Abstract
Motivated by the increasing demand of mass customisation in production systems, this paper proposes a robust and adaptive scheduling and dispatching method for high-mix human-robot collaborative manufacturing facilities. Scheduling and dispatching rules are derived to optimally track the desired production within the mix, while handling uncertainty in job processing times. The sequencing policy is dynamically adjusted by online forecasting the throughput of the facility as a function of the scheduling and dispatching rules. Numerical verification experiments confirm the possibility to accurately track highly variable production requests, despite the uncertainty of the system.
Highlights
Agile manufacturing is a relatively new term adopted to describe a production approach able to respond quickly to unforeseen customer demands, market volatilities, or other factors of high manufacturing impact such as changing lot sizes, variants, process technologies
With the aim of gaining a competitive advantage in terms of speed to react to these market volatilities, companies are leveraging on advanced components and Information Technologies
A simulation-based robust scheduling and dispatching algorithm has been proposed in this paper to control high-mix production systems
Summary
Agile manufacturing is a relatively new term adopted to describe a production approach able to respond quickly to unforeseen customer demands, market volatilities, or other factors of high manufacturing impact such as changing lot sizes, variants, process technologies. In contrast to lean manufacturing, one of the main principles of agile manufacturing is how to leverage the impact of production assets and data, while maintaining the lowest production costs In such Flexible Manufacturing Systems (FMS) optimisation of the production capacity, as well as proper scheduling (see Pinedo (2012)) and dispatching strategies are paramount, see e.g. Shi et al (2019); Ouelhadj and Petrovic (2009), and Blazewicz et al (2019). Introduced in Wu and Wysk (1989), the possibility to online evaluate the current performance of the production facility using software-mediated data is a promising direction, Morel et al (2003) and Wang et al (2019) This possibility has been exploited for the enhancement of both Manufacturing Operation Management (MOM) and Enterprise Resource Planning (ERP) systems, see Meyer et al (2009) and Moon and Phatak (2005), respectively. The simultaneous adoption of digital twins, possibly fed with advanced analytics coming from the field as in Li et al (2015), and predictive approaches, seems to represent the cornerstone to robustly manage the execution of flexible and collaborative production layouts
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