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Thesis Format

Monograph

Degree

Doctor of Philosophy

Program

Mechanical and Materials Engineering

Supervisor

Yang, Jun

2nd Supervisor

Knopf, George K.

Co-Supervisor

Abstract

The significant growth in e-commerce has highlighted the importance of optimizing warehouse operations, particularly the order-picking process, which can substantially reduce operational waste. Order-picking consists of up to 55% of total warehouse operating costs. Decision-making in this field has become more complex for human management due to varying system conditions, a large number of variables and constraints, and huge data volumes. Digital twin (DT) is a cost-effective solution that can accurately simulate system behaviors and replicate essential functional characteristics of physical system operations by utilizing real-time data. By incorporating human factors, DT plays a key role in facilitating the transition to Industry 5.0 (I5.0). As the cyber component of Cyber-Physical Systems (CPS), DT employs IT technologies, including Artificial Intelligence (AI), to simulate real-world systems and optimize complex problems. By incorporating optimization and AI-based techniques, DTs facilitate data analysis and accurate simulations, enabling more efficient decision-making. An intelligent warehouse digital twin can simulate the order-picking process, generate improvement scenarios, and assist with the implementation of effective solutions without trial and error. This study aims to develop a warehouse digital twin using a Discrete Event System Simulation model to predict and enhance operational efficiency in manual picker-to-part systems. The intelligent digital twin integrates optimization components to improve warehouse performance. The order-picking operation is modeled as a multiple-server queuing system using near real-time data. Optimization components include a dynamic order-batching (DOB) algorithm, and multi-objective storage location assignment problem (SLAP) optimizers, considering practical factors in the order-picking process. Optimization models are developed using mixed integer programming (MIP) techniques. The DOB combines heuristic and mixed integer linear programming (MILP) models, with feasibility assessed by a CP-SAT solver. Near-optimal picking tours are derived from the Travelling Salesman Problem (TSP) model, solved by a meta-heuristic optimizer. The SLAP model is developed using an integer non-linear programming (INP) technique and solved by a customized optimizer inspired by Simulated Annealing and Basin-Hopping algorithms. Experimental evaluation using warehouse data from a commercial facility in Australia shows that employing DOB reduces order throughput time by up to 15.2%, improving traveling time and distance. Multi-objective SLAP optimization enhances picking efficiency and significantly improves safety and rack stability by over 90%. These outcomes provide invaluable information for decision-makers to optimize warehouse operations without trial and error.

Summary for Lay Audience

Digital twin (DT) is a digitalized representation of a real-world system that helps companies run smoother without spending a lot of money. By considering human involvement, DT plays a key role in the transition of industries towards the new generation of Industry, so-called Industry 5.0, where technologies like Artificial Intelligence and Cyber-Physical Systems revolutionize manufacturing and logistics. DT was first introduced in 2002 and has since become a powerful tool for simulating real-world scenarios and solving complex problems. One area where DT can make a significant impact is warehouse management. In warehouses, where efficiency is crucial, DT can predict and improve operations. This study focuses on creating a digital twin for warehouses, built upon simulation models to enhance the efficiency of manual order-picking systems. By integrating optimization techniques, artificial intelligence and human factors into the digital twin, DT is enabled to improve various aspects of warehouse operations. In this study, algorithms are developed to optimize order-batching, and storage allocation, all tailored to real-world conditions. The key to success is using mathematical techniques like mixed integer programming and meta-heuristic optimization. These methods allowed for near-optimal solutions to be found quickly, even in complex warehouse environments. Testing this approach with real warehouse data demonstrates significant improvements in warehouse operational efficiency. By optimizing order-batching, the proposed method could reduce order throughput time by nearly 15%, while also ensuring practical considerations were met. Furthermore, by optimizing storage allocation with multi-objective optimization, safety and rack stability are improved by more than 90%. These enhancements not only increased productivity but also made warehouses safer and more stable environments. Overall, this study demonstrates the power of digital twins in optimizing warehouse operations. By providing valuable insights and actionable recommendations, digital twins can help decision-makers make informed choices and drive continuous improvement without the need for trial and error.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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