International Journal of Industrial Engineering: Theory, Applications and Practice
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Research on Improving The 9R Performance and Key Drivers of Circular Economy in The Bicycle Industry
No full textThe Circular Economy (CE) is recognized by scholars as a crucial tool for advancing Sustainable Development (SD). There is an urgent need to promote CE and develop an integrated model for its evaluation. Research indicates that industries face numerous challenges in implementing CE. To successfully transition to CE, it is essential to understand the key R-principle indicators and the supporting factors that facilitate this transition. This study focuses on Taiwan's bicycle industry, selected for its comprehensive supply chain system. A successful transformation in this sector could significantly benefit the overall development of Taiwan's industries. This study employs the Quality Function Deployment and the House of Quality as the fundamental framework to propose a practical model architecture, exploring how to accelerate the achievement of CE goals through driver factors. Using Fuzzy Multiple Attribute Decision Making methods, this research identifies the key driver factors for CE. The study identifies Reduce, Refuse, and Rethink as significant R principles and indicates that external funding, strong government support, enhanced industry competitiveness, government-led mandatory or voluntary social responsibility initiatives, and cost reduction are critical driver factors for realizing CE
Risk Assessment of Distributed Network Data Security Based on SimHash Algorithm
No full textDistributed network data has the characteristics of distribution and concurrency, which leads to the complexity of data processing and reduces the effectiveness of security risk assessment. Therefore, a security risk assessment method for distributed network data based on the SimHash algorithm is proposed. The actual support of the distributed network data set is reconstructed by probability distortion technology, and the data mining results after probability transformation are obtained by using the data mining method of random disturbance. In order to avoid the existence of duplicate information and redundant data, duplicate distributed network data is removed by calculating text similarity. Finally, the SimHash algorithm is used to calculate the hash value before and after the distributed network data attack, calculate the security risk assessment value of the distributed network data, and complete the security risk assessment. The analysis of the experimental results shows that the proposed method effectively improves the reliability of risk assessment of distributed network data and reduces the communication overhead of the assessment, with the maximum communication overhead not exceeding 10 bits. Therefore, the research method has high effectiveness and practicability
Wholesale Price and Buyback Contracts under Fairness Concerns and Asymmetric Information: A Case Study of Kalleh Dairy Company, Khuzestan Province Branch
No full textThis paper enhances supply chain coordination by incorporating behavioural considerations into contractual mechanisms. We analyze the performance of wholesale price and buyback contracts, highlighting the impact of fairness concerns on supply chain coordination. Our findings indicate the significant influence of supplier and retailer fairness concerns on order quantity and coordination. The buyback contract facilitates closely aligned coordination, while the wholesale price contract may achieve higher utility but lacks comparable coordination. Transparency in information sharing reduces retailer concerns and improves overall utility and coordination. Incorporating fairness concerns and behavioural considerations into contractual mechanisms also enhance supply chain coordination
Resilient Operator-Robot Collaboration in Smart Flexible Manufacturing Systems
Get PDFManufacturing key metrics are a useful approach for evaluating shop floor operations. The collaboration between operators and robots is essential in maintaining a resilient performance within smart and flexible manufacturing systems. For effective collaboration, both operators and robots must possess varying degrees of resilience, including full resilience, partial resilience and the ability to handle total disruptions. In this paper, lead time is considered a significant key metric. When the system is fully resilient and dependable, it achieves the optimal lead time. Consequently, lead time serves as a benchmark for evaluating the system's performance. However, if the robot experiences significant performance issues, it can negatively impact the cycle time, resulting in longer lead times. The discrepancy between the optimal lead time and the lead time obtained during partial or complete disruption is subtracted from the optimal lead time. To ensure the validity of the findings, mathematical equations are utilized in combination with other relevant data. This approach contributes to the knowledge base in the field. Finally, the paper will provide suggestions for future research endeavors.
RANKING OF KEY PERFORMANCE INDICATORS OF THE OVERHAUL PROCESS OF TECHNICAL SYSTEMS
Get PDFThe paper presents a developed model for measuring key performance indicators (KPI) of processes implemented in business organizations. The goal of the research is to identify problems that affect the processes through the measurement of KPIs, based on which the management of the organization improves its operations. The model was developed in four phases shown by the algorithm, based on higher-order fuzzy sets, more precisely, using Fermatean fuzzy sets (FFS). The first phase includes decomposing the process into sub-processes using the hierarchy-input-output-processing (HIPO) method and defining KPIs for measurements. In the second phase, the relative importance of sub-processes is determined using the Delphi method extended by FFS. In the third phase, KPIs are assessed at the level of each process by experts. In the fourth phase, KPIs are measured using the VIKOR (Multi-Criteria Optimization and Compromise Solution) method extended by FFS. The model was tested in a business organization that mainly deals with the maintenance of technical military systems. The obtained results confirmed the stability of the model by analyzing the comparison with the results of another method and by analyzing the sensitivity of the change of a certain parameter. The results have been verified in practice by the management of the business organization and, as such, serve for the constant improvement of business
AN EXPLORATORY OBSERVATION INTO THE STATUS OF COLLABORATIVE MANUFACTURING FOR TOOL AND DIE DEVELOPMENT IN MALAYSIA
No full textThis research explores the current status of collaborative manufacturing for tool and die development in the Malaysian manufacturing industry. Collaborative manufacturing is one of the characteristics that contribute to Industrial Revolution 4.0. Therefore, it is crucial to improve the current status of collaborative manufacturing in the Malaysian industry. Factory visits were conducted to observe the practices of manufacturing companies that use tools and dies, and the data collected from the observations were analyzed and compared to understand the present status of the industry. The findings suggest that the local industries are not significantly different from the overseas industries in most areas. However, some issues need to be addressed, particularly in terms of technology, quality, and management, to further improve the current status of collaborative manufacturing. Most importantly, the management’s participation and initiatives are crucial in establishing an ideal collaborative environment
Predicting Monthly Flight Cancellations in The Post-Pandemic Times Using Machine Learning Methods
No full textThis study explores the trends of flight cancellations using machine learning techniques in the post-pandemic times of COVID-19. The study identifies important factors influencing flight cancellations. The techniques used are linear regression, ridge regression, gradient-boosted regression, decision forest, and decision tree regression. Monthly data of ten leading US airlines with 33536 patterns of flight cancellations has been considered. The results of these sophisticated methods can help to make informed decisions in advance. The best training score of 0.99 is corresponding to the decision forest, followed by the random forest with a score of 0.93. Thus, Decision Forest has learned the data very well. Additionally, the best testing score of 0.65 is corresponding to a random forest, followed by a decision tree with a score of 0.37. Thus, on the basis of training decision tree is the best, whereas on the basis of testing, random forest is the best model. From the results, it is evident that monthly arrivals canceled have a relationship of 0.21 with both arrival delay15 and number of flights delayed attributed to the national aviation system. Moreover, its relationships with delayed flights attributable to late aircraft, weather and security reasons are 0.18, 0.12 and 0.07, respectively. The major contributions of the research are twofold. In the first place, predicting the number of cancellations in the post-pandemic circumstances. Next, machine learning techniques are implemented to draw meaningful conclusions for managing future airline schedules. Thus, the issue of flight cancellation can be properly managed
Discrete Event Simulation for COVID-19 Drive-Through Mass Vaccine Distribution Events
Get PDFThe key aim of this research is to demonstrate how a discrete-event simulation model and the simulation-based optimization model could help organizers of the mass vaccination distribution site better understand the system dynamics and make effective data-driven decisions. The proposed simulation model was developed based on empirical data collected during the COVID-19 drive-through vaccination events and insights provided by the system experts. Both the simulation and the simulation-based optimization models are found to be effective in helping the COVID-19 drive-through events’ decision-makers by providing information on how their system would respond over time under various scenarios leading to more informed and systematic decisions about the process and resource management. The model could easily be extended and reused for any other drive-through mass vaccination facilities in the future
INTEGRATING INTEGER PROGRAMMING WITH HEURISTIC ALGORITHM TO SOLVE THE WAREHOUSE RELOCATION PROBLEM OF AUTOMATED STORAGE AND RETRIEVAL SYSTEMS WITH MULTIPLE LOADING DEPOTS
No full textThe primary focus of the warehouse relocation problem is to systematically and efficiently move items already stored in the warehouse to newly planned storage locations, which is a critical issue for the successful operation of Automated Storage and Retrieval Systems (AS/RS). There is limited research on the warehouse relocation problem for AS/RS with multiple loading and unloading stations. To address this issue, this research proposed an approach to rearrange the materials stored in a unit-load AS/RS with multiple loading stations to a target assignment. A mathematical model based on integer programming was built to determine the optimal sequence for relocating storage locations and arrange the sequence of movement. Since this is at least NP-hard to solve, a heuristic algorithm was designed to solve this problem in several segments, enabling its application in practical scenarios with larger data scales to enhance the operational efficiency of automated storage systems. Computational experiments were conducted using various problem sizes to assess the performance of the proposed algorithm. Additionally, this study applied the proposed method to plan storage relocation for a large-scale automated AS/RS, which is operational at a computer hardware manufacturer in Taiwan, as a means to verify the feasibility and effectiveness of the method proposed in this research
Network Structure and Realization Path of Urban Green Sustainable Development Based on Improved Social Network Analysis Hybrid Method
No full textTelecommunication is one of the essential necessities of everyday life. In India, the telecommunications sector has seen a significant increase in the day-to-day. Telecommunications service companies hold data about their customers, and crisp graphs are used to depict these records. Examining and selecting the best mobile phone service providers (MPSPs) based on operational restrictions will help determine the best MPSPs. The analysis of MPSPs may be regarded as a difficult decision-making issue. The aim of this article is to provide an outline to examine the performance of MPSPs and the selection of the best MPSP for customers in India. The statistical data were obtained from the Telecom Regulatory Authority of India between April 2019 and March 2021. A novel approach for cosine similarity measures (CSM) among hesitancy fuzzy graphs (HFG) and estimating the certified repute scores of the experts by determining the ambiguous information of hesitancy fuzzy preference relations (HFPRs) and the regular cosine similarity grades from one separable HFPR to some others. And consider “objective” and “subjective” information given by experts. According to CSMs, we define the Laplacian energy of an HFG. This research provides a solution to a decision-making problem by applying the newly developed cosine similarity measure and the Laplacian energy of hesitancy fuzzy graphs. The ranking order of all alternatives and the best one is determined by calculating the cosine similarity between each alternative and the ideal alternative. Finally, an illustrated example is provided to show the applicability of the proposed approach to the decision-making problem as well as its effectiveness