Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

This paper aims to analyze the narratives that have emerged in the process of bureaucratic reform in Indonesia. The analysis is conducted using the Narrative Policy Framework at the mesa level. Using data from articles published in 6 credible national media about “bureaucratic reform” from 2010 to 2023. The collected data was classified according to the Narrative Policy Framework (NPF) elements in the article: Issue setting, the cause of the issue, plot, character (villain, victim, hero), and recommendations for solutions offered. There were 31 articles analyzed. The result showed that the main plot in the process of bureaucratic reform in Indonesia is based on the corrupt bureaucracy and the slow public service provided. The victims in the plot are the people who will access the services. The villains of the narrative are civil servants who do not improve the required competencies. The heroes of the narrative are several government institutions (Ministry of State Apparatus Utilization and Bureaucratic Reform, Commission of Corruption Eradication, and The Audit Board of The Republic of Indonesia) that are considered to expose the problem.

The objective of this research was to analyze several reading and writing methods used in educational settings, evaluating their pedagogical approaches and their effectiveness in the process of learning to read and write in school-age children. A systematic review was carried out in the open databases Dialnet and ScieELO, using different inclusion and exclusion criteria, which resulted in 164 documents, applying the PRISMA protocol, 20 were selected. A narrative synthesis analysis was carried out on the following dimensions: reading and writing methods, applied strategies, similarities with other methods and impact on the development of literacy. It is concluded that the combined application of the methods of synthetic and analytical approaches to reading and writing paves the way to attend to the diversity of learning styles, facilitates the strengthening of specific linguistic skills, and strengthens reading comprehension and writing competence.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.