Search Results (71)

Vanadium (V) is a strategically important metal commonly recovered from titaniferous magnetite ores. Its principal application is in steel production, where it enhances mechanical properties, while smaller quantities are utilized in chemical processing, catalysis, and emerging energy storage technologies. It is expected that the demand for vanadium in the steel industry will increase by a compound annual growth rate of approximately 2.7% by 2029, and demand in energy storage will increase by an additional 6%. The growing demand for V has triggered global concerns regarding the supply risks of this critical metal. Industrial recovery of vanadium from magnetite deposits is carried out either through dedicated primary vanadium extraction routes or integrated processes that co-produce vanadium alongside steel. The latter accounted for approximately 73% of global vanadium output in 2021. These co-production operations generate significant volumes of by-product slag, often referred to as titaniferous slag, which can still contain notable concentrations of vanadium. In this study, a modified primary vanadium extraction route is proposed to recover V from such slag, using material containing approximately 0.9% V₂O₅ sourced from the former Evraz Highveld Steel and Vanadium Corporation in South Africa as a representative case. The work focuses on assessing the economic feasibility of the proposed process through a Discounted Cash Flow (DCF) analysis. Key financial metrics including net present value (NPV), internal rate of return (IRR), and payback period were calculated to evaluate viability and to identify process stages requiring further optimization. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

This study employs global optimization techniques to examine optimal vaccine pricing strategies that consider quantity discounts and vaccine distribution equity, under centralized procurement by group purchasing organizations. Based on the economic characteristics of the vaccine market, a mathematical programming model incorporates the payment capacities and willingness to pay of different member countries, minimizing the maximum adjusted price disparities across pricing tiers and thereby enhancing the overall fairness of vaccine distribution. To further reduce computational complexity and enhance practical applicability, this study improves the model by reducing the number of binary variables. Experimental analysis is conducted using real-world data from the Vaccine Alliance (Gavi) and the Pan American Health Organization (PAHO). The results show that the improved model reduces computation time by over 30% on average and demonstrates effective control over price differentiation across various pricing tiers and parameter settings. Full article

This study investigates the tri-objective optimization of underground metro station structures, considering structural reliability, life-cycle economic cost, and annualized carbon emissions simultaneously. Using a representative metro station in Guangzhou as a case study, a multi-objective optimization framework is developed. The model defines structural failure probability, discounted life-cycle cost, and average annual carbon emissions as the primary objectives, with decision variables including concrete strength, cover thickness, the use of epoxy-coated reinforcement, and various maintenance/repair strategies. Material quantities are calculated through Building Information Modeling (BIM), while cost–carbon relationships are derived from industry price data and carbon emission factors. An improved multi-objective particle swarm optimization algorithm (OMOPSO) is used to derive the Pareto-optimal front. Case study results show that increasing cover thickness significantly improves durability and reduces carbon emissions with only moderate cost increases. In contrast, epoxy-coated reinforcement is excluded from the Pareto set due to its high cost under the given conditions. To facilitate practical decision-making, a weight-based solution selection method is introduced, and sensitivity analyses are performed to assess the model’s robustness. The study concludes by emphasizing the framework’s applicability and limitations: the findings are specific to the case context and require recalibration for use in other sites or construction practices. This research contributes by integrating durability, cost, and carbon considerations into an engineering-level optimization workflow, providing valuable decision support for sustainable metro station design. Full article

Price is a pivotal determinant of market demand, as higher prices typically reduce sales while lower prices stimulate them. Thus, incorporating price-dependent demand into inventory models is both realistic and necessary. In practice, limited storage capacity often forces retailers to rent additional space, motivating the adoption of two-warehouse systems. Trade credit also plays a critical role in supply chain management: suppliers may offer cash discounts or deferred payments to encourage larger orders, while retailers extend credit to customers to boost sales. To reduce default risk, however, retailers usually provide only partial credit. Considering the time value of money, costs and profits are assessed using discounted cash-flow analysis to account for payment delays and inflation. This study develops an integrated supplier–retailer–customer chain model that (1) incorporates price-dependent demand, (2) includes a rented warehouse for limited storage, (3) considers partial trade credit, (4) links two-level trade credit terms to order quantity, and (5) evaluates financial performance on a present-value basis. The model aims to maximize total profit by determining optimal price, replenishment cycle, and order quantity. Numerical and sensitivity analyses confirm that extending supplier credit can lower prices and improve overall profitability, offering useful insights for strategic inventory management. Full article

In inventory management, business organizations gradually face challenges due to the complexities of managing perishable goods whose value diminishes over time. In such circumstances, interval’s bounds estimated business policy can be adopted to study a non-deterministic inventory model incorporating decay, preservation technology, and financial incentives, viz. advanced payments and fixed discounts. This study explores an interval Economic Order Quantity (EOQ) model incorporating advanced payment with discount options under preservation technology framework in interval environment. In this model, the demand rate is expressed as a convex combination of linear and power patterns of the selling price. The present model is formulated mathematically using interval differential equations and interval mathematics. Then, the corresponding interval-valued average profit of the model is obtained. In order to optimize the corresponding interval optimization problem, C-U optimization technique is developed. Employing the C-U optimization technique, the said interval optimization problem is converted into crisp optimization problems. Then, these problems are solved numerically by Wolfrom MATHEMATICA-11.0 software and validated with the help of two numerical examples. Finally, sensitivity analyses have been performed to study the impact of known inventory parameters on optimal policy. Full article

With rising consumer concerns regarding food safety, cold chain management—which preserves product freshness through low-temperature distribution—has emerged as a critical competitive factor for retailers. This study examines how retail firms can manage quality deterioration over time to maximize profits, with a focus on pricing strategies and discard rates. Through game-theoretic modeling and empirical data analysis of milk products, we find that while individual items exhibit no consistent pattern, bundled fresh food items demonstrate an inverted U-shaped relationship between discount rates and profits, indicating an optimal discount level. Furthermore, we identify a U-shaped relationship between order quantity and disposal rate, highlighting the importance of determining optimal inventory levels to minimize waste and maximize efficiency for a sustainable competitiveness. Full article

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers. Full article

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

This study examines how contract structures influence coordination and innovation incentives in dual-channel telecommunications supply chains. We consider a setting where a mobile network operator (MNO) supplies services both directly to consumers and indirectly through a mobile virtual network operator (MVNO), which competes in the retail market. Using a game-theoretic framework, we evaluate how different contracts—single wholesale pricing, revenue sharing, and quantity discounts—shape strategic decisions, particularly in the presence of investment spillovers between parties. A key coordination problem emerges from the externalized gains of innovation, where one party’s investment generates value for both participants. Our results show that single wholesale and revenue sharing contracts often lead to suboptimal investment and profit outcomes. In contrast, quantity discount contracts, especially when combined with appropriate transfer payments, improve coordination and enhance the total performance of the supply chain. We also find that innovation led by the MVNO, while generally less impactful, can still yield reciprocal benefits for the MNO, reinforcing the value of cooperative arrangements. These findings emphasize the importance of contract design in managing interdependence and improving efficiency in decentralized supply chains. This study offers theoretical and practical implications for telecommunications providers and policymakers aiming to promote innovation and mutually beneficial outcomes through well-aligned contractual mechanisms. Full article

This study investigates two pricing strategies within a vendor-buyer supply chain system under cap-and-trade regulation, emphasizing demand sensitivity to market price and green technology investment. The findings reveal that quantity discounts significantly enhance profitability across the supply chain by encouraging buyers to place larger orders, thereby benefiting vendors, buyers, and end consumers. A novel profit-sharing parameter is introduced to foster sustainable and mutually beneficial relationships between supply chain participants. A search algorithm is developed to determine the optimal solutions by using the profit-sharing mechanisms. The analysis yields three key insights: first, a critical cap threshold is identified, enabling supply chain participants to make informed strategic decisions based on the value of the cap; second, another critical cap threshold is derived to assist governments in setting feasible emission limits that incentivize vendors to invest in green technology—caps below this threshold may discourage such investments; third, a reasonable return on investment (ROI) benchmark is established to guide vendors in adopting effective green technology strategies. Numerical examples and sensitivity analyses are conducted to illustrate the theoretical framework and validate the findings. Full article

This model extends the classical economic production quantity (EPQ) model to address the complexities within a two-echelon supply chain system. The model integrates the cost of raw materials necessary for production and takes into account the presence of imperfect quality items within the acquired raw materials. Upon receipt of the raw material, a thorough screening process is conducted to identify imperfect quality items. Combining imperfect raw material and the concept of shifting production rate, two different inventory models for deteriorating products are formulated under imperfect production with demand dependent on the stock level. In the first model, the imperfect raw materials are sold at a discounted price at the end of the screening period, whereas in the second one, imperfect items are kept in stock until the end of the inventory cycle and then returned to the supplier. Numerical analysis reveals that selling imperfect raw materials yields a favourable financial outcome, with an optimal inventory level $I_1$ = 11,774 units, optimal cycle time $T=2140$ h, and a total profit per hour of USD 183, while keeping the imperfect raw materials to return them to the supplier results in a negative profit of USD $-4.44\times {10}^3$ per hour, indicating an unfavourable financial outcome with the optimal inventory level $I_1$ and optimal cycle time T of 26,349 units and 4702.6 h, respectively. The findings show the importance of selling imperfect raw materials rather than returning them and provide valuable insights for inventory management in systems with deteriorating products and imperfect production processes. Sensitivity analysis further demonstrates the robustness of the model. This study contributes to satisfying the need for inventory models that consider both the procurement of imperfect raw materials, stock-dependent demand, and deteriorating products, along with shifts in production rates in a multi-echelon supply chain. Full article

While the establishment of a unified regional power market that promotes the optimal use of renewable energy is considered to be a pathway to meeting the policy targets of “carbon peaking” and “carbon neutrality”, the economic feasibility of the power transmission project is not well understood. To fill this gap, this study conducted a cost–benefit analysis of a proposed project that transmits hydropower, photovoltaic power, and/or wind power whose ratios are 100:0:0, 79:13:8, and 65:22:13 in three scenarios from Dian to Yu in China. It was found that the project has economic feasibilities for each scenario; however, this highly depends on its external benefits, discount rates, and transmission quantity. As the ratio of hydropower becomes lower, the net present value decreases from 117.32 billion to 112.99 billion for an annual transmission of 7.498 billion kWh of electricity. Since the substitution of coal-fired power with renewable power contributes the highest benefit to the project, the cross-regional transmission of renewable electricity should be promoted jointly with the internalization mechanism of externalities. Full article

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits. Full article

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by $18.12\%$, whereas autonomated inspection increased retailer profit by $10.27\%$. Full article