This study aims to optimize the material flow and storage space in automated distribution centers (DCs) by determining the most efficient number of depalletized cases for different item classes based on demand levels.
Understanding and optimizing these parameters are crucial for reducing operational costs, enhancing system flexibility, and improving throughput in modern warehouses, especially under increasing automation trends and labor shortages.
The research addresses the complex interplay between storage capacity, material flow, and equipment utilization, focusing on two integrated automated storage and retrieval systems (AS/RS) for pallets and cases. It analyzes three demand-based item classes (A, B, and C) across multiple scenarios, considering the trade-offs between depalletizing frequency, space requirements, and crane utilization.
The methodology involves formulating analytical equations and employing a genetic algorithm (GA) to minimize total costs, including storage, equipment, and maintenance expenses. The study examines the impact of different batch size rules and replenishment scenarios on system performance, providing practical insights for warehouse design and operation planning.
The findings highlight that high-demand items should be replenished using full pallet depalletizing, while lower-demand items benefit from partial depalletizing scenarios. The model demonstrates significant cost reductions and improved crane utilization, offering a valuable decision-support tool for practitioners aiming to enhance warehouse efficiency and responsiveness in automated DCs.
هذا المنشور يتضمن
Efficient Material Flow and Storage Space Determination in Automated Distribution Centers
659.35 KBالوصف
This study aims to optimize the material flow and storage space in automated distribution centers (DCs) by determining the most efficient number of depalletized cases for different item classes based on demand levels.
Understanding and optimizing these parameters are crucial for reducing operational costs, enhancing system flexibility, and improving throughput in modern warehouses, especially under increasing automation trends and labor shortages.
The research addresses the complex interplay between storage capacity, material flow, and equipment utilization, focusing on two integrated automated storage and retrieval systems (AS/RS) for pallets and cases. It analyzes three demand-based item classes (A, B, and C) across multiple scenarios, considering the trade-offs between depalletizing frequency, space requirements, and crane utilization.
The methodology involves formulating analytical equations and employing a genetic algorithm (GA) to minimize total costs, including storage, equipment, and maintenance expenses. The study examines the impact of different batch size rules and replenishment scenarios on system performance, providing practical insights for warehouse design and operation planning.
The findings highlight that high-demand items should be replenished using full pallet depalletizing, while lower-demand items benefit from partial depalletizing scenarios. The model demonstrates significant cost reductions and improved crane utilization, offering a valuable decision-support tool for practitioners aiming to enhance warehouse efficiency and responsiveness in automated DCs.
جدول المحتويات
| الفصل 1 | Introduction |
| الفصل 2 | Literature Review |
| الفصل 3 | Materials and Methods |
| الفصل 4 | Results and Analysis |
| الفصل 5 | Conclusions |
| الفصل 6 | Data Availability |
| الفصل 7 | Conflicts of Interest |
| الفصل 8 | References |
