OR in the industrial engineering of Industry 4.0: experiences from the Iberian Peninsula mirrored in CJOR

Mula, Josefa; Bogataj, Marija

doi:10.1007/s10100-021-00740-x

Cited by 5 publications

(1 citation statement)

References 60 publications

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“…From a PPC view, Industry 4.0 (I4.0) is a connection of digital technologies, organisational concepts and management principles to provide cost-effective, responsive, resilient and sustainable operations (Ivanov et al 2021;Cañas, Mula, and Campuzano-Bolarín 2020;Mula and Bogataj 2021;Bueno, Filho, and Frank 2020). The PPC area is evolving, according to design principles, digital enabling technologies and implementation principles of the I4.0 initiative (Cañas et al 2021), towards self-management PPC models based on automation and autonomy.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning applied to production planning and control

Esteso

Peidro

Díaz-Madroñero

2022

International Journal of Production Research

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The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

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