AOSR 2.0: A Novel Approach and Thorough Validation of an Agent-Oriented Storage and Retrieval WMS Planner for SMEs, under Industry 4.0

The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, but many of these benefits do not translate to SMEs. The agent-oriented smart factory (AOSF) framework provides a solution to help bridge the gap between Industry 4.0 frameworks and SME-oriented setups by providing a general and high-level supply chain (SC) framework and an associated agent-oriented storage and retrieval (AOSR)-based warehouse management strategy. This paper presents the extended heuristics of the AOSR algorithm and details how it improves the performance efficiency in an SME-oriented warehouse. A detailed discussion on the thorough validation via scenario-based experimentation and test cases explain how AOSR yielded 60–148% improved performance metrics in certain key areas of a warehouse.

[1]  Jaroslav Vrchota,et al.  Predictive Maintenance and Intelligent Sensors in Smart Factory: Review , 2021, Sensors.

[2]  Joseph F. Ryan,et al.  Validating Time Efficiency of AOSR 2.0: A Novel WMS Planner Algorithm for SMEs, under Industry 4.0 , 2020, J. Softw..

[3]  Cognitive Decision-Making Algorithms, Internet of Things Smart Devices, and Sustainable Organizational Performance in Industry 4.0-based Manufacturing Systems , 2020, Journal of Self-Governance and Management Economics.

[4]  Lutz Sommer,et al.  Industrial revolution - industry 4.0: Are German manufacturing SMEs the first victims of this revolution? , 2015 .

[5]  Nile W. Hatch,et al.  As Time Goes By: From the Industrial Revolutions to the Information Revolution , 2002 .

[6]  Ling Li,et al.  Supply Chain Management: Concepts, Techniques And Practices: Enhancing The Value Through Collaboration , 2007 .

[7]  Nils Masuch,et al.  Multi-Agent System in Practice: When Research Meets Reality , 2016, AAMAS.

[8]  E. H. Grosse,et al.  Logistics 4.0: a systematic review towards a new logistics system , 2019, Int. J. Prod. Res..

[9]  Yolande Berbers,et al.  Modeling Human Actors in an Intelligent Automated Warehouse , 2009, HCI.

[10]  Dan Simon,et al.  Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling , 2015, Eng. Appl. Artif. Intell..

[11]  김성태 SMART FACTORY 구축사례 , 2017 .

[12]  Piera Centobelli,et al.  Flow shop scheduling algorithm to optimize warehouse activities , 2016 .

[13]  Erik Hofmann,et al.  Industry 4.0 and the current status as well as future prospects on logistics , 2017, Comput. Ind..

[14]  David J. Paul,et al.  Formalisation of Problem and Domain Definition for Agent Oriented Smart Factory (AOSF) , 2018, 2018 IEEE Region Ten Symposium (Tensymp).

[15]  Big Data-driven Decision-Making Processes, Industry 4.0 Wireless Networks, and Digitized Mass Production in Cyber-Physical System-based Smart Factories , 2020, Economics, Management, and Financial Markets.

[16]  Big Data-driven Innovation, Deep Learning-assisted Smart Process Planning, and Product Decision-Making Information Systems in Sustainable Industry 4.0 , 2021, Economics, Management, and Financial Markets.

[17]  Barriers of Supply Chain Management Practices in Manufacturing Companies in Republic of Yemen: Pre-War Perspective , 2017 .

[18]  Alexandre Salles da Cunha,et al.  The Pickup and Delivery Problem with Cross-Docking , 2013, Comput. Oper. Res..

[19]  David J. Paul,et al.  Revitalising and Validating the Novel Approach of xAOSF Framework Under Industry 4.0 in Comparison with Linear SC , 2020, KES-AMSTA.

[20]  N. Jazdi,et al.  Cyber physical systems in the context of Industry 4.0 , 2014, 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.

[21]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[22]  David J. Paul,et al.  Conceptualised Visualisation of Extended Agent Oriented Smart Factory (xAOSF) Framework with Associated AOSR-WMS System , 2021, J. Softw..

[23]  John D. Worth,et al.  A Modern Approach , 2005 .

[24]  Artificial Intelligence-based Decision-Making Algorithms, Automated Production Systems, and Big Data-driven Innovation in Sustainable Industry 4.0 , 2020, Economics, Management, and Financial Markets.

[25]  Chien-Jung Huang,et al.  Warehouse management with lean and RFID application: a case study , 2013 .

[26]  Kagermann Henning Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[27]  Duncan C. McFarlane,et al.  The Role of Distributed Intelligence in Warehouse Management Systems , 2014, Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics.

[28]  Ulrich Berger,et al.  A multi-case study on Industry 4.0 for SME's in Brandenburg, Germany , 2018, HICSS.

[29]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[30]  David J. Paul,et al.  Agent-Oriented Smart Factory (AOSF): An MAS Based Framework for SMEs Under Industry 4.0 , 2018, KES-AMSTA.

[31]  Internet of Things-enabled Sustainability, Big Data-driven Decision-Making Processes, and Digitized Mass Production in Industry 4.0-based Manufacturing Systems , 2021, Journal of Self-Governance and Management Economics.

[32]  Gwynne Richards,et al.  Warehouse Management: A Complete Guide to Improving Efficiency and Minimizing Costs in the Modern Warehouse , 2011 .

[33]  Henry C. W. Lau,et al.  A RFID case-based logistics resource management system for managing order-picking operations in warehouses , 2009, Expert Syst. Appl..

[34]  Aabid Abdul Majeed,et al.  Internet of Things (IoT) Embedded Future Supply Chains for Industry 4.0: An Assessment from an ERP-based Fashion Apparel and Footwear Industry , 2017 .

[35]  K. Voigt,et al.  Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0 , 2018, Technological Forecasting and Social Change.