Showing 10 results for Aziz
Shereen Abdelaziz, Munjiati Munawaroh,
Volume 0, Issue 0 (IN PRESS 2025)
Abstract
This study investigates the intersection of sustainable logistics and supply chain resilience, emphasizing their role in mitigating global disruptions such as natural disasters, pandemics, and geopolitical tensions. It identifies key trends, critical gaps, and actionable insights to guide the development of robust, adaptable, and sustainable supply chains. A bibliometric analysis of 480 scholarly works systematically maps the academic landscape, uncovering key themes, emerging trends, and knowledge gaps. The analysis focuses on sustainable logistics practices, such as green logistics, circular economy principles, and reverse logistics, alongside digital transformation technologies, including IoT, blockchain, and predictive analytics, to assess their integration into resilience strategies. The analysis reveals a fragmented approach to integrating sustainability and resilience, with practices often treated in isolation. Sustainable logistics practices enhance resource efficiency and adaptability but are constrained by the lack of holistic frameworks that integrate diverse sustainability practices with resilience strategies. While environmental dimensions and digital technologies, such as IoT and blockchain, are recognized as critical enablers, social and governance dimensions remain underexplored. Adoption disparities further hinder progress, particularly among SMEs, resource-constrained sectors, and underrepresented regions like Africa and South Asia. Inclusive frameworks, sector-specific applications, and empirical research—incorporating mixed methods, longitudinal studies, and real-world case studies—are essential to address these gaps and operationalize sustainability-resilience integration across diverse contexts. This research bridges a critical gap in the literature by presenting a comprehensive bibliometric analysis of sustainable logistics and supply chain resilience, emphasizing holistic, sector-specific, and integrative frameworks and highlighting the transformative role of digital technologies in achieving operational and strategic resilience.
F. Sereshki, S.a. Hosseini, N. Aziz , I. Porter ,
Volume 19, Issue 5 (IJES 2008)
Abstract
The Outburst can be defined as a sudden release of coal and rock accompanied by large quantities of gas into the underground coal mine workings which represents a major hazard in underground coal mines. Gas drainage has been proven to be successful in reducing outburst hazards by decreasing the in-situ gas pressure. One of aspect of gas drainage from coal seams is coal matrix volume changes. Current study is primarily concerned with experimental studies related to coal volume change (coal shrinkage) under various gas types and pressures. Two types of tests were conducted on each sample, the adsorption test for coal swelling and the desorption test for coal shrinkage. The gases used in the study were CH4, CO2, CH4/CO2 (50-50% volume), and N2. In this research, tests were conducted with respect to volumetric change behavior in different gases and their corresponding comparative results were presented.
Saeed Ramezani , Azizollah Memariani,
Volume 22, Issue 2 (IJIEPR 2011)
Abstract
Maintenance , as a support function, plays an important role in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistically expressed, are herein quantified and used in decision making. In this research, it is intended to justify the importance of historic data in oil analysis for fault detection. Initial rules derived by decision trees and visualization then these fault diagnosis rules corrected by experts. With the access to decent information sources, the wear behaviors of diesel engines are studied. Also, the relation between the final status of engine and selected features in oil analysis is analyzed. The dissertation and analysis of determining effective features in condition monitoring of equipments and their contribution, is the issue that has been studied through a Data Mining model.
A Azizi, V. Boppana , A.c. Clement,
Volume 22, Issue 4 (IJIEPR 2011)
Abstract
This paper demonstrates a preliminary investigation of geometry, function and its relation to DFX principles, namely DFM (Design for Manufacturing). This is the starting point for research on the development of an expert system that assesses design goals along DFX principles in a feature-based CAD environment. There is a need for a deeper level of understanding of the relationship between geometry and its effects on function, in order to correct and improve the product concept before large amounts of resources are invested in the product’s development.
This paper is a preliminary investigation into geometry and function involving DFM as part of an early stage of research into geometric effects on function and DFX through the use of CAD/CAE/CAM.In this paper, a concept is chosen to develop a parametric solid model that will be used to investigate a set of defined function attributes using model variants, which are evaluated in terms of the defined function attributes and DFM. The investigation found that for the functions defined, geometric parameters had less of an effect than expected. This is mainly due to the fact that the defined function attributes under investigation were associated with material properties. This paper demonstrates a preliminary investigation at the early stage of research to develop a more detailed relationship structure between geometry and functional attributes and their relationship with DFX. The end goal is to develop an integrated methodology involving geometry, function and DFX principles through the use of CAD/CAE/CAM .
Mohammad Sarvar Masouleh, Amir Azizi,
Volume 30, Issue 4 (IJIEPR 2019)
Abstract
The present research aims at investigating feasible improvements by determining optimal number of stations and required workforce using a simulation system, with the ultimate goal of reaching optimal throughput while respecting the problem constraints in an attempt to achieve maximum feasible performance in terms of production rate. For this purpose, similar research works were investigated by reviewing the relevant pieces of the literature on simulation model, car signoff station, and techniques for optimizing the station, and the model of the car signoff unit was designed using data gathering tools, existing documents, and actual observations. Subsequently, the model was validated by means of descriptive statistics and analysis of variance (ANOVA). Furthermore, available data was analyzed using ARENA and SPSS software tools. In a next step, potential improvements of the unit were identified and the model was evaluated accordingly. The results indicated that some 80% of the existing problems could be addressed by appropriately planning for human resources, on-time provision of the required material at reworking units, and minimization of transportation at the stations that contributed the most to the working queue. Thus, the waiting time per station could be minimized while increasing the production rate.
Mostafa Soltani, R. Azizmohammadi, Seyed Mohammad Hassan Hosseini, Mahdi Mohammadi Zanjani,
Volume 32, Issue 2 (IJIEPR 2021)
Abstract
The blood supply chain network is an especial case of the general supply chain network, which starts with the blood donating and ends with patients. Disasters such as earthquakes, floods, storms, and accidents usually event suddenly. Therefore, designing an efficient network for the blood supply chain network at emergencies is one of the most important challenging decisions for related managers. This paper aims to introduce a new blood supply chain network in disasters using the hub location approach. After introducing the last studies in blood supply chain and hub location separately, a new mixed-integer linear programming model based on hub location is presented for intercity transportation. Due to the complexity of this problem, two new methods are developed based on Particle Swarm Optimization and Differential Evolution algorithms to solve practical-sized problems. Real data related to a case study is used to test the developed mathematical model and to investigate the performance of the proposed algorithms. The result approves the accuracy of the new mathematical model and also the good performance of the proposed algorithms in solving the considered problem in real-sized dimensions. The proposed model is applicable considering new variables and operational constraints to more compatibility with reality. However, we considered the maximum possible demand for blood products in the proposed approach and so, lack of investigation of uncertainty conditions in key parameters is one of the most important limitations of this research.
Md. Rafsan Islam, Md. Azizur Rahman, Kazi Mohammad Nazib, Lasker Ershad Ali,
Volume 36, Issue 3 (IJIEPR 2025)
Abstract
The Capacitated Vehicle Routing Problem (CVRP) is a significant variant of the vehicle routing problem that incorporates constraints related to customer demand and vehicle capacity. Owing to its extensive applications in logistics and transportation, CVRP has attracted substantial research attention, with numerous algorithms proposed from the perspective of intelligent search. A common solution strategy involves two phases: first, assigning customers to different vehicles to form feasible routes, and second, optimizing these routes. This paper presents a two-phase CVRP solution framework through the clustering concept with intelligent search to improve route planning. In the first phase, a set of clustering methods - fuzzy c-means, k-means, and k-medoids - combined with a nearest neighbor heuristic search, are applied to generate feasible routes for each vehicle. In the second phase, these routes are iteratively optimized using the Simulated Annealing (SA) algorithm. The process yields three distinct solution pathways: fuzzy c-means with SA, k-means with SA, and k-medoids with SA. For performance evaluation, 46 benchmark CVRP datasets from a publicly available library are used. Simulation results demonstrate that k-means with SA performs the best, surpassing the other two approaches and outperforming other clustering-based two-phase state-of-the-art algorithms in terms of solution quality.
Salwa Mahmood, Ahmad Zahin Zainal Rashid, Nurul Ainina Nadhirah Tajurahim, Helmy Mustafa El Bakri, Ismail Abdul Rahman, Noorul Azreen Aziz,
Volume 36, Issue 4 (IJIEPR- Special Issue 2025)
Abstract
This study addresses ergonomic risks faced by firefighters during hose rolling activities, a physically demanding task that can lead to musculoskeletal issues. Building on a previous project conducted at the Bukit Gambir Fire and Rescue Station, this research expands the analysis by comparing four different hose rolling postures: roll and coil, dutch roll, flaking, and figure of eight. The Rapid Entire Body Assessment (REBA) method was used to evaluate the ergonomic risk levels of each posture. To further enhance firefighter safety, a preventive strategy tool was proposed and developed. The tool’s design was tested using Finite Element Analysis (FEA) in SolidWorks to assess the structural performance of its alloy steel hook and shaft under load. Simulation results showed stress levels below the material’s yield strength, and factor of safety (FOS) analysis confirmed the tool’s structural reliability. This project takes a holistic approach to understanding and mitigating ergonomic risks in firefighting. This study found that the structural of an assistive tool is safe and confirming the robustness and reliability of both the hook and alloy steel shaft designs. By combining ergonomic assessment with engineering simulation, it not only identifies high-risk postures but also provides a practical solution to reduce strain and prevent injury. Ultimately, the project contributes to improving the safety and well-being of firefighters, supporting a safer work environment for those who risk their lives to protect others.
Azizan Ramli, Siti Noraishah Ismail, Tofan Agung Eka Prasetya, Herman Bagus Dwicahyo, Cendana Fitrahanjani,
Volume 36, Issue 4 (IJIEPR- Special Issue 2025)
Abstract
Safety culture is a critical aspect of organizational culture, directly linked to improved safety performance. Although it is widely discussed in sectors like construction, manufacturing, and electronics, its significance in academic institutions is often overlooked. This study presents a systematic literature review (SLR) to explore the factors affecting safety culture in academic institutions across Malaysia and Indonesia from 2017 to 2021. The research includes a diverse range of institutions, including preschools, primary schools, secondary schools, matriculation colleges, vocational colleges, public and private universities, and training centers. Adopting the PRISMA methodology, the study applies thematic analysis to assess the findings, revealing eight key themes and 20 subthemes for Malaysia, compared to just two main themes and four subthemes for Indonesia. The results highlight safety competence, commitment, and attitude as the most influential elements for cultivating a strong safety culture in Malaysia. In terms of dimensions, behavioral factors (85%) were found to have the greatest impact on safety culture, followed by psychological factors (10%) and situational factors (5%). In Indonesia, the primary drivers of safety culture were situational and behavioral factors. Overall, the study underscores the importance of increasing awareness among academic institution leaders, governments, and policymakers to foster a robust safety culture in both countries.
Muhammad Nabhan Mohamed Nadzri, Azizan Ramli, Juwari Juwari,
Volume 36, Issue 4 (IJIEPR- Special Issue 2025)
Abstract
Start-up operations in small chemical plants represent a critical yet underexplored phase for human error analysis. This study presents the first systematic application of the Cognitive Reliability and Error Analysis Method (CREAM) to assess human reliability during start-up operations of three utility systems steam boilers, water-cooling pumps, and air compressors in a small Malaysian chemical plant. Unlike existing studies that focus on routine operations or large-scale facilities, this research addresses the unique challenges of manual start-up procedures in resource-constrained environments. Both basic and extended CREAM versions were applied using Hierarchical Task Analysis (HTA) validated by seven experts with more than 10 years of experience. The analysis revealed that all systems predominantly operated under tactical control mode, with human error probabilities ranging from 0.073 to 0.121. Water-cooling pump operations showed the highest risk (0.320) due to time constraints and collaboration quality issues, while boiler operations demonstrated the lowest risk (0.014) through structured procedures. Critical failure modes were identified in observation and timing-related tasks, particularly in speed verification and parameter adjustment subtasks. This study demonstrates CREAM's applicability to small-scale chemical plant start-ups and provides quantitative reference values for integrating human reliability assessment into Process Safety Management (PSM) systems. The findings support targeted interventions including procedural standardization, enhanced training for high-risk subtasks, and improved shift handover protocols to reduce human error in early-phase operations.
