Research on digital workshop dynamic scheduling technology based on blockchain

An online task offloading method based on improved starfish optimization and blockchain

The proposed OIBTO framework employs a lightweight Proof-of-Authority consensus within a two-tier architecture consisting of a vehicle layer and an edge layer, and proposes an Improved Starfish Optimization Algorithm (ISFOA) that utilizes chaotic mapping and genetic mutation to optimize offloading decisions and task partitioning ratios, aiming to minimize a priority-weighted combination of latency and energy consumption.

Design and Implementation of a Blockchain-based Traceability System for Green Transformation in Optoelectronic Enterprises

By integrating blockchain technology with data-driven intelligent decision-making, the framework ensures trustworthy supply chain data management and procurement optimization. A multi-channel consortium blockchain architecture enables secure data sharing among suppliers, manufacturers, distributors, and end-users. Smart contracts automate transaction verification and recording, ensuring data authenticity, immutability, and full lifecycle traceability This paper proposes a blockchain-based traceability framework to support the green transformation of optoelectronic enterprises through secure, transparent, and intelligent supply network operations. An on-chain/off-chain collaborative storage mechanism reduces storage overhead while preserving data integrity. Furthermore, a reinforcement learning-based procurement decision model combines historical production data and market intelligence to optimize raw material purchasing strategies, inventory management, and cost control. Experimental results show that traceability query time is reduced to approximately 95 ms, improving efficiency by over 60% compared with traditional approaches. The system achieves a tamper detection rate above 99%, reduces inventory holding costs by about 20%, and lowers total procurement costs by approximately 10%, demonstrating significant benefits for sustainable supply chain management.

A Requirement-Driven Expert System for Blockchain Consensus Mechanism Selection

Blockchain technology has introduced a rapidly expanding range of consensus mechanisms, each designed to satisfy different operational requirements related to security, scalability, decentralization, transaction throughput, and energy efficiency. Selecting an appropriate consensus mechanism has consequently become a complex multi-criteria decision problem, particularly for developers and organizations without extensive expertise in distributed systems and blockchain architectures. Existing tools primarily address protocol benchmarking and static documentation, leaving the decision-support dimension largely unaddressed. This paper presents a web-based expert system designed to support the selection of blockchain consensus mechanisms according to specific user-defined operational requirements. The proposed solution was implemented using the MERN technology stack, consisting of MongoDB, Express.js, React, and Node.js, enabling a modular and scalable architecture suitable for future expansion and maintenance. The recommendation process is based on a two-phase filtering and scoring algorithm. In the first phase, mechanisms incompatible with mandatory user-defined constraints, including network type and key resource type, are systematically eliminated. In the second phase, the remaining mechanisms are ranked using attribute matching across criteria encompassing energy efficiency, scalability, security, decentralization, and transaction speed. The system returns the three most suitable consensus mechanisms for the given operational scenario together with their key characteristics. In addition to recommendation functionality, the application supports user authentication, recommendation history management, and administrative maintenance of the consensus mechanism database, which currently contains 38 distinct blockchain consensus protocols. Experimental evaluation through twelve representative usage scenarios demonstrated that the system consistently produces contextually relevant recommendations aligned with user-specified requirements. A comparative analysis with existing tools confirms that the proposed system occupies a distinct decision-support role currently absent from the available tooling landscape. A comparative analysis against four representative existing tools indicates that the proposed system combines a set of decision-support capabilities not jointly offered by any one of them. The presented approach contributes a transparent and extensible decision-support framework intended to simplify architectural planning and management of blockchain-based distributed systems.

Efficient and Fair Scheduling Schemes for Enabling Priority Awareness in Blockchain Systems

Blockchain-Based Predictive Resource Sharing in NFV Environments with LSTM-Based Demand Forecasting

: Network Function Virtualization (NFV) enables network functions to be implemented as software through flexible deployment capabilities. However, static demand assumptions, centralized security dependencies, and reactive decision-making approaches hinder efficient resource allocation among VNFs. This paper addresses these challenges by proposing a hybrid framework that integrates LSTM-based demand prediction regression model for time series forecasting, game-theoretic resource allocation, and blockchain-based access control. The blockchain provides decentralized, tamper-proof storage of cryptographic keys and automated enforcement of sharing agreements through smart contracts, eliminating reliance on trusted third parties.The proposed predictive algorithm combines real-time demand data with LSTM forecasts to estimate effective demand, enabling proactive resource allocation. The framework is evaluated through extensive simulations, comparing the proposed method with greedy matching and diagonalization baseline approaches. Results show that the proposed method achieves a 10.3% improvement in social utility over greedy matching, while reaching a near-optimal solution with a cost 6.3 × lower than diagonalization. The LSTM model achieves a Mean Absolute Percentage Error (MAPE) of 8.91% and an R 2 score of 0.880.These results demonstrate that integrating demand prediction into game-theoretic NFV resource sharing significantly enhances efficiency while preserving strong security guarantees.

B-MARCO: a blockchain-driven multi-national auction and resource optimization framework for cross-border logistics and trade

Cross-border maritime logistics requires coordination among carriers, ports, customs authorities, and financial institutions, yet existing workflows are often constrained by fragmented regulatory data systems, inefficient dynamic capacity matching, and slow batch-based clearing. This paper proposes B-MARCO, a blockchain-driven multi-national auction and resource optimization framework for cross-border maritime logistics. B-MARCO integrates permissioned consortium coordination, rolling auction-based market clearing, a hybrid GA-GSA dispatching engine, and privacy-preserving access-control and compliance-verification components. The evaluation uses a semi-synthetic discrete-event simulation with a 7-day horizon, hourly decision intervals, 30 random seeds, and shared order streams across all configurations. Under a 5,000-orders/day workload, B-MARCO achieves a 91.80% transaction success rate and 21.53 h average end-to-end latency, compared with 60.09% and 36.66 h for the 24-hour batch-clearing baseline. Relative to a centralized rolling-matching baseline, it reduces average latency by 13.60%. The ablation results show that rolling auction clearing reduces latency, while smart-contract-based settlement-delay reduction is a major contributor to improved success and deadline performance. These findings support the value of combining rolling market clearing with spatial-temporal dispatch optimization in simulated cross-border maritime logistics, while the blockchain and cryptographic modules define the broader coordination, auditability, and data-governance architecture.