Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Supply chain control: Trade-offs and system requirements

The official published version can be accessed from the link below.A paper describes the underlying forces which drive change in manufacturing enterprises and supply chains. It sets out the complexities in modern capitalism and global economics and illustrates the trade-offs that can be made. IT systems which are required to assist improvements to both customer service and enterprise manufacturing performance are explained, alluding to the special case for the semiconductor industry. Arguments are presented showing how the new tools being developed with the ESPRIT project 20544, X-CITTIC, will satisfy the control needs for a virtual enterprise. This paper describes the underlying forces which drive change in manufacturing enterprises and supply chains. It sets out the complexities in modem capitalism and global economics and illustrates the trade-offs that can be made. IT systems which are required to assist improvements to both customer service and enterprise manufacturing performance are explained alluding to the special case for the semiconductor industry. Finally it shows how the new tools being developed with the ESPRIT project 20544, XCITTIC, will satisfy the control needs for a virtual enterprise

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Including Generative Mechanisms in Project scheduling using Hybrid Simulation

Scheduling is central to the practice of project management and a topic of significant interest for the operations research and management science academic communities. However, a rigour-relevance gap has developed between the research and practice of scheduling that mirrors similar concerns current in management science. Closing this gap requires a more accommodative philosophy that can integrate both hard and soft factors in the construction of project schedules. This paper outlines one interpretation of how this can be achieved through the combination of discrete event simulation for schedule construction and system dynamics for variable resource productivity. An implementation was built in a readily available modelling environment and its scheduling capabilities tested. They compare well with published results for commercial project scheduling packages. The use of system dynamics in schedule construction allows for the inclusion of generative mechanisms, models that describe the process by which some observed phenomenon is produced. They are powerful tools for answering questions about why things happen the way they do, a type of question very relevant to practic

Modelling and Formulation of Holonic Workforce Allocation to Reduce the Impact of Absenteeism and Turnover

Holonic Manufacturing System (HMS) adopts Arthur Koestler's generalisation on living organisms and social organisations into a novel paradigm suitable for manufacturing industry. While the HMS paradigm has been researched on myriad technical subjects, workforce allocation is rarely attempted. In this research paper, an advisory model called Holonic Workforce Allocation Model (HWM) was developed, with the aim to reduce the impact of absenteeism and turnover in job shop environments. This model is associated with a weighted randomised formulation that can provide cross-training opportunities in parallel with specialisation requirements. For verification purpose, HWM was tested in several computer-simulated scenarios and was compared with some models commonly used in manufacturing. The experimental results showed that HWM is more effective than the others in minimising task overdue rate, improving average skill level, as well as providing moderate workload balance and cross-training chances

Strumenti per la simulazione: dal "Discrete Event Simulation" all’“Agent Based Modeling” - Lo stato dell’arte attraverso lo sviluppo di casi reali e la sperimentazione delle nuove metodologie

2010 - 2011Questa tesi presenta un percorso di attività caratterizzato dall’applicazione della simulazione in ambienti produttivi. Viene prima presentato uno studio di ottimizzazione di un impianto di cartotecnica attraverso simulazione DES in ambiente Digital Factory. L’esperienza maturata nella modellazione DES ha permesso di apprezzare i vantaggi di questo tipo di approccio ma anche di individuare i suoi punti deboli. Dal punto di vista della modellazione, DES propone la costruzione del modello attraverso la combinazione di blocchi logici standard predefiniti che riproducono lo schema di flusso del processo. Questo tipo di approccio si adatta bene alla simulazione di sistemi manifatturieri soprattutto se i fattori umani non sono considerati. Lo studio su un caso reale ha evidenziato l’approfondito livello di dettaglio che questo approccio richiedere. Dal punto di vista dell’utilizzatore, questo rappresenta un vantaggio perché aumenta l’accuratezza e la credibilità dei modelli realizzati e quindi delle soluzioni migliorative proposte. Dall’altro però rappresenta un ostacolo poiché rispetto ad altri approcci, DES è decisamente una metodologia “data hungry”. Un altro aspetto importante riguarda la complessità di utilizzo: sebbene negli ultimi anni, alcune società fanno uso di questi tool, quasi sempre si avvalgono di consulenza esterna perché mancano le risorse specializzate per realizzare questo tipo di studio. Inoltre i costi di training sono elevati così come i costi di sviluppo dei modelli. In particolare, questi ultimi sono causati da lunghi tempi di sviluppo dei modelli sia per la complessità computazionale che per procedure di modellazione lunghe e ripetitive. Un’applicazione basata su ACCESS è stata sviluppata per accelerare la fase di costruzione dei modelli e facilitare l’utilizzo agli utenti meno esperti. I risultati, valutati in termini di tempo di modellazione e numero di operazioni elementari realizzate sono stati comparati con la procedura tradizionale del software DES di QUEST. La verifica è stata effettuata attraverso la costruzione di numerosi modelli di impianti produttivi e infine è stato ricostruito il modello della linea rotoli della cartotecnica Confalone. I risultati hanno evidenziato una riduzione del 50% dei tempi di costruzione del modello evidenziando come è possibile facilitare l’utilizzo di tool DES attraverso questo tipo di applicazione. Infine, la tesi ha presentato uno studio di simulazione basato su metodologia ABM per l’analisi di un sistema DRC configurato come una linea di assemblaggio con layout flow-shop. Regole di assegnazione degli operatori sono state implementare per modellare la cosiddetta “workforce flexibility”. L’approccio di modellazione non convenzionale di ABM, seppur non sviluppato specificatamente per sistema produttivi, ha comunque permesso di realizzare un modello ad agenti per una linea di assemblaggio costituita da 8 stazioni di assemblaggio e buffer intermedi. La complessità di modellare le regole di assegnazione degli operatori è venuta meno grazie alla tipologia di costruzione dei sistemi ad agenti. Nel modello infatti sono stati definiti tre tipologie di agenti: agente “Macchina”, agente “Prodotto” e agente “Operatore”. Gli agenti così definiti e inseriti in un ambiente possono scambiare informazioni tra di loro e mostrare un comportamento che scaturisce da regole definite per ognuno di essi. Una campagna di esperimenti (DOE) e un’analisi ANOVA hanno permesso di valutare i risultati del sistema produttivo in termini di prestazioni del sistema e “human effects”.Il modello ABM sviluppato ha permesso di comparare i risultati ottenuti sul sistema di produzione in configurazione DRC con quelli presenti in letteratura. La metodologia ABM ha inoltre mostrato grandi potenzialità nell’integrazione di fattori umani nei processi di ottimizzazione dei sistemi produttivi superando la complessità di programmazione dell’approccio DES. Future applicazioni proveranno ad implementare fattori di fatica nel modello. [a cura dell'autore]X n.s