{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/28931"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/28931","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Intelligent Digital Twin for Optimizing Warehouse Operations: Embedded Optimization Components for Enhanced Order-Picking Efficiency","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.","abstract_html":"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.","abstract_has_math":false,"creators":["ZarinchangMokalla, Amir"],"institution":"The University of Western Ontario","degree_name":"Ph D","degree_level":null,"degree_discipline":"Mechanical and Materials Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Yang, Jun","Knopf, George K."],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-11-15","date_published":"2024-11-15","updated_at":"2026-07-27T21:55:58Z","subjects":["Intelligent digital twin","order-picking optimization","Logistics 5.0","multi-objective storage location assignment problem","embedded soft component","dynamic order batching","mixed integer programming","basin-hopping","simulated annealing","worker safety"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/28931","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Yang, Jun","Knopf, George K."]},{"key":"dc:creator","label":"Author","values":["ZarinchangMokalla, Amir"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T16:16:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T16:16:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-11-15"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical and Materials Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph D"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Intelligent digital twin","order-picking optimization","Logistics 5.0","multi-objective storage location assignment problem","embedded soft component","dynamic order batching","mixed integer programming","basin-hopping","simulated annealing","worker safety"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/28931"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Intelligent Digital Twin for Optimizing Warehouse Operations: Embedded Optimization Components for Enhanced Order-Picking Efficiency"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yang, Jun","Knopf, George K."],"dc:creator":["ZarinchangMokalla, Amir"],"dc:date.accessioned":["2025-07-10T16:16:39Z"],"dc:date.available":["2025-07-10T16:16:39Z"],"dc:date.issued":["2024-11-15"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.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."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/28931"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Intelligent digital twin","order-picking optimization","Logistics 5.0","multi-objective storage location assignment problem","embedded soft component","dynamic order batching","mixed integer programming","basin-hopping","simulated annealing","worker safety"],"dc:title":["Intelligent Digital Twin for Optimizing Warehouse Operations: Embedded Optimization Components for Enhanced Order-Picking Efficiency"],"dc:type":["thesis"],"thesis:degree_discipline":["Mechanical and Materials Engineering"],"thesis:degree_name":["Ph D"]},"updated_at":"2026-07-27T21:55:58Z"}