
Ahmed Ghaithan, Laith Hadidi, Awsan Mohammed
Renewable Energy, 2024
The role of environmental social governance on achieving sustainable development goals in Gulf Cooperation Council countries
Awsan Mohammed, Naif Al-Mahdi, Ahmed Ghaithan
Environment Development and Sustainability, 2024
ECONOMIC-ENVIRONMENTAL ASSESSMENT OF CONCENTRATED SOLAR POWER FOR SEAWATER DESALINATION
Proceedings of International Conference on Computers and Industrial Engineering CIE, 2024
Effect of TIG welding parameters on 316 L stainless steel joints using taguchi L27 approach
Samir Khrais, Awsan Mohammed, Ahmad Abdel Al, Tariq Darabseh
Materials Research Express, 2024
A Markov-based model to mitigate human errors occurrence during maintenance activities in petrochemical systems
Laith A. Hadidi, Ahmed Ghaithan, Awsan Mohammed, Naif Alhwoaikan
International Journal of System Assurance Engineering and Management, 2023
Sizing a grid-connected photovoltaic system under economic and environmental uncertainties
Ahmed M. Attia, Mohamed N. Darghouth, Ahmed M. Ghaithan, Awsan Mohammed
Solar Energy, 2023
A meta-heuristic-based algorithm for designing multi-objective multi-echelon supply chain network
Awsan Mohammed, Maged S. Al-shaibani, Salih O. Duffuaa
Applied Soft Computing, 2023
Performance evaluation of meta-heuristic algorithms for designing multi-objective multi-product multi-echelon supply chain network
Salih O. Duffuaa, Awsan Mohammed
Soft Computing, 2023
An Optimization-Based Model for A Hybrid Photovoltaic-Hydrogen Storage System for Agricultural Operations in Saudi Arabia
Awsan Mohammed
Processes, 2023
A multi-objective optimization model based on mixed integer linear programming for sizing a hybrid PV-hydrogen storage system
Awsan Mohammed, Ahmed M. Ghaithan, Ahmad Al-Hanbali, Ahmed M. Attia
International Journal of Hydrogen Energy, 2023
Data driven-based model for predicting pump failures in the oil and gas industry
Awsan Mohammed
Engineering Failure Analysis, 2023
Integrated Impact of Circular Economy, Industry 4.0, and Lean Manufacturing on Sustainability Performance of Manufacturing Firms
Ahmed M. Ghaithan, Yasser Alshammakhi, Awsan Mohammed, Khwaja Mateen Mazher
International Journal of Environmental Research and Public Health, 2023
An integrated fuzzy-FMEA risk assessment approach for reinforced concrete structures in oil and gas industry
Awsan Mohammed, Ahmed Ghaithan, Fahad Al-Yami
Journal of Intelligent and Fuzzy Systems, 2023
An Optimization Model for Sizing a Hybrid Photovoltaic-Wind-Grid-Connected System in Saudi Arabia
Ahmed M. Ghaithan, Awsan Mohammed
Process Integration and Optimization for Sustainability, 2022
A hybrid algorithm based on tabu search and generalized network algorithm for designing multi-objective supply chain networks
Awsan Mohammed, Salih O. Duffuaa
Neural Computing and Applications, 2022
Identifying Measures of Effective Risk Management for Public–Private Partnership Infrastructure Projects in Developing Countries
Khwaja Mateen Mazher, Albert P. C. Chan, Rafiq M. Choudhry, Hafiz Zahoor, David J. Edwards, Ahmed M. Ghaithan, Awsan Mohammed, Mubashir Aziz
Sustainability Switzerland, 2022
Assessment of integrating solar energy with reverse osmosis desalination
Ahmed M. Ghaithan, Awsan Mohammed, Laith Hadidi
Sustainable Energy Technologies and Assessments, 2022
Spare parts supply with incoming quality control and inspection errors in condition based maintenance
Ahmad Al Hanbali, Haitham H. Saleh, Omar G. Alsawafy, Ahmed M. Attia, Ahmed M. Ghaithan, Awsan Mohammed
Computers and Industrial Engineering, 2022
A regression-based model for prediction of flowmeters calibration cost in oil and gas industry
Abdullah Javaid, Awsan Mohammed, Ahmed Ghaithan
Flow Measurement and Instrumentation, 2022
Multi-objective optimization of a photovoltaic-wind- grid connected system to power reverse osmosis desalination plant
Ahmed M. Ghaithan, Awsan Mohammed, Ahmad Al-Hanbali, Ahmed M. Attia, Haitham Saleh
Energy, 2022
A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic
Ahmed M. Ghaithan, Ibrahim Alarfaj, Awsan Mohammed, Osaid Qasim
Neural Computing and Applications, 2022
Performance evaluation and feasibility analysis of 10 kWp PV system for residential buildings in Saudi Arabia
Awsan Mohammed, Ahmed Ghaithan, Ahmad Al-Hanbali, Ahmed M. Attia, Haitham Saleh, Omar Alsawafy
Sustainable Energy Technologies and Assessments, 2022
An integrated decision support system for mobile crane selection
Mahmoud El-Tourkey, Adel Alshibani, Awsan Mohammed, Ali Shash, Firas Tuffaha
Expert Systems with Applications, 2022
Operational Energy and Carbon Cost Assessment Model for Family Houses in Saudi Arabia
Othman Alshamrani, Adel Alshibani, Awsan Mohammed
Sustainability Switzerland, 2022
A holistic reliability system approach of a water distribution network in Saudi Arabia
Laith Hadidi, Awsan Mohammed, Ahmed Ghaithan, Firas Tuffaha
Water Supply, 2021
Optimization of a solar-wind- grid powered desalination system in Saudi Arabia
Ahmed M. Ghaithan, Ahmad Al-Hanbali, Awsan Mohammed, Ahmed M. Attia, Haitham Saleh, Omar Alsawafy
Renewable Energy, 2021
Two-machine scheduling with aging effects and variable maintenance activities
Hesham Alfares, Awsan Mohammed, Mustafa Ghaleb
Computers and Industrial Engineering, 2021
Impact of industry 4.0 and lean manufacturing on the sustainability performance of plastic and petrochemical organizations in saudi arabia
Ahmed Ghaithan, Mohammed Khan, Awsan Mohammed, Laith Hadidi
Sustainability Switzerland, 2021
The role of blockchain in improving the processes and workflows in construction projects
Awsan Mohammed, Ahmad Almousa, Ahmed Ghaithan, Laith A. Hadidi
Applied Sciences Switzerland, 2021
A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system
Ahmed M. Attia, Ahmad Al Hanbali, Haitham H. Saleh, Omar G. Alsawafy, Ahmed M. Ghaithan, Awsan Mohammed
Energy, 2021
A regression-based model for estimating the energy consumption of school facilities in Saudi Arabia
Awsan Mohammed, Adel Alshibani, Othman Alshamrani, Mohammad Hassanain
Energy and Buildings, 2021
Reliability-based preventive maintenance strategy of truck unloading systems
Awsan Mohammed, Ahmed Ghaithan, Mashel Al-Saleh, Khalaf Al-Ofi
Applied Sciences Switzerland, 2020
Deploying municipal solid waste management 3R-WTE framework in saudi arabia: Challenges and future
Laith A. Hadidi, Ahmed Ghaithan, Awsan Mohammed, Khalaf Al-Ofi
Sustainability Switzerland, 2020
A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks
Awsan M. Mohammed, Salih O. Duffuaa
Expert Systems with Applications, 2020
A Meta-Heuristic Algorithm Based on Simulated Annealing for Designing Multi-Objective Supply Chain Systems
Awsan Mohammed, Salih Duffuaa
2019 Industrial and Systems Engineering Conference Isec 2019, 2019
Multi-objective mathematical model for closed loop supply chain design
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019

Awsan Mohammed

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Scopus Publications