The core idea of flexible manufacturing is adapting to changes. In this domain, the machine is not confined to a single fixed type of process but can perform different jobs (e.g., cutting, drilling) in different ways (e.g., varying speed, tool, power consumption). This adaptability should be enabled by a detailed view of how the machines work. The idea is to perform machine scheduling by exploiting the dynamical models-expressed as differential equations-of manufacturing processes, i.e., both machines and production items. The main innovation in this paper is the ability to compute a machine's schedule where the state of the product does not linearly evolve in time but is determined by the set of differential equations instead. Finding the schedule is defined as a multi-objective optimization problem-manufacturers may seek a trade-off between processing time, energy consumption, and other cost functions. The proposed optimization is evaluated using accurate process models, exemplifying how it works and harnesses the expressiveness of differential equations.

Exploiting Process Dynamics in Multi-Stage Schedule Optimization for Flexible Manufacturing

Fraccaroli, E;Chakraborty, S
2022-01-01

Abstract

The core idea of flexible manufacturing is adapting to changes. In this domain, the machine is not confined to a single fixed type of process but can perform different jobs (e.g., cutting, drilling) in different ways (e.g., varying speed, tool, power consumption). This adaptability should be enabled by a detailed view of how the machines work. The idea is to perform machine scheduling by exploiting the dynamical models-expressed as differential equations-of manufacturing processes, i.e., both machines and production items. The main innovation in this paper is the ability to compute a machine's schedule where the state of the product does not linearly evolve in time but is determined by the set of differential equations instead. Finding the schedule is defined as a multi-objective optimization problem-manufacturers may seek a trade-off between processing time, energy consumption, and other cost functions. The proposed optimization is evaluated using accurate process models, exemplifying how it works and harnesses the expressiveness of differential equations.
2022
978-1-6654-9996-5
Flexible manufacturing
scheduling
multi-objective optimization
particle swarm optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1091346
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