Abstract
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.
Highlights
A significant proportion of the world’s economy is based on the manufacturing industry [1]
We have explored the design mechanisms provided by several available tools but the features provided by Java Agent Development Environment (JADE) [32] were determined to be most suitable for the AOSR 2.0 strategy, e.g., JADE provides simple and flexible options for designing multi-agent scenarios with the ability to monitor overall agents’ interactions via the sniffer agent module
The prototype developed in JADE to validate the AOSR 2.0 strategy utilises a comprehensive dataset to represent large-scale applicability as discussed in Section 2, which includes thorough variation of different product classifications, SKUs, and time-bound situations related to product delivery and shipment
Summary
A significant proportion of the world’s economy is based on the manufacturing industry [1]. Industrial setups have been evolving ever since their inception This continuous growth is supported by incorporating process integration, mechanisation of operations and customised procedural manufacturing [2]. Extensive research and development have provided the manufacturing industry with high-tech solutions to speed up the process of production and delivery of end-products to customers by utilising the concepts of distributed artificial intelligence [4], Internet of Things (IoT) [5], Big Data [6], multi-agent systems (MAS) [7], cloud computing [8], and Industry 4.0 [9]. The initiative of Industry 4.0 recommends IoT-enabled, sustainable, Big Data-driven decisionmaking processes and digitized mass production within manufacturing systems [10] by utilising advanced infrastructural transformation and incorporating smart machines within the supply chain (SC), having nano- or micro-chips installed in them [11]. Large setups can afford such solutions, small-to-medium-sized enterprises (SME), which are mostly centrally controlled and mostly not compatible with such advanced systems [12], may lag behind [13]
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