Anarchic manufacturing

Abstract

This paper introduces anarchic manufacturing, an extremely distributed planning and control philosophy as the methodology for planning and controlling future smart factories. Anarchic manufacturing delegates decision making authority and autonomy to the lowest level of entities in system elements with no centralised control or oversight. It is often postulated that traditional hierarchical structures may not be well suited to manage the state-of-the-art hyper-connected smart factories due to their reliance on communication between management layers. Distributed systems, on the other hand, are commonly perceived to be inherently more flexible, robust and adaptable than hierarchical systems due to their structure. This paper characterises distributed systems by evaluating the relative flexibility of a representative hierarchical system against an anarchic system in a job shop scenario. Multi agent based simulation is used to model both hierarchical and anarchic systems, which are tested for flexibility following the Taguchi method and compared against Taillard's benchmark job shop problems for overall performance. The results show that the anarchic system performs as well as the hierarchical system when subjected to unforeseen disruption, refuting the argument that hierarchical systems are too rigid and distributed systems are inherently more flexible. However, anarchic manufacturing systems, which show adaptability and self-optimising traits, provide a platform to potentially enable the emerging digital manufacturing paradigm through the free market structure especially when bandwidth for communications is limited.

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing

“…Thus, an efficient MC would mean a robust product towards right of CODP which could then be customized to final step by just a single process change. The methodology needs to be further expanded for autonomous decision making while identifying and selecting suitable modules as proposed by Ma et al [ 68 ]. This can support further evolution into Industry 5.0 applications.…”

Anarchic manufacturing: Distributed control for product transition

“…Distributed structures offer a completely different approach to transition and ramp up, given the volatile and unpredictable nature the proposed self-organising, flexible and adaptable characteristics of distributed systems (Ma et al, 2019a;Ouelhadj & Petrovic, 2009;Shen & Norrie, 1999) would be highly desirable. Distributed systems have been investigated for a variety of production scenarios and applications, for example the resource management of automated guided vehicles (De Ryck, Versteyhe, & Shariatmadar, 2020) and to integrate multiple planning functions for manufacturing (Kumar, Manjrekar, Singh, & Kumar Lad, 2020).…”

“…Distributed systems have been proposed as radically alternative decision making structures that have self-organising, flexible and highly adaptable characteristics (Ma, Nassehi, & Snider, 2019a); this self-healing trait is highly desirable during the transition and ramp up phase. Given the rise of smart manufacturing exploiting cyber-physical systems and Internet of Things technologies that provide low level computation capabilities (Monostori et al, 2016;Napoleone, Macchi, & Pozzetti, 2020), distributed systems for planning and control can be realised.…”

“…This paper utilises the anarchic manufacturing system (Ma et al, 2019a) and compares it against centralised systems in idealised transition scenarios and a real-world automotive case study. The generalised product transition scenario simulated, considers a manufacturing system that must move from producing one product to another, following predetermined ramp down and up curves over a prolonged duration.…”

Smart Cyber-Physical System applications in production and logistics: special issue editorial

“…Smart Cyber-Physical System applications in production and logistics: special issue editorial Current business environments, including production and logistics, are confronted with a great variety of emerging trends, such as growing collaboration of computational entities and networks of entities with the surrounding physical world (seen as Cyber-Physical Systems (Nahian Al Sunny, Liu, and Shahriar 2018; Khaitan and McCalley 2015)); creation and exploitation of large amounts of data/information (O'Donovan et al 2015;Dubey et al 2016;Preuveneers and Ilie-Zudor 2017;Zhong et al 2017); open hardware and software engineering (Bosch 2016); societal and business practice shifts such as digital currency (Narayanan et al 2016) (e.g. : Bitcoin, Namecoin, Litecoin); new human-machine interaction paired with advances in sensing and analytics (Wang et al 2018;Varsaluoma et al 2017); anarchic manufacturing (Ma, Nassehi, and Snider 2018) and many more. These trends and technologies separately as well as together are concurrently challenging and high potential, even to the level of disruptive innovation.…”