Optimal Control of Industrial Assembly Lines

Liberati, Francesco; Tortorelli, Andrea; Mazquiaran, Cesar; Imran, Muhammad; Panfili, Martina

doi:10.1109/codit49905.2020.9263946

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…We implemented the MPC algorithm proposed in our previous work [33], which controls the allocation of tasks and resources to the workstations in discrete time, with sampling time of T seconds. The MPC logic foresees that, every T seconds, an optimization problem is built and solved, to decide the best allocation of resources and tasks to workstations, while respecting all the applicable constraints.…”

Section: Mpc For Task Execution Controlmentioning

confidence: 99%

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

et al. 2022

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Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

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Section: Mpc For Task Execution Controlmentioning

confidence: 99%

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

et al. 2022

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“…The control problem is formulated in discrete time. The formulation proposed in this paper is an extension of [23], where an algorithm is presented to control tasks' execution with the sole goal of minimizing the cycle time, while respecting all the existing constraints. In this paper, instead, we focus on the extension of the problem to enable an energy-aware control of the tasks, including in the optimization process also the goal of minimizing the energy bill, reducing the power peaks at the point of connection with the grid, and maximizing the self-consumption of the locally produced renewable energy.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the following, the problem formulation is presented. We start by only recalling the main variables introduced in [23] for controlling tasks' execution and resources' assignment to the workstations. The detailed formulation of the associated constraints can be found in [23].…”

Section: Problem Formulationmentioning

confidence: 99%

Energy-Aware Model Predictive Control of Assembly Lines

2022

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This paper presents a model predictive approach to the energy-aware control of tasks’ execution in an assembly line. The proposed algorithm takes into account both the need for optimizing the assembly line operations (in terms of the minimization of the total cycle time) and that of optimizing the energy consumption deriving from the operations, by exploiting the flexibility added by the presence of a local source of renewable energy (a common scenario of industries that are often equipped, e.g., with photovoltaic plants) and, possibly, also exploiting an energy storage plant. The energy-related objectives we take into account refer to the minimization of the energy bill and the minimization of the peaks in the power injected and absorbed from the grid (which is desirable also from the perspective of the network operator). We propose a mixed-integer linear formulation of the optimization problem, through the use of H-infinite norms, instead of the quadratic ones. Simulation results show the effectiveness of the proposed algorithm in finding a trade-off that allows keeping at a minimum the cycle time, while saving on the energy bill and reducing peak powers.

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Action Masked Deep Reinforcement learning for Controlling Industrial Assembly Lines

Ali,

Tirel

2023

2023 IEEE World AI IoT Congress (AIIoT)

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Optimal Control of Industrial Assembly Lines

Cited by 3 publications

References 13 publications

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Energy-Aware Model Predictive Control of Assembly Lines

Action Masked Deep Reinforcement learning for Controlling Industrial Assembly Lines

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