Anarchic manufacturing: distributed control for product transition

Andrew K C Ma*, Marcus Frantzén, Chris M Snider, Aydin Nassehi

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Manufacturers are poorly equipped to manage product transition scenarios, when moving from one product to another. Most tools consider a mature system, yet during transition and ramp up disturbances and inefficiency are common. The traditional methods, using centralised planning and control structures are too rigid and often resort to simple dispatch heuristics in this highly dynamic environment. Distributed systems have been proposed to leverage their self-organising and flexible traits to manage highly volatile and complex scenarios.
Anarchic manufacturing, a free market based distributed planning and control system, delegates decision-making authority and autonomy to system elements at the lowest level; this system has previously been shown to manage job and flowshop style problems. The system has been adapted to use a dynamic batching mechanism, where jobs cooperate to benefit from economies of scale. The batch enables a direct economic viability assessment within the free market architecture, whether an individual machine should changeover production to another product type. This profitability assessment considers the overall system state and an agent’s individual circumstance, which in turn reduces system myopia. Four experiments, including a real-world automotive case study, evaluate the anarchic manufacturing system against two centralised systems, using three different ramp-up curves. Although not always best performing against centralised systems, the anarchic manufacturing system is shown to manage transition scenarios effectively, displaying self-organising and flexible characteristics. The hierarchical system was shown to be impeded by its simplifying structure, as a result of structural rigidity.
Original languageEnglish
Pages (from-to)1 - 10
Number of pages10
JournalJournal of Manufacturing Systems
Volume56
Early online date18 May 2020
DOIs
Publication statusE-pub ahead of print - 18 May 2020

Keywords

  • Simulation
  • planning and control
  • distributed systems

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