The present work is meant to show the effective capability of optimizing an unbalanced Paired-Cell Overlapping Loops of Cards with Authorization (POLCA)-controlled production system by means of a heuristic algorithm. This objective is suggested by the fact that one of the most significant issues when using card-driven production control systems is represented by the optimized setting of the large number of cards within the control loops. This is particularly true in the case of unbalanced systems, where the number of cards may vary significantly among the different loops. Little law is usually adopted in literature to infer this number from historical data, but the obtained number is usually far from the optimum. Indeed, in real-world applications, the systems to be controlled are designed to process units with very different routings, each with different probability to occur. In all these situations, they result particularly difficult to set correctly. To this aim, in the present work a Genetic Algorithm is used. The objective is that of finding the correct number of cards and to reduce the overall Total Throughput Time and the average Work In Process. The proposed approach may provide a valid support tool to overcome these limitations, making the most of POLCA capabilities in many manufacturing configurations.

Optimization of POLCA controlled production systems with a simulation-driven Genetic Algorithm

BRAGLIA, MARCELLO;CASTELLANO, DAVIDE;FROSOLINI, MARCO
2014

Abstract

The present work is meant to show the effective capability of optimizing an unbalanced Paired-Cell Overlapping Loops of Cards with Authorization (POLCA)-controlled production system by means of a heuristic algorithm. This objective is suggested by the fact that one of the most significant issues when using card-driven production control systems is represented by the optimized setting of the large number of cards within the control loops. This is particularly true in the case of unbalanced systems, where the number of cards may vary significantly among the different loops. Little law is usually adopted in literature to infer this number from historical data, but the obtained number is usually far from the optimum. Indeed, in real-world applications, the systems to be controlled are designed to process units with very different routings, each with different probability to occur. In all these situations, they result particularly difficult to set correctly. To this aim, in the present work a Genetic Algorithm is used. The objective is that of finding the correct number of cards and to reduce the overall Total Throughput Time and the average Work In Process. The proposed approach may provide a valid support tool to overcome these limitations, making the most of POLCA capabilities in many manufacturing configurations.
Braglia, Marcello; Castellano, Davide; Frosolini, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/242373
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