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Journal : International Journal Software Engineering and Computer Science (IJSECS)

Enhancing Logistic Efficiency in Product Distribution through Genetic Algorithms (GAs) for Route Optimization Judijanto, Loso; Fauzan, Tribowo Rachmat; Fisher, Bobby
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1872

Abstract

This research highlights the significant potential of Genetic Algorithms (GA) as a powerful tool for optimizing logistics distribution routes. The utilization of GA has led to substantial improvements in route efficiency, resulting in cost reductions and shorter delivery times. Notably, the inclusion of customer satisfaction as a key parameter in route optimization emphasizes the importance of meeting customer expectations and ensuring timely deliveries. Additionally, the study recognizes the positive environmental implications of reduced travel distances and durations, indicating a favorable impact on environmental sustainability by reducing carbon emissions. Ethical considerations remain paramount, as the research employs anonymized data sources and adheres rigorously to industry standards to safeguard data privacy. Comparative analyses consistently favor GA over conventional distribution methods, reaffirming its capacity to generate more efficient routes. Overall, this investigation underscores the versatility and efficacy of Genetic Algorithms in addressing complex logistics distribution challenges, offering practical solutions that benefit businesses, customers, and environmental conservation alike.
Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries Nofirman; Ahmada, Naufal Haidar; Fauzan, Tribowo Rachmat
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2027

Abstract

-This research analyzes potential locations for the manufacturing industry studied using a GIS approach and data analysis. Researchers combine statistical and spatial analysis methods and unique techniques such as TOPSIS and MOORA to evaluate the most suitable locations based on predefined criteria. Key findings show that Purbalingga Regency is the optimal location, supported by high labor availability, developed logistics infrastructure, and supportive environmental regulations. Sumedang Regency also shows good potential, especially regarding vital market accessibility and strict environmental regulations. However, Bengkulu City faces challenges in several aspects, such as underdeveloped logistics infrastructure and suboptimal ecological regulations. The implications of these findings for manufacturing location decision-making practices, the advantages and disadvantages of GIS approaches and data analysis, and the research contributions to science and the manufacturing industry are also discussed in depth. Thus, this research provides valuable insights for decision-makers in allocating resources and planning investments in the manufacturing industry.
Optimization of Hospital Queue Management Using Priority Queue Algorithm and Reinforcement Learning for Emergency Service Prioritization Adhicandra, Iwan; Nurhidayati, Safitri; Fauzan, Tribowo Rachmat
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2772

Abstract

This study aims to develop and implement an efficient hospital queue management system by integrating the Priority Queue algorithm with Reinforcement Learning (RL). The primary objective is to enhance the prioritization of emergency patients, ensuring that those with the most critical conditions receive timely care. The Priority Queue algorithm facilitates the sorting of patients based on the severity of their medical conditions, while RL enables the system to continuously learn and optimize the queue management process using historical data and real-time feedback. The research methodology includes data collection from hospital queues, algorithm model development, and simulated and real-world data validation. The results demonstrate that the combination of these algorithms significantly reduces waiting times for emergency patients and improves overall hospital operational efficiency. Additionally, implementing this algorithm has increased patient satisfaction due to shorter wait times and more timely services. The study concludes that the Priority Queue algorithm enhanced by RL is an effective solution for hospital queue management and recommends further research on larger scales and with more complex algorithms.