IMT Nord Europe - PhD - Digital Twin-Driven Warehouse Optimization: Advancing Efficiency and Ergonomics in Modern Warehouse Operations (F/H)

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This thesis proposes integrating multimethod simulation models to develop a digital twin aiming to optimize warehouse operations with a strong emphasis on ergonomic considerations. It employs, Optimization and Simulation to advance warehouse management, filling notable gaps in the current literature. The research, positioned at the forefront of the state of the art, aims to design warehouses that balance efficiency with ergonomic optimization. Methodologically, it involves applying optimization and Discrete Event Simulation (DES) for modeling order-picking processes and Multi-Agent Systems (MAS) for simulating interactions among Automated Guided Vehicles, cobots, and human workers. This approach leads to the development of a decision-making tool for warehouse operations. This collaboration between IMT Nord Europe and the University of Padua combines expertise in manufacturing and logistics, optimization, and simulation. The anticipated outcomes include enhanced methodologies for ergonomic integration in warehouse operations, and balancing human-centric design with operational efficiency. These contributions are expected to develop future warehouse management research and practices, in line with the EU Commission's Industry 5.0 guidelines, promoting sustainable, resilient, and human-centered models. Keywords: Smart Warehouse Systems, Digital Twin, Optimization, Simulation, Sustainable Logistics Solutions, Industry 4.0 Scientific content: This research proposal focuses on advancing warehouse optimization with a digital twin model that integrates multi-agent and discrete event simulations. The model aims to improve operational efficiency and ergonomics by using optimization and Discrete Event Simulation for process optimization and Multi-Agent Systems for simulating agent interactions, thereby enhancing warehouse management and worker well- being. Our focus is on tactical and operational decision-making in warehouse management, specifically in optimizing order-picking processes through routing, scheduling, and workforce management. The approach aligns with Industry 4.0 trends, incorporating technologies like AGVs and cobots to modernize warehouse operations. Firstly, we will address routing and scheduling for order-picking optimization. The integration of optimization and Discrete Event Simulation in this phase will allow us to simulate and analyze the impact of different routing strategies on overall warehouse efficiency, especially focusing on order-picking processes. Secondly, our methodology emphasizes workforce management, where we seek to balance the roles of cobots, AGVs, and human operators. Here, Multi-Agent Systems will be employed to facilitate coordination among Automated Guided Vehicles (AGVs), cobots, and human workers, enhancing the order-picking process. Research Methodology: · Application of Optimization and Discrete Event Simulation (DES): Application of optimization and DES to model the order-picking process. This step will focus on understanding and optimizing the workflow, factoring in demand fluctuations, and supply chain dynamics, with an emphasis on ergonomic safety and efficiency. · Integration of Multi-Agent Systems (MAS): Following DES, we incorporate MAS to simulate dynamic interactions among Automated Guided Vehicles (AGVs), cobots, and human workers. This integration is aimed at modeling and optimizing these interactions with an emphasis on ergonomic safety and efficiency, ensuring a balance between human-centric design and operational effectiveness. · Development of a Digital Twin: Utilizing the models and insights from DES and MAS, we will develop a digital twin of the warehouse. This virtual model will enable real-time analysis and optimization of the routing and scheduling of order-picking processes, with a comprehensive focus on ergonomics, operational efficiency, and adaptability to varying operational conditions. Workplace Information: · Possibility of partial remote work · Exceptional work environment · Numerous holidays · On-site collective catering · Subsidy for public transportation for home-to-work commute (75%) · Sustainable mobility package (for carpooling or cycling) · Family supplement depending on family composition · Wide range of social benefits (first installation indemnity, financial aid, vacation checks, etc.) · Stimulating innovation ecosystem (startups, students, research, companies) Candidate profile: · Master’s Degree in Industrial Engineering, Computer Science, Operations Research, or a related field, with a strong academic record. · Proficiency in programming languages suitable for simulation and optimization (e.g., Python, Java, C++). · Experience with simulation software (e.g., Anylogic, Witness, Arena) and tools related to digital twins and logistics optimization is highly desirable. Douai (59) SUPERVISOR(S) OF THE THESIS: IMT Nord Europe: - Arnaud DONIEC, thesis director; contact: - Amine ABDOUS, co-supervisor; contact: University of Padua: - Daria BATTINI, co-director of the thesis - Serena FINCO, co-encadrante

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IMT Nord Europe - PhD - Digital Twin-Driven Warehouse Optimization: Advancing Efficiency and Ergonomics in Modern Warehouse Operations (F/H)

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