The increasing complexity in manufacturing systems due to mass-customization and part varieties demands an extraordinary effort for material flow in factories that only the digitalization can enable. The complexity is reflected also in decisional levels and a data-driven quantitative perspective gains momentum. In Logistics 4.0, real-time information becomes a critical asset for the implementation of effective IoT and CPS solutions. Among these applications, real-time data-driven task assignment emerges as an innovative baseline in factory logistics. In part feeding systems, the inter-connection of shop floor entities – vehicles, stations, materials – benefits the most by the introduction of IoT and CPS technologies. In this thesis, a data-driven task assignment approach applied to the part feeding of a mixed-model assembly line is presented. Within this system, autonomous mobile robots (AMR) fulfill replenishment requests, serving assembly stations with materials, stored initially in supermarkets near the line. The objective of the work is to quantify the impacts of different data-driven task assignment methodologies to create schedules of pick-up and drop-off requests to AMR. Data-driven approaches differ in the availability, type and amount of real-time information. An agent-based simulation model has been developed with four system configurations, with static or dynamic decision systems, working with offline or real-time data. The model has been validated with a real case from the automotive industry. A computational study has been conducted to compare system performances (i.e. idle times of stations, task completion times, inventory levels at the line) between the four configurations to quantify the benefits of increasing data-driven levels. Variations in the number of robots and the space occupied at the line by the inventories are studied in order to provide insights on the trade-off between these two factors, considering the effects of the enhanced synchronization brought by investments in connection and digitalization.
La crescente complessità dovuta a fenomeni come la customizzazione di massa e grande varietà dei componenti richiede oggi uno sforzo senza precedenti al controllo le flusso di materiale nei sistemi manifatturieri che solo la digitalizzazione può rendere possibile. La complessità si riflette a livello decisionale e per questo sta riscontrando successo la gestione orientata ai dati e intrinsecamente quantitativa. Nell’ambito della Logistica 4.0, le informazioni in tempo reale sono diventate un asset critico per l’implementazione di soluzioni IoT e CPS efficaci. Tra queste applicazioni, l’assegnazione di istruzioni guidate da dati in tempo reale emerge come una soluzione innovativa nella logistica di fabbrica. Con l’introduzione di tecnologie IoT e CPS che prevedono l’interconnessione di entità nella fabbrica come veicoli, stazioni, materiali, i sistemi di asservimento di materiali possono ricevere un grande beneficio. In questa tesi è affrontato il problema dell’assegnazione di rifornimenti di materiale basata su dati in tempo reale in una linea di assemblaggio mixed-model. In questo sistema veicoli del tipo autonomus mobile robots (AMR) svolgono richieste di movimentazione di componenti, inizialmente situati in zone decentralizzate di stoccaggio. L’obiettivo dello studio è quantificare gli impatti di differenti logiche di assegnazione di istruzioni ai robot. Queste logiche si contraddistinguono per la tipologia, quantità e sincronizzazione dei dati in tempo reale. Per rispondere all’obiettivo della ricerca è stato sviluppato un modello ad agenti con quattro configurazioni, che lavorano con sistemi decisionali statici o dinamici e con dati non aggiornati o in tempo reale. Il modello è stato validato con dati di un caso reale, riferito all’industria automobilistica. Le performance del sistema (tempi di blocco delle stazioni, tempi di rifornimento dei robot, livello di stock alla linea) sono confrontate tra le diverse configurazioni per quantificare i benefici del crescente utilizzo di dati. Successivamente è stato studiato il rapporto tra il numero di robot e lo spazio occupato dallo stock alla linea al variare del livello di utilizzo di dati nella logica di assegnazione per fornire una comprensione dell’impatto dell’investimento in connessione delle risorse su questo trade-off.
A real-time data-driven approach for the dynamic control of mobile robots in part feeding to assembly lines : assessment through agent-based simulation
Dall'Oglio, Martina
2019/2020
Abstract
The increasing complexity in manufacturing systems due to mass-customization and part varieties demands an extraordinary effort for material flow in factories that only the digitalization can enable. The complexity is reflected also in decisional levels and a data-driven quantitative perspective gains momentum. In Logistics 4.0, real-time information becomes a critical asset for the implementation of effective IoT and CPS solutions. Among these applications, real-time data-driven task assignment emerges as an innovative baseline in factory logistics. In part feeding systems, the inter-connection of shop floor entities – vehicles, stations, materials – benefits the most by the introduction of IoT and CPS technologies. In this thesis, a data-driven task assignment approach applied to the part feeding of a mixed-model assembly line is presented. Within this system, autonomous mobile robots (AMR) fulfill replenishment requests, serving assembly stations with materials, stored initially in supermarkets near the line. The objective of the work is to quantify the impacts of different data-driven task assignment methodologies to create schedules of pick-up and drop-off requests to AMR. Data-driven approaches differ in the availability, type and amount of real-time information. An agent-based simulation model has been developed with four system configurations, with static or dynamic decision systems, working with offline or real-time data. The model has been validated with a real case from the automotive industry. A computational study has been conducted to compare system performances (i.e. idle times of stations, task completion times, inventory levels at the line) between the four configurations to quantify the benefits of increasing data-driven levels. Variations in the number of robots and the space occupied at the line by the inventories are studied in order to provide insights on the trade-off between these two factors, considering the effects of the enhanced synchronization brought by investments in connection and digitalization.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/170263