Driven by the robust growth of ecommerce and the ongoing upgrading of consumer demand structures, the development and strategic layout of private store brands by ecommerce platforms have emerged as a key development trend in the global retail industry. This industrial shift not only grants platforms stronger channel bargaining power and wider profit margins, but also fundamentally reconstructs the traditional channel power structure that was long dominated by national brand manufacturers. Against this backdrop, this study takes a two-tier supply chain consisting of a national brand manufacturer and an ecommerce platform as the research object. Under the channel power structure where the ecommerce platform serves as the leader in the Stackelberg game, we build game models integrating the fairness preference behaviors of supply chain members, to systematically explore the heterogeneous effects of brand advantage, store brand quality perception, and fairness concerns on the pricing strategies and revenue performance of all supply chain participants. The empirical results indicate that in the fairness neutrality scenario, the ecommerce platform’s leadership can significantly boost the revenue of all supply chain members. However, the fairness concerns of the national brand manufacturer are not always conducive to its own benefits, and may even impair the ecommerce platform’s revenue. When the commission rate exceeds a specific critical threshold, overemphasis on fairness may even trigger a drop in the manufacturer’s own revenue. For the ecommerce platform, the revenue contribution of its fairness concern behaviors is heavily dependent on the level of the commission rate, and only exerts a positive promotion effect in the high commission rate scenario. In addition, under the ecommerce platform’s leadership, the increase of the commission rate has a positive driving effect on the retail prices of both national brand and store brand products, while the promotion effect of brand advantage on product quality is restricted by specific parameter thresholds. This study enriches the comparative research on store brand supply chain management under different channel power structures, extends the behavioral operation theory of supply chain members with fairness preference, and offers a new analytical perspective for investigating the core operational decision-making mechanisms of ecommerce supply chains.

In order to meet the market demands for customization and rapid response while controlling carbon emissions in manufacturing systems, it has become increasingly important to consider the collaborative optimization of equipment layout in smart production units alongside intelligent storage and transportation systems. This study investigates the loop layout problem of smart manufacturing units equipped with Automated Guided Vehicles (AGVs) through a novel simulation-optimization framework integrating intelligent search algorithms with directed graph-based isomorphism detection. An optimization model is presented with the objective functions of maximizing production capacity and minimizing energy consumption, constrained by performance indicators such as relative position of the equipment within the unit, AGV transport speeds and buffer capacity. Given the lack of a closed mathematical expression for this multi-objective function, a second-generation NSGA-II based on simulation is designed to solve the model. Furthermore, a method based on directed graph isomorphic layout processing is proposed to quickly eliminate similar solutions, enhancing solution quality and algorithm efficiency. Through comparative experimental design and practical applications in intelligent workshops, the effectiveness, superiority, and application value of this optimization algorithm in addressing the circular layout problem of smart production units are validated.

Due to the high uncertainty in humanitarian supply chains, utilizing pre-positioning strategies in warehouses to manage crises entails significant risks, including excess inventory and shortages. One approach to managing these risks is to use options contracts. In this study, a dual-option contract, which combines the benefits of both call and put options, is employed to manage risks in a humanitarian supply chain. In this network, in addition to the main supplier, the humanitarian organizations (HO) can also access secondary and tertiary supply sources, namely, in-kind donations and spot markets. Considering these supply sources and the HO's logistics costs including procurement, inventory and transportation, this study models the HO's procurement problem and identifies the optimal initial order, call option, and put option. After forming the centralized problem, the reservation and exercise prices of the dual-option contract are calculated to achieve coordination among supply chain members and a win-win solution. This modeling effort presents a novel application of dual option contracts within humanitarian supply chain, where the contract parameters are endogenously derived to serve as coordination mechanism, an approach not extensively explored in prior literature. Finally, sensitivity analyses are performed on key parameters using numerical examples, and several managerial insights are reported.

With the rapid development of the new energy vehicle industry, vehicle-to-grid interaction technology, as an important link connecting the power system and the transportation system, its maturity directly affects the construction of the new power system and the process of energy transition. This paper proposes a maturity assessment method for vehicle-to-network interaction technology that integrates CNN-LSTM and fuzzy comprehensive evaluation. Firstly, a comprehensive evaluation index system was constructed from multiple dimensions such as technology, market, and business model, consisting of five levels: the regulatory layer, the system layer, the market layer, the security protection layer, and the common support layer, covering key indicators such as the accuracy rate of charging load prediction and the return on investment ratio. Secondly, in view of the uncertainty and complexity characteristics in the assessment of technology maturity, the CNN-LSTM model in deep learning was combined with the theory of fuzzy mathematics to construct a dual-path assessment framework. Finally, an empirical analysis was constructed based on multi-source datasets to verify the effectiveness of this method, providing a scientific basis for investment decisions and industrialization promotion of vehicle-to-network interaction technology.

Volume-Based Procurement has been implemented as a key reform policy in China's pharmaceutical distribution since 2018, resulting in a sharp decline in the profitability of the pharmaceutical supply chain. This paper develops cost and profit models for Decentralized Decision-Making (DD) and Centralized Decision-Making (CD) in a three-tier supply chain comprising one manufacturer, one distributor, and one hospital. Then, under member profit margin, profit allocation based on the impact of channel power, and the increased profit of each member, a coordination contract is designed that includes the range of coordination factors for the distribution fee rate paid by the manufacturer to the distributor and the subsidies provided by the manufacturer to the hospital. Additionally, the boundary of pharmaceutical pricing under both decisions is investigated, and the findings are finally validated through numerical computation. Some results are found: Under CD, the system’s overall profit increases. Before profit coordination under CD, the logistics costs for the hospital and manufacturer increase, while the distributor’s logistics costs decrease compared with DD, resulting in a concentration of increased profit in the distributor. After coordination, the manufacturer's costs are lower than under DD Furthermore, the pharmaceutical price boundary under CD is lower than that under DD.

The present study constructs a remanufacturing supply chain framework that encompasses both manufacturer and retailer. We conduct an in-depth investigation into the influence exerted by blockchain platforms in the supply chain, in response to carbon reduction challenges initiated by manufacturers. By introducing parameters such as the proportion of joint emission reduction investment costs and blockchain unit verification fees, we perform a comprehensive analysis of the effects produced by consumer sensitivity and blockchain platforms on sales prices, recycling prices, carbon emission reduction, market demand, amount of recycled, and profits under different emission reduction modes. Research has revealed that irrespective of whether the manufacturer adopts blockchain platforms, wholesale prices, retail prices, carbon emission reduction, market demand, manufacturer's profit in the joint emission reduction model are always higher than those in individual emission reduction model, while recycling prices and amount of recycled remain unchanged. With retailer's share of the carbon reduction investment cost kept within an appropriate range, both profits of retailer and supply chain increase under joint emission reduction model. When a manufacturer introduces blockchain technology platforms, unit verification fees only affect wholesale prices and have no impact on retail prices, recycling prices, carbon emission reduction, and profits.

The prevalence of counterfeit goods hinders the platform economy's growth. To tackle this, we model the strategic interaction between an online platform and a seller using a bilateral evolutionary game. The platform chooses between strict or lenient regulation, while the seller decides on blockchain adoption for product authentication, with government penalties as a key context. We derive the evolutionary stable strategies (ESSs) under four scenarios. Results show that the seller's blockchain adoption depends critically on its cost and the intensity of anti-counterfeiting enforcement by both the government and the platform. Excessively high penalties or insufficient subsidies can deter adoption, highlighting the need for the platform to balance subsidies and penalties. Once the seller adopts blockchain, the platform prefers lenient regulation; otherwise, the platform's decision hinges on her regulatory costs and potential government intervention. Initial strategy choices also significantly impact the evolutionary outcomes.

Recently, a growing number of upstream factories are entering the territory of the national brand manufacturer (also called the original equipment manufacturer, denoted by OEM) by establishing their private labels (PLs) in a platform-based supply chain. Yet, existing literature rarely considers how the factory’s entry strategy, non-entry, entry via the platform’s marketplace channel, or entry via the platform’s resale channel, interacts with the OEM’s decision of whether (and how) to introduce a marketplace channel, although this phenomenon is common in reality. In a three-tier supply chain consisting of a factory, an OEM and a platform, we utilize game theory to discuss the relationship between the factory’s entry strategy and the OEM's marketplace channel introduction strategy. Our results show that the factory’s entry via the resale channel always benefits both the platform and himself. By contrast, the factory entering via the marketplace channel may hurt the platform or himself. Furthermore, we show that when the PL's perceived value is low, the OEM’s marketplace channel introduction reduces the probability of the factory’s entry through the marketplace channel; otherwise, such marketplace channel introduction raises the probability that the factory enters the market via the marketplace channel. Surprisingly, we find that the OEM's introduction of the marketplace channel may worsen her profit reduction caused by factory entry. Finally, we derive the equilibrium result and show that in response to factory entry, the OEM chooses not to introduce a marketplace channel to guide the factory to enter the market through the resale channel.

To improve the efficiency of mobile robots carrying shelves for material handling in manufacturing enterprises, this study considers multiple material requirements of orders in a coordinated manner and investigates the transfer of multi-load shelves between storage areas and picking workstations. Three types of resources, shelves, automated guided vehicles (AGVs), and storage locations, are jointly considered, and the task allocation and path planning problem for AGVs is studied within an integrated framework. With the objective of minimizing the overall order picking completion time, a mixed-integer programming model is established to simultaneously represent decisions including resource allocation, path selection, and task sequencing, enabling the optimization of multiple interdependent decisions within a unified scheduling framework. To solve the proposed model, an improved genetic algorithm is developed. The algorithm adopts a chromosome encoding scheme based on the "pick–sort–deliver" shelf-handling task unit and constructs multi-granularity crossover operators at the order level, AGV level, and task level to effectively address the multi-resource and multi-level decision structure of the problem. In addition, five neighborhood search operators are designed to form a hierarchical neighborhood search mechanism, further enhancing local solution quality. Experimental results show that the hybrid genetic algorithm significantly outperforms the comparative methods in maximum order completion time, with average improvements of 19.06% and 16.38% over the greedy algorithm and greedy-local search algorithm, respectively. The algorithm also exhibits satisfactory stability and robustness across various problem scales.

Effective strategies for Prognostics and Health Management (PHM) are fundamental to advancing the operational integrity and economic sustainability of complex industrial assets. Overcoming the persistent hurdle of modeling the intricate temporal nonlinearities and stochastic behaviors inherent in degradation processes is central to this endeavor. This study introduces an optimized maintenance framework based on real-time condition monitoring for systems subject to stochastic wear, utilizing an innovative nonlinear Wiener process model with time-scale transformation. By framing the optimization objective as the minimization of the long-term average cost rate, we derive explicit expressions for the optimal inspection interval and the threshold for preventive maintenance. A likelihood ratio test demonstrates the superiority of the nonlinear model over linear alternatives. A case study on a Power Take-Off (PTO) unit validates the effectiveness of the proposed method, showing its ability to balance failure risk and maintenance cost efficiently. The proposed strategy provides a practical and scientifically grounded tool for managing systems with nonlinear degradation characteristics.