The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on âcloud-edge-endâ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
TomaĆŸ Berlec, Marko Corn, Sergej Varljen, PrimoĆŸ PodrĆŸaj
The Fourth Industrial Revolution has introduced âshared manufacturingâ as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems.
We study the optimal channel selection and blockchain strategy of a capital-constrained manufacturer who sells the green product through an online platform. Blockchain technology (BCT) can be used by the manufacturer to increase consumer confidence in the value of green product. We first show that the manufacturerâs financing strategy is characterized by two thresholds that depend on the manufacturerâs channel selection and blockchain strategy. BCT increases wholesale and retail prices, but it does not always increase product greenness and demand. We find that the manufacturerâs ability to bear the blockchain cost correlates with his financial standing as well as the platformâs operation mode. Furthermore, we show that the agency mode should be selected by the manufacturer only when the commission rate falls below a specified threshold. We also examine the impact of commission rate on the platformâs operational strategy and channel consistency. Furthermore, under the agency model, we show that there is always a âwin-winâ situation in green cost-sharing cooperation regardless of the manufacturerâs financial state. However, such a âwin-winâ situation only exists in financing circumstances under the reselling mode. Finally, the extended model explores the relationship between channel strategies and risk preferences of the manufacturer under demand uncertainty.
Focusing on the dual-channel supply chain system consisting of capital-constrained manufacturers, banks, retailers and third-party platforms, considering the dual roles of retailers and third-party platforms as channel participants and loan providers, the optimal financing strategies of capital-constrained manufacturers under centralized decision-making and decentralized decision-making are studied respectively, and the influence of different initial capital levels on their financing strategy selection is explored. The results show that under centralized decision-making, the third-party platform financing strategy is always better than the bank financing strategy; under the condition of equal financing interest rates, if the initial capital level allows both financing strategies to be selected, the third-party platform financing strategy can more effectively solve the capital constraint problem than the bank financing strategy. Under decentralized decision-making, with the intensification of channel competition, the reduction of revenue sharing rate or production cost, or the increase of initial capital, manufacturers will tend to choose the retailer financing strategy; under the condition of equal financing interest rates, if the initial capital level allows all three financing strategies to be selected, the retailer financing strategy can more effectively solve the manufacturer's capital constraint problem than the other two financing strategies.
The introduction of non-fungible tokens (NFTs) has revolutionised digital ownership and asset management in the quickly changing environment of blockchain technology. NFTs are a kind of token that cannot be exchanged for another token. On the other hand, as the market for NFTs continues to grow, customers are becoming more and more interested in interoperability across various blockchain networks. Cross-chain non-fungible token markets have been developed as a result of this necessity. These marketplaces make it possible for different blockchain ecosystems to engage in transactions and interactions with one another. LayerZero and Chainlink are two technologies that are very significant in addressing the difficulty of interoperability across different chains.
Kushal Babel, Nerla Jean-Louis, Yan Ji, Ujval Misra · 8 authors
Users of decentralized finance (DeFi) applications face significant risks from adversarial actions that manipulate the order of transactions to extract value from users. Such actions -- an adversarial form of what is called maximal-extractable value (MEV) -- impact both individual outcomes and the stability of the DeFi ecosystem. MEV exploitation, moreover, is being institutionalized through an architectural paradigm known Proposer-Builder Separation (PBS). This work introduces a system called PROF (PRotected Order Flow) that is designed to limit harmful forms of MEV in existing PBS systems. PROF aims at this goal using two ideas. First, PROF imposes an ordering on a set ("bundle") of privately input transactions and enforces that ordering all the way through to block production -- preventing transaction-order manipulation. Second, PROF creates bundles whose inclusion is profitable to block producers, thereby ensuring that bundles see timely inclusion in blocks. PROF is backward-compatible, meaning that it works with existing and future PBS designs. PROF is also compatible with any desired algorithm for ordering transactions within a PROF bundle (e.g., first-come, first-serve, fee-based, etc.). It executes efficiently, i.e., with low latency, and requires no additional trust assumptions among PBS entities. We quantitatively and qualitatively analyze incentive structure of PROF, and its utility to users compared with existing solutions. We also report on inclusion likelihood of PROF transactions, and concrete latency numbers through our end-to-end implementation.
Hulin Yang, Mingzhe Li, Jin Zhang, Alia Asheralieva · 6 authors
The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leverages Bribery-driven attacks to Delay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited (< 1/4) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.66 ETH (16,442.23 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.
Yifan Mao, Mengya Zhang, Shaileshh Bojja Venkatakrishnan, Zhiqiang Lin
Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.
Executing smart contracts is a compute and storage-intensive task, which currently dominates modern blockchain's performance. Given that computers are becoming increasingly multicore, concurrency is an attractive approach to improve programs' execution runtime. A unique challenge of blockchains is that all replicas (miners or validators) must execute all smart contracts in the same logical order to maintain the semantics of State Machine Replication (SMR). In this work, we study the maximal level of parallelism attainable when focusing on the conflict graph between transactions packaged in the same block. This exposes a performance vulnerability that block creators may exploit against existing blockchain concurrency solutions, which rely on a total ordering phase for maintaining consistency amongst all replicas. To facilitate the formal aspects of our study, we develop a novel generic framework for Active State Machine Replication (ASMR) that is strictly serializable. We introduce the concept of graph scheduling and the definition of the minimal latency scheduling problem, which we prove to be NP-hard. We show that the restricted version of this problem for homogeneous transactions is equivalent to the classic Graph Vertex Coloring Problem, yet show that the heterogeneous case is more complex. We discuss the practical implications of these results.
Automated Market Makers (AMMs) are an integral component of the decentralized finance (DeFi) ecosystem, as they allow users to exchange crypto-assets without the need for trusted authorities or external price oracles. Although these protocols are based on relatively simple mechanisms, e.g., to algorithmically determine the exchange rate between crypto-assets, they give rise to complex economic behaviours. This complexity is witnessed by the proliferation of models that study their structural and economic properties. Currently, most of theoretical results obtained on these models are supported by pen-and-paper proofs. This work proposes a formalization of constant-product AMMs in the Lean 4 Theorem Prover. To demonstrate the utility of our model, we provide mechanized proofs of key economic properties like arbitrage, that at the best of our knowledge have only been proved by pen-and-paper before.
Byzantine fault-tolerant consensus protocols have provable safety and liveness properties for static validator sets. In practice, however, the validator set changes over time, potentially eroding the protocol's security guarantees. For example, systems with accountable safety may lose some of that accountability over time as adversarial validators exit. As a result, protocols must rate limit entry and exit so that the set changes slowly enough to ensure security. Here, the system designer faces a fundamental trade-off. Slower exits increase friction, making it less attractive to stake in the first place. Faster exits provide more utility to stakers but weaken the protocol's security. This paper provides the first systematic study of exit queues for Proof-of-Stake blockchains. Given a collection of validator-set consistency constraints imposed by the protocol, the social planner's goal is to provide a constrained-optimal mechanism that minimizes disutility for the participants. We introduce the MINSLACK mechanism, a dynamic capacity first-come-first-served queue in which the amount of stake that can exit in a period depends on the number of previous exits and the consistency constraints. We show that MINSLACK is optimal when stakers equally value the processing of their withdrawal. When stakers values are heterogeneous, the optimal mechanism resembles a priority queue with dynamic capacity. However, this mechanism must reserve exit capacity for the future in case a staker with a much higher need for liquidity arrives. We conclude with a survey of known consistency constraints and highlight the diversity of existing exit mechanisms.
Our Pharmaceutical Supply chain systems using smart contracts can have wide range of applications across the pharmaceuticals industry.Smart contracts are self-executing agreements with the terms of the agreement directly written into the code.They can be use to automate the process of supply chain management and reduce costs, increase transparency and account ability, and improve patient safety.
Existing blockchain system face scalability issues when processing massive amounts of data.These issues primarily arise due to their consensus based block generation methods.Sharding has emerged as a promising on chain solution to enhance the scalability of blockchain.This technology increases throughput by dividing the main network into several sub-networks, called shards, which can process transactions in parallel.However, implementing sharding in blockchain system presents two significant challenges: Cross shard transactions and load imbalance between different shards.Cross shard transaction refers to transactions generated between accounts belonging to different shards.Load imbalance occurs when specifical one shard processes a disproportionately higher transaction load than others.These challenges can lead to increased network delay, confirmation time, latency, and fees due to complicated inter-shard communication, thereby reducing blockchain throughput.To address these challenges, this paper proposes an innovative account relocation scheme.This scheme aims to optimize load balancing in blockchain sharding using a round robin algorithm.To validate the effectiveness of our approach, we utilized a simulator that incorporates real Ethereum data.We then compared the degree of load balancing achieved by our method against existing methods, such as schemes that use no-relocation and random relocation.Our results indicate a significant improvement in load balancing performance compared to previous approaches.
Amal Abdulbaqi Maryoosh, Ahmed Abd Ali Abdulkadhim, Muntaha AbdulzahraHatem
Block chain is a technology for securely storing data in a manner that makes system changes or hacks impossible or difficult. Numerous organizations have adopted this technology, which utilizes a peer-to-peer distributed network. Using a decentralized peer-to-peer network to operate. A block chain is a collection of linked computer systems that operate as a distributed ledger of transactions. Every new transaction that happens on the block chain is copied to each participant's ledger, and each block on the chain is made up of numerous transactions. This paper aims to explain block chain technology, including its uses and difficulties.
Abstract Coin selection method refers to the process undergone when selecting a set of unspent transaction outputs (UTXOs) from a cryptocurrency wallet or account to use as inputs in each transaction. The most applied coin selection method that UTXO-based cryptocurrencies currently employ is an algorithm that decides on a certain set of UTXOs that matches the target amount and limits the transaction fee. However this approach trades off favourable maintenance overhead of the entire network for low transaction fees, as many low-value UTXOs known as âdustâ is produced. Over time, this will impact the scalability and management of the cryptocurrency network as the global set of UTXOs become larger. Therefore, there is an urgency to find a higher-performing coin selection method suitable for UTXO-based cryptocurrencies. This paper proposes a method based on the greedy and genetic algorithm for effectively choosing sets of UTXOs in Bitcoin. The main objective of this coin selection strategy is to get as close as possible to the target while also maintaining and possibly reducing the number of UTXO inputs.
Ankit Bansal, Osman Y. Ăzaltın, Reha Uzsoy, Karl G. Kempf
Abstract Product transitions involve the replacement of products currently being produced and distributed by a firm with new products throughout the firm's supply chain. In high technology industries effective management of product transitions is crucial to longâterm success, and involves the coordination of multiple product development units and a manufacturing unit by a product division serving a particular market. Since the different units are organizationally autonomous, and the product division does not have access to their detailed technological constraints and internal operating policies, a decentralized solution is required. We develop a priceâbased coordination framework using the subadditive dual of a mixedâinteger linear program that seeks to maximize the number of units whose proposed plans are included in the final solution. The proposed approach yields superior solutions to a linearâprogrammingâbased branchâandâprice approach within the same computing budget. We discuss the broader applicability of this integer column generation approach, and suggest directions for future work.
The Line-less Mobile Assembly System paradigm (short LMAS) provides necessitated flexibility, especially for large-scale products as customer demands for individualized products persist and product life-cycles remain short. To make LMAS advantages operationally usable in an industrial context, it requires a suitable control system to connect multi-purpose assembly resources and to autonomously configure transient assembly stations. Therefore this paper reviews the relevant literature to identify the necessary components of such architectures. Depending on the control systemâs intention and use cases, the properties of the organizational paradigm, an adequate ontology and basic design patterns regarding the distribution and order-relationships of the system entities are to be defined conceptually. For the implementation, a role model, an interaction model, and a data model are required. Due to the utilization of mobile multipurpose resources and the possibility of factory shopfloor reconfiguration by transient stations, existing approaches do not meet LMAS inherent properties. Consequently, we present a suitable decentralized multi-agent control system approach.
We review the recent developments in dynamic inventory models with financial flow considerations. The focus is on the literature that introduces cash flow dynamics into the classic inventory models that do not explicitly consider the interactions between physical (or material) and financial flows. These augmented models serve two important purposes. First, they help understand the impact of financial flows on inventory dynamics and decisions. Second, with the connection to the classic inventory models, one can leverage the extant results to derive the optimal control policy or to evaluate/optimize the performance of any given type of policy and reveal insights. We summarize models for both single-stage and multi-stage inventory systems, and discuss the implications and applications to decentralized systems within a broader topic of supply chain finance.