OPTIMIZATION OF FISHBONE LAYOUTS IN WAREHOUSE MANAGEMENT: A FRAMEWORK FOR EFFICIENT PICKING PATH DESIGN

Abstract

1. Introduction Warehouses serve as critical nodes in the supply chain, facilitating the storage and movement of goods for various stakeholders, including manufacturers, wholesalers, and transport companies. Their operational efficiency directly influences logistics performance, particularly in industries reliant on perishable goods. The advent of Industry 4.0 has ushered in transformative technologies that enhance warehouse operations, transitioning traditional layouts into smart warehouses. This study investigated the design of a simulation model aimed at optimizing warehouse layout and efficiency, specifically through the implementation of a fishbone storage method. 2. Research Methodology This study presents a Prolog-based framework to optimize warehouse layouts by calculating rows and columns from key dimensions like width and aisle width. Incorporating the fishbone layout with diagonal aisles, it enhances retrieval efficiency by reducing travel distances and labor costs. By optimizing row distribution across pick zones and streamlining picking paths, the model improves space utilization, accessibility, and order fulfillment, providing an effective solution for modern warehouse management. 3. Findings and Discussion The Prolog model effectively calculates key warehouse layout metrics, specifically the number of rows and columns, based on parameters such as warehouse width and picking aisle width. The findings demonstrate that wider picking aisles reduce the number of rows, while narrower double row shelves increase the column count, directly impacting storage capacity. This model underscores the importance of balancing space utilization and operational efficiency, providing a practical tool. 4. Conclusion and implications The study develops a Prolog-based framework By utilizing a fishbone design, the framework streamlines retrieval processes, enabling faster order fulfillment and improved productivity while providing valuable insights for modernizing designs to enhance efficiency and scalability.