The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

One of the hurdles in the widespread commercial deployment of renewable energy is the lack of a mature market-oriented trading system. Beyond the intermittent nature of power generation and the mismatch between production and demand in renewable energy, the operational complexities include a multitude of trading entities, intricate trading mechanisms, and significant regional variations in policy management, which are further compounded when considering the integration of such systems within the secure and rapidly evolving digital ecosystems of the Metaverse. This poses distinct challenges for distributed renewable energy suppliers, highlighting the urgent need for information-based methods to support transaction decision-making and ensure robust security frameworks for Metaverse environments. Leveraging blockchain technology’s decentralized, transparent, and high-frequency trading capabilities, a segment of renewable energy firms are venturing into blockchain-enabled renewable energy trading systems that mirror the security measures crucial for transactions in the Metaverse. Currently, blockchain applications in renewable energy mainly focus on P2P energy transactions and shared energy storage. Cryptocurrencies and energy tokens are two bridge tools of the renewable energy blockchain. This work begins by examining the background and investigating hundreds of renewable energy projects with over 50 cryptocurrencies and energy tokens. Then, this work systematically summarizes the 1) Financing, 2) Settlement, 3) Permission, 4) Carbon Credits and Carbon Offset, and 5) Incentives and reward functions of cryptocurrency and tokens in the renewable energy blockchain. Additionally, the paper delves into the advantages and disadvantages of cryptocurrencies and tokens within the Metaverse economy. It offers a comprehensive analysis of their impact on both the renewable energy sector and the broader digital economic landscape and suggests pathways for their future development.