To alleviate the inventory pressure and improve operational performance, the e-tailer that develops a store brand (SB) may make a service investment in her self-operated stores. However, the existing literature rarely considers such a service investment strategy and its impact on national brand (NB) suppliers, especially on their selling model selection. We employ a theoretical model to explore the interactions of the NB supplier’s selling model selection and the service investment strategy of the e-tailer developing an SB. Our findings show that under the reselling model, the e-tailer always benefits from her service investment. Interestingly, however, the e-tailer may suffer from her service investment under the agency model. Meanwhile, the likelihood of the e-tailer adopting service investment decreases as the consumer service sensitivity increases. Furthermore, we find that the service investment increases the scope wherein both firms prefer the reselling model. In addition, we show that the supplier may adopt the agency model rather than the reselling model to counteract the service investment strategy of the e-tailer. These findings provide actionable insights to help suppliers and e-tailers make strategic operational decisions.

Most of the renewable energy sources have unstable supply and high volatility. With the growing share of renewable energy in the integrated energy system, it is more and more difficult to execute the energy pre-dispatch regulation decision of the integrated energy system. To address the problem of increased volatility of the system, the study proposes to optimize the pre-dispatch decision-making of the system’s control center by analyzing the difference between the day-ahead market clearing price and the declared price of electricity supply. The results show that the node declared power is much higher than the ground's actual clearing power during the period from 1:00 am to 4:00 am. During this period the declared power of the nodes is at 2500kW and the actual clearing power of the nodes is around 1500kW. The outgoing power of the integrated energy system electrical load can be reduced in advance during the period from 10 am to 3 pm. The proposed pre-dispatch decision of the integrated energy system on the basis of the difference between the day-ahead clearing price and the node declared price can ensure the stability of the system operation while reducing the operating cost of the integrated energy system.

Reservation travel has gradually become an important strategy to alleviate urban traffic congestion by finely matching traffic supply and demand. In order to quantify the effectiveness of the reservation travel strategy, ordinary vehicles and reservation vehicles are considered, The reservation travel problem is formulated as a Bi-level Programming (BP), where the upper-level objective is to maximize traffic demand, while the lower-level model considers the System Optimal-Stochastic User Equilibrium (SO-SUE) mixed traffic equilibrium. The equivalence of the mixed equilibrium problem and the existence of the solution are proved. On the basis of the WOA algorithm, combined with the lower-level Partan Frank-Wolfe algorithm, the traffic assignment solution process is connected to the upper-level as a function. A Whale Optimization Algorithm (WOA) nested Partan Frank-Wolfe algorithm is proposed to solve the model. Finally, the Sioux-Falls network numerical experiment proves that the reservation travel has a gratifying benefit in alleviating traffic congestion. By comparing the Vehicle-to-Capacity (V/C) ratio and cost before and after the implementation of reservation, it shows the effectiveness of reservation travel for urban traffic congestion management, and discusses the impact of the number of reserved roads and reservation trends on road network capacity, service level and travel cost under the implementation of reservation travel.

As an important part of the fresh produce business format, fresh food instant delivery encounters numerous challenges. Issues like high losses, complex cold chains and time sensitivity lead to increased costs. Additionally, the living space of end-delivery personnel is under pressure and the talent market is saturated. The platform algorithms focus on the interests of themselves and customers while relatively overlooking those of delivery personnel, which affects the overall operation quality, resulting in a significant reduction in delivery efficiency and a remarkable decline in service quality, and further leading to the loss of user stickiness. Therefore, optimizing the fresh food delivery route and considering the interests of multiple parties to improve efficiency and service quality is a crucial research issue in the field of fresh food instant delivery. This paper designs a three-objective static model for fresh food instant delivery aiming at minimizing the total cost, maximizing customer satisfaction and maximizing riders satisfaction. Considering the dynamic changes of orders during the actual operation process and in combination with the dynamics of newly added orders, a multi-objective dynamic model with the goals of minimizing the total cost, minimizing the average customer dissatisfaction and maximizing the income fairness of riders is further established. Based on the constructed models and by incorporating the SPBO strategy, the NSGA-III algorithm is improved and designed to make it more adaptable to the multi-objective optimization requirements in the fresh food instant delivery scenario. This study selects five operational points within a specific region of a fresh food self-operated platform and the order data from a particular day as research cases to obtain the relevant parameters required for the model and conduct case analysis. Based on the platform's business priorities and development needs, appropriate Pareto solutions are selected. Additionally, the feasibility and effectiveness of the improved algorithm are verified through algorithmic comparison. The research aims to provide valuable references and insightful implications for the management decisions of relevant fresh food self-operated platforms, as well as to continuously optimize the management and service of the instant delivery process.

Conventional railway emergency logistics frameworks are typically characterized by transport capacity adjustments to prioritize emergency material transportation. However, this paradigm frequently results in extended emergency response times and substantial delays in conventional freight operations. To address these limitations, an entire-process optimization strategy encompassing the Emergency Recovery Phase (ERP) and Post-Emergency Recovery Phase (PERP) was formulated, accompanied by a two-stage multi-objective optimization model. Diverging from conventional frameworks that necessitate operation plan reconfiguration for emergency train deployment, the proposed strategy streamlined operation plan replanning in the ERP through formation and loading plan optimization, while concurrently incorporating transportation cost-effectiveness in the PERP into the holistic optimization framework. The ERP submodel was designed to ensure the balanced allocation of limited emergency materials while achieving significant reductions in emergency response time. Subsequently, the PERP submodel incorporated dual considerations of transportation cost-effectiveness for railway carriers and cargo owners, while mitigating delay losses in conventional freight operations. To resolve this multi-objective optimization model, the Adaptive Variable Neighborhood Non-dominated Sorting Genetic Algorithm-II (AVNNSGA-II) was developed. The following results were obtained by this empirical study. (1) The ERP submodel attained emergency material satisfaction rates exceeding 51.28% across multiple disaster-affected areas while achieving emergency response time reductions of 6.16–19.22% relative to conventional railway emergency logistics frameworks. Notably, it demonstrated superior performance relative to road-based emergency logistics under different speed scenarios, with 55.9–69.4% response time reductions. (2) The PERP submodel effectively reduced delay losses in non-emergency freight operations by 50.49% through the implementation of differentiated transport prioritization mechanisms. (3) The superiority of this algorithm was confirmed with 97% of Pareto front solutions of AVNNSGA-II exceeding those of conventional NSGA-II. In conclusion, the proposed strategy is demonstrated to synergistically balance emergency response efficiency and transportation cost-effectiveness, thereby significantly enhancing railway emergency logistics performance. Furthermore, the integration of AVNNSGA-II with the multi-objective optimization model provides innovative perspectives for addressing large-scale rail freight allocation and scheduling challenges.

In order to address project delays caused by uncertain factors in the construction project schedule management process, a new method for calculating the size of critical chain buffers is proposed. Taking into account process uncertainty and based on the GERT network, the correction process and duration of different logical processes are provided to transform the uncertain network into a deterministic one. By combining the critical chain management method and information entropy theory to assess the impact of increasing uncertainty in the merging process on the critical chain buffer, a calculation model for the critical chain buffer size based on the GERT network is established and analyzed using a numerical example. The feasibility and effectiveness of the model are verified through an example, demonstrating its ability to reduce project risks and shorten the construction period while ensuring a high probability of project completion.

Promoting green packaging production represents a crucial strategy for the packaging industry in its pursuit of sustainable development. This study constructs a three-party evolutionary game model involving suppliers, manufacturers, and brands to examine their strategic decision-making under various scenarios. Simulation and analysis yield three principal findings. First, the system initially begins at (0,0,0) and may transition to a manufacturer-dominated intermediate state—either (1,1,0) or (0,1,1)—before gradually stabilizing at the equilibrium point (1,1,1). Second, supply chain decision-making is influenced by both internal and external factors. Internal factors include penalty mechanisms, carbon trading allocation, and cooperative concessions, whereas external factors comprise consumer preferences for traceability and the environmental attributes of packaging. Specifically, suppliers are primarily driven by internal factors, manufacturers are predominantly influenced by external factors, and brands are impacted by a combination of both. Third, serving as the central node in the supply chain, manufacturers enable upstream and downstream integration through traceable production, refine cooperative concession mechanisms to enhance brand participation, and harness market signals to promote green transformation and co-production among suppliers. Therefore, the effective management of the green packaging supply chain necessitates the establishment and ongoing refinement of a tripartite active cooperation mechanism. Additionally, cultivating consumer preferences for traceability is essential for advancing the long-term sustainable development of the supply chain.

The parallel batch-processing machine scheduling problem is widely present in industries such as manufacturing, service, and healthcare, and becomes more complex when incorporating flexible preventive maintenance (FPM). This paper presents a mixed-integer programming (MIP) model and a multi-objective artificial bee colony (MOABC) algorithm to tackle the unrelated parallel batch-processing machine scheduling problem with flexible preventive maintenance (UPBPM-FPM). The objective is to simultaneously minimize the makespan, earliness and tardiness, and total energy consumption, providing a comprehensive solution to optimize both scheduling efficiency and energy use while incorporating preventive maintenance considerations. The MOABC algorithm integrates three key innovations: (1) a novel processing power-feature information (PP-FI) heuristic to generate high-quality initial solutions, (2) a hybrid selection strategy combining the hypervolume index and roulette wheel approach to improve diversity and convergence, and (3) a set of random and goal-oriented neighborhood search methods to enhance Pareto frontier. Experimental results demonstrate that the MOABC algorithm outperforms three classical algorithms, NSGA-III, ABC, and PSO, in terms of convergence, diversity, and robustness of the Pareto solutions. This study provides a robust framework for energy-efficient scheduling in complex manufacturing environments.

To address the frequent changes, dynamic evolution, and complex collaboration of BOM (Bill of Materials) under ETO (Engineer-to-Order) mode, this paper proposes a dual-layer BOM-based change control model. First, to enable model definition and change expression throughout the product lifecycle, a version control-based BOM model is defined by introducing material revision, material relationship links, and a multi-view mechanism, while also constructing a general BOM structure system. Then, to ensure traceability of product structural changes and cross-view consistency in the ETO mode, we design a dual-layer change traceability model. This model features vertical version chains and horizontal view collaboration traceability as its core components. Finally, an ETO-oriented BOM change operation model is constructed to standardize both in-view change operations and cross-view cooperative operations. This standardization enhances change control capability and lifecycle traceability efficiency of product structures in ETO manufacturing environments. The application of this model in a large equipment manufacturing enterprise shows that it significantly improves the change response efficiency and provides strong support for the digital transformation and supply chain collaboration of ETO enterprises.

This paper explores the strategic behavior of power generators under green certificate trading policies, considering both renewable and conventional energy generators. Using game theory, we construct a Nash equilibrium model that incorporates the unit price of green certificates, the required quantity of certificates, and the cap on the quantity. By applying the Karush-Kuhn-Tucker conditions, we reform this Nash equilibrium problem as a mixed complementarity system, which can be solved by MATLAB software. Furthermore, we conduct sensitivity analysis and numerical tests on a number of important parameters. The results reveal that, under certain conditions, the unit price of green certificates does not affect the number obtained by renewable energy generators or purchased by conventional energy generators. However, as the required number of certificates for conventional energy generators increases, both the quantity of certificates that renewable generators obtained and conventional generators purchased increase proportionally. Additionally, the outcomes of limiting the quantity of green certificates awarded to renewable energy generators align with government regulations on the purchase requirements for conventional energy generators. This research provides new insights for power generators in ensuring financial viability and optimizing operations under green certificate trading policies. By enhancing carbon emission reduction capacity, these findings may contribute to the effective management of the electrical supply chain.