Industrial Engineering and Management
This thesis investigates the integration of sustainability assessment considering Industry 4.0 technologies and the use of Markov Decision Process capabilities. The manufacturing industry is facing increasing pressure to improve sustainability assessment performance, and Industry 4.0 technologies like Digital Twins, Internet of Things, Big Data Analytics, Cloud Computing, Machine Learning, and Artificial Intelligence have the potential to support these efforts. However, effectively integrating sustainability assessment goals and Industry 4.0 technologies within manufacturing systems can be challenging. The research addresses this challenge by developing a framework for optimizing the flow of operations in a manufacturing system while incorporating sustainability assessment and Industry 4.0 technologies effectively. The framework utilizes the Markov Decision Process to model the decision-making process of the manufacturing system and its decision-makers. From the other side, it includes sustainability assessment goals as constraints or objectives in the Markov Decision Process model. The use of Industry 4.0 technologies is integrated into the framework to gather data and optimize the decision-making process based on that data. The thesis begins by reviewing the literature on sustainability assessment, Industry 4.0 technologies, and their impacts with regard to manufacturing systems. The proposed framework is then presented, and its capabilities are demonstrated through case studies of single and multiple agents on a shop floor. The trend in pioneer manufacturing firms is to implement new technological applications on their shop floor to agile their Manufacturing Execution System. The findings from the case study indicate that the proposed framework can effectively support decision-making at the top-tier level of the enterprise by integrating sustainability assessment and the Industry 4.0 paradigm.
Energy management and sustainability have become critical global priorities in response to growing environmental concerns and the need to optimize resource consumption. As industries expand and technological advancements continue to shape modern societies, energy demands are rapidly rising. This leads to escalating levels of carbon emissions and resource depletion, negatively impacting the environment and the economy.
The thesis proposes an innovative approach to addressing energy management and sustainability complexities. The research focuses on developing a framework for smart energy systems that can autonomously improve their performance through knowledge sharing and semantic interoperability. The core idea behind this thesis is to enable smart energy systems to self-develop their knowledge models through decentralized technologies, particularly blockchain while ensuring peer-to-peer semantic interaction and collaboration across different environments and supply chains.
A key innovation of this thesis is the use of blockchain technology as the underlying platform for achieving semantic interoperability and knowledge exchange among smart systems. By leveraging blockchain’s decentralized nature, a peer-to-peer semantic interaction framework is established. The research introduces a smart contract mechanism and a token-based economic model to incentivize stakeholders within the blockchain network, ensuring that participants align with the sustainability goals of the network.
The thesis presents a novel approach to storing and exchanging knowledge models on the blockchain using the InterPlanetary File System (IPFS). This enables real-time updates to smart systems' knowledge models, allowing them to adapt and respond dynamically to changing environmental conditions and data inputs. Through the proposed blockchain ecosystem, the research provides a comprehensive solution for enhancing the interoperability, autonomy, and sustainability of smart energy systems.
Steel manufacturing is characterized by its high energy consumption and the production of high-value-added products. In real-world steel production, unforeseen events frequently disrupt schedules, emphasizing the critical need for effective and adaptive planning to ensure continuous operations. This study makes significant contributions to the steel industry by offering new approaches to improve efficiency, streamline operations, and optimize production processes, ultimately driving advancements in steel manufacturing performance.
To address the challenges inherent to EAF-based steelmaking, a seamless pipeline of algorithms has been developed. This pipeline works together to enhance the manufacturing planning process by providing an accurate chemical condensation of molten steel and classifying this outcome according to the most feasible steel grade, which can be obtained with a minimum of purification. Finally, the pipeline reschedules the planning process with the objective of maximizing machine utilization.
To achieve these goals, the steelmaking stage key performance indicators (KPIs) that have the most impact on the quality of the final product were first identified. In the next step, a novel prediction algorithm utilizing a multilayer feedforward neural network was developed to estimate key quality parameters. Finally, to enhance the pipeline's resilience to disruptions, a Genetic Algorithm (GA) is employed to mitigate the impact of scenarios where processing times for jobs vary and do not align with the established schedule. This is a prevalent disruption event that renders the base schedule infeasible. The proposed algorithm aims to minimize total completion and waiting times, thereby enhancing operational efficiency and minimizing manufacturing costs.
The rapid rise of new technologies, such as mobile phones, social networks, and increased internet access, has created new opportunities for retailers to expand through omnichannel strategies, which aim to provide a seamless customer experience across different channels. While omnichannel can offer benefits and competitive advantages, it faces challenges like price inconsistencies and poor information sharing. Despite its growing recognition, its integration with Product Lifecycle Management (PLM) is underexplored.
This thesis investigates the overlap and mutual effects of omnichannel and PLM, particularly emphasizing the importance of data and knowledge sharing between them. As research into data analytics in omnichannel evolves, tracking and tracing mechanisms in PLM become increasingly critical. The main objective of this study is to develop a bi-directional framework connecting omnichannel and PLM using a System Dynamics approach. This framework aims to incorporate omnichannel mechanisms into PLM and provide cause-and-effect analysis to better understand the operational role of omnichannel within PLM.
Additionally, the research introduces an approach to integrate Business-to-Business (B2B) aspects from PLM with Business-to-Customer (B2C) elements through omnichannel strategies. Leveraging omnichannel's influence on touchpoints like influencers and social media, this approach helps companies align consumer behavior with strategic goals, boosting competitiveness. Ultimately, this thesis aims to bridge the gap between omnichannel and PLM, driving a shift in consumer behavior and strengthening the integration of B2B and B2C in retail.
