This present paper explores the topic of integration of lot sizing and scheduling to optimize production processes in different manufacturing environments. The goal of this study is to mention the complexities related to managing increased ranges of product while taking into consideration the lot scheduling issue, more precisely in job shop and flow shop systems. By developing and applying advanced mathematical strategies, the research focuses on key objective related to cost reduction, efficiency enhancement and solution robustness by decreasing the lead time. The system types employed include a wide range of production configuration, containing parallel and single machine setups. At the basis of the research are the capacitated lot-sizing problem (CLSP) and the generalized lot-sizing problem (GLSP), which are essential for making the optimization models. These models are directly validated through both qualitative and quantitative analyses, making sure of their robustness and practical applicability. The study’s results are significant, demonstrating strong improvements in key performance metrics. Taking into consideration some of the most notable results, we can see that the implementation the discussed models resulted in 15% reduction in lead times and a 10% of cost savings underlining the practical benefits of the optimization techniques. Moreover, it was noted in one of the papers a high of 20% enhancement in production efficiency, underscoring the effectiveness of the integrated approach. The research, furthermore, go deeper into the limitations of existing lot-sizing and scheduling models. It identifies important issues related to the inability of the current models to accommodate zero lead time and the high complexity arising from the incorporation of binary variables. These challenges are stated through extensive validation methods, including algorithmic robustness tests, simulation as well as industrial application. A substantial portion of this study is related to examining the real-world applications of the models. It demonstrates successful integration and practical application in several industrial sectors, involving flexible manufacturing systems (FMS). These case studies show tangible proof of the model’s ability in improving production efficiency and reduction of operational costs in complex manufacturing environments. Future directions are identified to enhance these models even more and improve their applicability across different industrial sectors. This contains identifying more robust algorithms and adding real-time data analyses to enhance decision-making steps. Additional considerations made to enhance this study also recommends extending the models to accept more complex production scenarios and varying demand patterns. This study also considers the use of batch sizing and lot sizing to find the element that would be the most beneficial to reach a result that is most optimal as each element has had different results on each of the three objectives that were set. The use of both sizing methods enhances the study as it would help with considering multiple factors that could not have been seen in one of them alone. Summing up, this thesis proves to be an essential contribution to the setting of production optimization by offering a comprehensive framework for our topic, integrating lot sizing and scheduling. The results provide important information for both scholar research and practical industrial implementation, clearing the road for more efficient and cost effectiveness manufacturing processes. This work not only show theoretical concept but also underlines strategies for industry experts focusing on optimizing their production operations.
Questo documento esplora il tema dell'integrazione del dimensionamento dei lotti e della programmazione per ottimizzare i processi produttivi in diversi ambienti manifatturieri. L'obiettivo di questo studio è menzionare le complessità legate alla gestione di una gamma aumentata di prodotti, tenendo in considerazione il problema della programmazione dei lotti, più precisamente nei sistemi job shop e flow shop. Sviluppando e applicando strategie matematiche avanzate, la ricerca si concentra su obiettivi chiave relativi alla riduzione dei costi, al miglioramento dell'efficienza e alla robustezza delle soluzioni, riducendo i tempi di consegna. I tipi di sistemi impiegati includono una vasta gamma di configurazioni produttive, comprendenti configurazioni a macchina singola e parallela. Alla base della ricerca ci sono il problema del dimensionamento dei lotti con capacità (CLSP) e il problema generalizzato del dimensionamento dei lotti (GLSP), essenziali per la creazione dei modelli di ottimizzazione. Questi modelli sono direttamente validati attraverso analisi qualitative e quantitative, garantendo la loro robustezza e applicabilità pratica. I risultati dello studio sono significativi, dimostrando forti miglioramenti nei principali indicatori di performance. Considerando alcuni dei risultati più notevoli, si può osservare che l'implementazione dei modelli discussi ha portato a una riduzione dei tempi di consegna del 15% e a un risparmio dei costi del 10%, sottolineando i benefici pratici delle tecniche di ottimizzazione. Inoltre, è stato notato in uno degli studi un miglioramento del 20% dell'efficienza produttiva, evidenziando l'efficacia dell'approccio integrato. La ricerca, inoltre, approfondisce le limitazioni dei modelli esistenti di dimensionamento e programmazione dei lotti. Identifica importanti problematiche legate all'incapacità dei modelli attuali di gestire tempi di consegna pari a zero e all'elevata complessità derivante dall'incorporazione di variabili binarie. Queste sfide sono affrontate attraverso metodi di validazione estensivi, tra cui test di robustezza algoritmica, simulazione e applicazione industriale. Una parte sostanziale di questo studio riguarda l'esame delle applicazioni reali dei modelli. Dimostra l'integrazione e l'applicazione pratica di successo in diversi settori industriali, coinvolgendo sistemi di produzione flessibili. Questi studi di caso mostrano prove tangibili della capacità del modello di migliorare l'efficienza produttiva e ridurre i costi operativi in ambienti manifatturieri complessi. Le direzioni future sono identificate per migliorare ulteriormente questi modelli e aumentare la loro applicabilità in diversi settori industriali. Ciò include l'identificazione di algoritmi più robusti e l'integrazione di analisi dei dati in tempo reale per migliorare i processi decisionali. Ulteriori considerazioni fatte per migliorare questo studio raccomandano anche di estendere i modelli per accettare scenari produttivi più complessi e modelli di domanda variabili. Questo studio considera inoltre l'uso del dimensionamento dei lotti e del batch per trovare l'elemento che sarebbe più vantaggioso per raggiungere un risultato ottimale, poiché ciascun elemento ha avuto risultati diversi sui tre obiettivi prefissati. L'uso di entrambi i metodi di dimensionamento arricchisce lo studio, poiché aiuta a considerare molteplici fattori che non sarebbero stati visibili utilizzandone solo uno. In sintesi, questa tesi si dimostra un contributo essenziale all'ottimizzazione della produzione, offrendo un quadro completo per il nostro tema, integrando il dimensionamento e la programmazione dei lotti. I risultati forniscono informazioni importanti sia per la ricerca accademica che per l'implementazione pratica industriale, aprendo la strada a processi produttivi più efficienti e convenienti. Questo lavoro non solo mostra concetti teorici, ma sottolinea anche strategie per esperti del settore concentrati sull'ottimizzazione delle operazioni produttive.
Review of integrated lot sizing and scheduling: a focus on model and applications
IBRAHIM, HASAN;FARHAT, MOHAMAD
2023/2024
Abstract
This present paper explores the topic of integration of lot sizing and scheduling to optimize production processes in different manufacturing environments. The goal of this study is to mention the complexities related to managing increased ranges of product while taking into consideration the lot scheduling issue, more precisely in job shop and flow shop systems. By developing and applying advanced mathematical strategies, the research focuses on key objective related to cost reduction, efficiency enhancement and solution robustness by decreasing the lead time. The system types employed include a wide range of production configuration, containing parallel and single machine setups. At the basis of the research are the capacitated lot-sizing problem (CLSP) and the generalized lot-sizing problem (GLSP), which are essential for making the optimization models. These models are directly validated through both qualitative and quantitative analyses, making sure of their robustness and practical applicability. The study’s results are significant, demonstrating strong improvements in key performance metrics. Taking into consideration some of the most notable results, we can see that the implementation the discussed models resulted in 15% reduction in lead times and a 10% of cost savings underlining the practical benefits of the optimization techniques. Moreover, it was noted in one of the papers a high of 20% enhancement in production efficiency, underscoring the effectiveness of the integrated approach. The research, furthermore, go deeper into the limitations of existing lot-sizing and scheduling models. It identifies important issues related to the inability of the current models to accommodate zero lead time and the high complexity arising from the incorporation of binary variables. These challenges are stated through extensive validation methods, including algorithmic robustness tests, simulation as well as industrial application. A substantial portion of this study is related to examining the real-world applications of the models. It demonstrates successful integration and practical application in several industrial sectors, involving flexible manufacturing systems (FMS). These case studies show tangible proof of the model’s ability in improving production efficiency and reduction of operational costs in complex manufacturing environments. Future directions are identified to enhance these models even more and improve their applicability across different industrial sectors. This contains identifying more robust algorithms and adding real-time data analyses to enhance decision-making steps. Additional considerations made to enhance this study also recommends extending the models to accept more complex production scenarios and varying demand patterns. This study also considers the use of batch sizing and lot sizing to find the element that would be the most beneficial to reach a result that is most optimal as each element has had different results on each of the three objectives that were set. The use of both sizing methods enhances the study as it would help with considering multiple factors that could not have been seen in one of them alone. Summing up, this thesis proves to be an essential contribution to the setting of production optimization by offering a comprehensive framework for our topic, integrating lot sizing and scheduling. The results provide important information for both scholar research and practical industrial implementation, clearing the road for more efficient and cost effectiveness manufacturing processes. This work not only show theoretical concept but also underlines strategies for industry experts focusing on optimizing their production operations.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/226956