The School of Electrical Engineering and Computing, The College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); frans.kenskensens@newcastle. For SC operations, warehouses serve as the real backbone for maintaining the whole value chain [14]. Handling warehouse operations efficiently is not possible without a warehouse management system (WMS). A WMS is a software application that supports day-to-day operations in a warehouse. Extensive research is conducted to provide complete autonomous warehousing systems, e.g., the solutions with robo-machines and auto-conveyor belts, such as the flow shop algorithm [15] and EMBBO [16] or niche warehousing problems such as the pickup and delivery problem with cross-docking (PDPCD) [17]. Even though the idea of Industry 4.0 is transforming the manufacturing industry via state-of-the-art decision-making algorithms, automated production systems, and Big Data-driven innovation [18; 19], recent research claims that Industry 4.0 standards cannot be purely mapped to SMEs [12; 13]. The concept of Logistics 4.0 [20] is also a futuristic progression in this domain, which recommends incorporating optimised routing, reduced storage requirements, and autonomous robots with tracking and decision systems for optimised inventory control and information exchange; however, Industry 4.0 is still at its very beginning, and any future impacts on logistics management are rather uncertain [21]. SMEs are still facing warehousing issues, such as wandering items or picking lists [22], inaccurate stock values at runtime [23], unmanaged receiving and expedition areas [24], unmanaged storage capacity, and inappropriate retrieval scheduling [4]. In order to provide an overarching solution to help bring the SMEs many of the benefits of Industry 4.0, the agent-oriented smart factory (AOSF) framework [25] provides a comprehensive and high-level SC architecture for SMEs under the umbrella of Industry 4.0, and the agent-oriented storage and retrieval (AOSR) system [26] presents a complementary addition to the novel approach of the AOSF framework, to provide a low-cost, semi-autonomous warehouse system. The episodic series of this broad contribution includes: a high-level SC architecture of the AOSF framework [25]; its problem and domain definition, which provided the baseline model for the agent-oriented storage and retrieval (AOSR) system [27]; the AOSF's recommended _six-feature strategy_ and the general workflow of the AOSR system [26]; the validation of the AOSF framework in comparison with a linear supply chain [28]; the validation of the time efficiency of the AOSR's algorithmic strategy [29]; and the conceptualisation of the AOSF as a customised CPS [30]. The main contribution of this paper is the definition of the extended heuristics of AOSR 2.0. This paper also demonstrates that AOSR improves existing warehouse algorithms. This is performed via scenario-based test cases, in comparison with the standard linear SC-based standard warehouse management system (WMS) strategies. The remainder of this paper is structured as follows. Section 2 describes the methodology used in this paper. This includes a complete overview of the AOSR 2.0 algorithmic heuristics, as well as a description of the methods and data used to compare our new algorithm with existing strategies.