The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

The Integrated Warehouse Application (IWA) is a platform that combines inventory management, order management, warehouse operations, shipping, logistics, and reporting and analysis. It helps users manage their inventory to meet supply and demand requirements. Although many large companies use IWA to increase their operational efficiency, its complexity, technology, and customization requirements make it too expensive for small and medium-sized enterprises (SMEs). Our study focuses on SMEs in the retail business, where fast and accurate order processing is important for customer satisfaction and loyalty. We researched how to accommodate multi-echelon demand for the retail industry. To address these challenges, we used an Open-source ERP System called Odoo to create the IWA. Our findings show that implementing an integrated warehouse inventory system like the one provided by Odoo can offer real-time visibility into inventory levels, orders, and shipments. Automating manual processes enhances speed and accuracy, which improves customer service and business performance.

Penelitian ini dilakukan di Desa Mentaras, Kecamatan Dukun, Kabupaten Gresik. Beberapa masalah pokok yang terjadi di Desa Mentaras yaitu belum optimalnya pengetahuan dalam budidaya urban farming & pemanfaatan lahan pekarangan yang lebih produktif. Program ini dilakukan dengan cara melakukan observasi langsung dengan mengamati lokasi pembanding urban farming di Gresik dengan lokasi yang memiliki daerah dengan kondisi yang sama dengan Gresik, yaitu Surabaya dan Sidoarjo. Selanjutnya dilakukan tanya jawab dengan pelaku usaha urban farming di wilayah Surabaya dan Sidoarjo. Tahapan selanjutnya penyebaran kuesioner ke daerah sekitar Gresik untuk menentukan jenis lokasi dan tanaman yang cocok untuk dilakukan pengadaan urban farming. Dalam pengadaan urban farming, kami menggunakan Metode factor rating karena yang paling mudah dalam menentukan keputusan pemilihan lokasi, dalam metode ini melibatkan beberapa faktor penting yang perlu diperhatikan yaitu berdasarkan karakteristik lokasi, ketersediaan lahan, tanaman yang masyarakat sekitar inginkan, jenis sayur yang sedang dikonsumsi, kondisi curah hujan, kebutuhan konsumsi sayur masyarakat dalam seminggu, pengganti pemenuhan gizi selain sayur, kebersediaan masyarakat ketika diadakan penyuluhan urban farming, kondisi tanah, minat bercocok tanam masyarakat sekitar, dan sumber air. Tujuan dari dilakukannya program ini adalah untuk menentukan satu lokasi urban farming dari beberapa alternatif lokasi dengan menggunakan metode factor rating. Metode factor rating dilakukan dengan memberikan skor pada masing-masing faktor yang dipertimbangkan untuk mendapatkan lokasi pengadaan urban farming yang baik. Metode penelitian yang kami gunakan adalah metode campuran kuantitatif dan kualitatif dengan teknik analisis deskriptif dengan total sampel penelitian yang berjumlah 53 responden. Pengumpulan data yang kami gunakan secara pedoman studi, dokumentasi, kuesioner, wawancara, dan Focus Group Discussion (FGD). Hasil dan luaran kegiatan dari program yang dilaksanakan, yaitu mendapatkan satu lokasi yang dinilai cocok untuk dibangun urban farming karena lokasinya sudah memenuhi kriteria dari metode yang digunakan dan juga mempertimbangkan data yang telah diambil melalui kuisioner yang dibagikam. Selain itu, melalui sosialisasi yang diberikan kepada sekertaris desa dapat memberikan pengetahuan mengenai manfaat dari diadakannya urban farming.

Product availability is a crucial factor in maintaining customer satisfaction and securing revenue streams for retailers and suppliers. By clustering products based on demand patterns and inventory turnover, retailers can enhance efficiency and improve customer satisfaction. This study proposes optimizing product clustering by comparing dynamic time warping and k-means methods to find the best clustering solution. The results show that dynamic time warping outperforms the k-means algorithm, with a higher Silhouette score of 0.8406 compared to 0.7578. This indicates that our proposed method is more efficient in optimizing product clustering. Furthermore, the time complexity is faster compared to k-means. We also provide a numerical demonstration of the table-filling procedure for a small problem. Overall, this study offers insights into how retailers can improve their product clustering strategies to better meet customer needs and maximize revenue.