This thesis addresses two research areas: scalable distributed ledgers for micro-transactions, and the automation of assembly planning in manufacturing industries. Established blockchain solutions are robust and reliable. Being distributed and decentralized, they avoid a single point of failure, and fault-tolerant consensus mechanisms ensure that the system works as intended even when some participants are faulty or malicious. However, their main weakness is scalability. The two most popular and well-known blockchain solutions, Bitcoin and Ethereum, require all nodes to store all transactions, and their transaction throughput is far too low to compete with traditional, centralized transaction processing systems. To improve scalability, systems have been developed that split the network nodes into groups that can process transactions in parallel, a technique known as sharding. We propose a sharded system called ScaleGraph that uses a novel architecture with one transaction per block and one shard per account, designed to maximize parallelism. The design is inspired by concepts from distributed hash tables, particularly to define shards based on a logical distance metric for node IDs and account IDs. Nodes store and process only transactions involving those accounts that are close to the node according to the distance metric. This greatly reduces the storage burden on each node and allows any number of transactions involving distinct accounts to be validated in parallel. We also design a new cross-shard transaction commit protocol for this architecture. The protocol offers global serializability and inevitable atomic commit, without the need for an abort path. This is achieved using only shard-local consensus and certificate exchange, rather than global or joint cross-shard consensus. Manufacturing is a highly complex process in many industries and involves many different planning problems where increasing automation has the potential to make manufacturing more efficient. This thesis presents a proof-of-concept solution to the kitting layout problem, where a list of parts has to be placed on a kitting wagon for delivery to an assembly line station. However, some problems have proven difficult to automate in practice, despite decades of research. One such problem, assembly line balancing, is analyzed in depth. We identify fundamental challenges that make the goal of complete automation implausible in some industries, such as automotive manufacturing. Human intervention is thus unavoidable, suggesting that bridging the gap between theory and practice requires decision support systems for assisted, iterative, and interactive planning. The thesis also includes preliminary work on the product sequencing problem, limited to framing the use case, assumptions, and requirements. Subsequent ongoing work suggests strong parallels to assembly line balancing, indicating that the identified challenges and possibilities for addressing them reflect a broader pattern in industrial planning automation.
Rong Zhao, Hong Kang, Qiyue Zhang, Sizheng Fan · 5 authors
Blockchain technologies, particularly blockchain-based digital twins (DTs), have gained widespread interest in academia and industry. Despite this, existing literature frequently overlooks the unique characteristics of location information for consensus nodes in DTs, often merging them with conventional blockchain frameworks. This article introduces LocPoS, a novel location-based proof-of-stake mechanism tailored for industrial DTs, bridging this gap. Our proposed model strives to enhance security via broader consensus node distribution. Despite this enhanced security, potential for dishonest behavior by users for gain maximization persists. We perform an in-depth analysis of user strategies and devise a mechanism to inhibit malicious behavior, fostering truthfulness among all users. Our theoretical and simulation results demonstrate that LocPoS satisfies truthfulness and individual rationality, while reducing the proportion of malicious nodes in the consensus group by over 50% compared to PoS and DPoS, thereby significantly improving consensus integrity. The proposed mechanism’s effectiveness in ensuring truthful behavior and secure node selection is validated through both analytical proofs and extensive simulations.
Mikhail Kartavchenko, Maksim Izmaylov, Alexander Kartavchenko, Nikita Svirskii · 9 authors
The Stablecoin Trilemma—balancing stability, decentralization, and capital efficiency—remains a significant challenge in the decentralized finance (DeFi) landscape. This study introduces an innovative stablecoin system, USC, designed to overcome the inherent limitations of existing models by simultaneously achieving stability, decentralization, and capital efficiency. The USC system leverages a combination of decentralized assets, including the hypothetical native token CAPE and ETH, integrated through key components such as the Treasury, Treasury Credit Module, CAPE Bonder, and Merchant. A comprehensive Python-based simulation spanning 298 days was conducted to evaluate the system’s performance under varying market conditions. ETH price movements were meticulously modeled using historical data from the Binance/USDT trading pair, with specific periods of daily growth and decline to test the system’s resilience. Additionally, CAPE price determination was simulated using a hypothetical liquidity pool. The simulation results demonstrated that the USC system effectively maintained the usc_support metric above the critical threshold of 1 throughout the study period, indicating that each USC remained fully backed by the underlying assets. The Treasury Credit Module played a crucial role by offering a stable 10% Annual Percentage Rate (APR), incentivizing participants to lock their USC and thereby reducing the circulating supply during asset depreciation periods. Furthermore, the Merchant component provided a substantial safety net with a borrowing capacity of up to 67,315 ETH (approximately $160.6 million at the simulation’s final ETH price), which is nearly three and a half times the Treasury’s holdings. This extensive liquidity provision enhanced the system’s ability to maintain stability during significant market downturns. However, the study acknowledges several key limitations, including the lack of realistic participant behavior modeling, the absence of external shocks such as regulatory changes or technological failures, and the lack of empirical validation through real-world testing. These limitations highlight the need for further research and development to address behavioral complexities, incorporate unforeseen variables, and validate the system’s performance in live environments. In conclusion, the USC stablecoin system presents a promising framework for resolving the Stablecoin Trilemma by integrating decentralized assets and innovative financial mechanisms. While the simulation results are encouraging, demonstrating robust stability, the system’s practical implementation will require overcoming significant technical, regulatory, and operational challenges.
The International Journal of Computer Engineering in Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
Elisa Negri, Roberto La Rocca, Roman Felipe Bastidas Santacruz, Luca Fumagalli
Among the different smart technologies with the highest digitalisation capacity for manufacturing environments, distributed ledger technologies (DLT) has the potential to support one of the main challenges to be faced in the new environment created by the Industry 4.0 paradigm, i.e., the interchange of data through the supply chain. DLT enhances data immutability and transparency and offers new possibilities of interactions and business models to the new industrial networks, allowing a trustable horizontal and vertical data flow between different organisations. Although these possible improvements are in sight, the approach and characteristics that can make the technology suitable with the I4.0 paradigm, are not yet well-defined, and in some cases puzzling for industry managers. In this paper, possible applications, features of DLTs and its available types are reviewed and analysed, giving a clear definition of the most relevant characteristics that these technologies can offer to manufacturing industry.
The planning and control of intralogistics systems in line with versatile production systems of smart factories requires new approaches and methods to cope with changing requirements within future factories. The planning of intralogistics can no longer follow a static, sequential approach as in the past since the planning assumptions are going to change in a high frequency. Reasons for these constant changes are amongst others external turbulences like rapidly changing market conditions, decreasing batch sizes down to customer-specific products with a batch size of one and on the other hand internal turbulences (like production and logistic resource breakdowns) affecting the production system. This paper gives an insight into research approaches and results how capabilities of intelligent logistical objects (intelligent bins, autonomous transport systems etc.) can be used to achieve a self-organized, cost and performance optimized intralogistics system with autonomously controlled process execution within versatile production environments. A first consistent method has been developed which has been validated and implemented within a scenario at the pilot factory Werk150 at the ESB Business School (Reutlingen University). Based on the incoming production orders, the method of the Extended Profitability Appraisal (EPA) covering the work system value to define the most effective work system for order fulfilment is applied. To derive the appropriate intralogistics processes, an autonomous control method involving principles of decentralized and target-oriented decision-making (e.g. intelligent bins are interacting with autonomously controlled transport systems to fulfil material orders of assembly workstations) has been developed and applied to achieve a target-optimized process execution. The results of the first stage research using predefined material sources and sinks described in this paper is going to set the basis for the further development of a self-organized and autonomously controlled method for intralogistics systems considering dynamic source and sink relations. By allowing dynamic shifts of production orders in the sense of dynamic source and sink relations the cost and performance aims of the intralogistics system can be directly aligned with the aims of the entire versatile production system in the sense of self-organized and autonomously controlled systems.
Muhammad Hafidz Fazli Md Fauadi, Fairul Azni Jafar, Adi Saptari, Wan Ling Li
The increasingly challenging and dynamic nature of manufacturing industry has driven organizations to enhance their system efficiency. One of the most important aspects in manufacturing plant is the Material Transportation System (MTS). In order to address dynamic factors, MTS need to be equipped with rescheduling capability. This paper focuses on reactive transportation task assignment to a fleet of autonomous Automated Guided Vehicles (AGVs). This paper proposes a Mixed Integer Programming method to reschedule transportation tasks for a non-disruptive job shop manufacturing system based on Multi Agent System (MAS) architecture. The result shows that proposed rescheduling method is able to outperform conventional method significantly.
In a world of rapid change, production companies are faced with major challenges. They have to deal quickly to an increase in competition and individual customer requests. New technologies and organizational structures like RFID or decentralized and real-time based production control can handle these challenges. The analysis of a production process and the selection of appropriate technical solutions often proves to be difficult and cost intensive. Previous simulation approaches are frequently combined with high effort in order to state credible propositions regarding suitability and economic efficiency. This paper presents a new opportunity towards overcoming these disadvantages. The approaches of computer-based simulation and model factories are combined into a new simulation approach with reduced effort. For the purpose of documentation of simulated processes the Value Stream Design method is extended towards the requirements resulting from autonomous production control. For this purpose additional symbolism, data dictionaries and key figures are introduced.