Search Results (513)

This paper investigates a transportation service procurement problem for truckload operations within a combinatorial auction framework. In practice, conventional clock auctions may suffer from slow convergence and inefficient allocation when the number of auction rounds is limited. To address this issue, we propose a two-phase combinatorial auction framework, in which supplementary bidding is introduced after the clock auction to improve allocation efficiency. The proposed framework integrates the bid generation problem, the winner determination problem, and the supplementary bidding process, all formulated as mixed-integer linear programming models. Two variants of the framework are developed, where supplementary bundles or bids are generated by the auctioneer or the carriers. Computational experiments show that the proposed framework improves allocation efficiency and reduces procurement costs within the tested settings compared with the standard clock auction. In addition, the supplementary bidding phase accelerates convergence and enables near-efficient allocations within a limited number of rounds. The results demonstrate that the framework can achieve reasonable allocation outcomes while accelerating convergence. Full article

The objective of this study was to evaluate the effects of timing of injectable trace mineral (ITM) administration (28 days (d) prior to or at weaning) on performance and health in mixed-sex beef calves (n = 115; 224 ± 40 kg). Calves were randomly assigned to one of the following treatments: (1) no ITM (CON), (2) ITM administered 28 d before weaning (PW), or (3) ITM administration at weaning (WEAN). At weaning, calves were transported to a local auction barn, held overnight, and returned the following day; BW, blood, and hair samples were collected prior to and through the receiving period. Data were analyzed using SAS 9.4. Serum Se increased in PW calves following ITM administration (p < 0.01). Serum Mn increased in PW and WEAN groups (p < 0.01) and PW calves showed increased serum Cu at weaning (p < 0.01). Across treatments, calves experienced 6% shrink following weaning and transport, with recovery of BW and intake occurring within 21 d and 8 d, respectively. Despite improved mineral status, no performance benefits were observed during the receiving period, reflecting adequate baseline mineral status and low-stress management conditions, suggesting that ITMs may have limited benefits in well-managed herds. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article

Dynamic task allocation for UAV swarms in complex scenarios is often complicated by uncertain object discovery, potential UAV loss, as well as stringent battery and execution resource limitations. These resource constraints critically affect UAV survivability and mission success but are frequently neglected in existing studies. This paper develops an auction-based dynamic task allocation for resource-constrained UAV swarms conducting cooperative monitoring and interception missions in dynamic scenarios. Task priority is incorporated to prioritize high-urgency areas and identified objects, and a threshold-based cooperative engagement strategy is proposed to facilitate multi-UAV coordination for interception missions beyond individual UAV capabilities. Meanwhile, battery-aware resource allocation is adopted to improve utilization during cooperative operations. Simulation results across scenario scales and resource configurations demonstrate that the proposed method significantly improves UAV survivability while maintaining competitive mission completion rates, proving its effectiveness for resource-constrained UAV swarm operations. Full article

This paper investigates the problem of contract coordination in a two-tier multi-unit auction supply chain consisting of a seller and an auction house. We theoretically show that the conventional commission-based mechanism distorts the transmission of demand information from the demand side to the supply side, thereby preventing effective supply chain coordination. In contrast, guaranteed auction contracts can achieve coordination under both cooperative and non-cooperative game frameworks. Under the cooperative game setting, profits are allocated according to a Nash bargaining solution, in which each party receives its disagreement payoff and a bargaining-power-weighted share of the surplus, with risks and returns being allocated symmetrically. Under the non-cooperative game setting, the supply chain leader can appropriate a larger share of the total profit while bearing relatively lower risk. These results indicate that, as the supply chain leader, the auction house can select different cooperation modes under guaranteed auction contracts according to its bargaining position, but profit allocation should be benchmarked against the cooperative game outcome in order to enhance the long-term competitiveness and stability of the supply chain. Full article

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

Peer-to-peer (P2P) energy trading offers a decentralised framework for integrating distributed renewable resources. When local renewable energy generation exceeds demand, surplus electricity can be converted into hydrogen for long-duration storage, providing flexibility beyond the electricity vector. However, most existing P2P markets are focused only on electricity, do not account for network losses and are not designed to coordinate multi-vector trading with inter-temporal couplings. To address these gaps, we propose a distance-aware periodic double auction (DA-PDA) market-clearing mechanism that extends the conventional PDA by incorporating loss-aware pricing and enabling trades between peers with the lowest loss cost. The DA-PDA provides a distributed, market-based coordination mechanism for joint electricity–hydrogen trading, improving efficiency through dynamic price signals. The framework enhances system-level performance by reducing renewable curtailment, increasing utilisation of surplus electricity and enabling hydrogen-supported flexibility. Using a real-world case study, we demonstrate that sector-coupled P2P markets can improve local social welfare and act as an effective energy-conservation mechanism in highly renewable, electrified systems. Full article

In the context of China’s transition from “dual control of energy consumption” to “dual control of carbon emissions,” understanding the synergistic mechanisms among carbon emission trading (CET), energy use rights trading (EURT), and electricity markets is critical for achieving the nation’s dual carbon goals. This study develops a system dynamics (SD) model to examine the coupled interactions within this “carbon–electricity–energy” ternary market system, focusing on thermal power enterprises as the primary analytical subject. The model reveals that the ternary market framework drives energy conservation and emission reduction through three key mechanisms: price signal transmission, dual regulatory constraints, and mutual quota recognition. These mechanisms propagate low-carbon incentives throughout the industrial chain by transmitting cost signals to end-users via electricity prices. Compared to binary market structures, the ternary framework achieves superior outcomes, it facilitates higher renewable energy consumption, maintains more stable price levels, enhances market liquidity for both carbon and energy rights, and improves resource allocation efficiency alongside environmental–economic performance. However, the simulation also exposes critical inefficiencies under the current “dual control of energy consumption” regime. The parallel operation of EURT and CET markets creates functional overlap and duplicated compliance burdens. This redundancy increases enterprise costs without commensurate environmental gains, validating the necessity of transitioning to carbon-focused dual control. Further analysis demonstrates that a mutual recognition mechanism between carbon and energy rights effectively alleviates dual compliance pressures and improves enterprise profitability. Optimal market performance emerges when the recognition ratio is appropriately calibrated. Additionally, gradually increasing the share of auctioned quotas while maintaining appropriate levels of free allowances can drive emission reductions without compromising enterprise profitability. This research provides both theoretical foundations and practical policy recommendations for building an efficient multi-market coordination mechanism, facilitating the policy transition, and advancing low-carbon transformation in China’s power sector. Full article

In the Internet of Vehicles (IoV), connected vehicles generate high-load perception tasks with large-scale and multimodal sensitive data, imposing strict requirements on latency, computing, and privacy. Existing solutions still suffer from high task service latency and privacy risks. To address these issues, this paper proposes an integrated framework that jointly considers multi-flow task offloading, adaptive privacy preservation, and latency-aware resource incentive mechanism. Specifically, we propose a Location-Aware and Trust-based (LA-Trust) dual-node task offloading algorithm based on deep reinforcement learning (DRL), which treats pre-partitioned subtasks as multiple parallel flows and enables flow-level collaborative offloading optimization across neighboring nodes, allows subtask data uploading and processing to proceed concurrently, and incorporates node security into decision making. To further enhance privacy protection, a Distribution-Aware Local Differential Privacy (DA-LDP) algorithm is designed to adaptively inject artificial noise according to data heterogeneity, balancing privacy protection and task execution accuracy. In addition, a Delay-Cost Reverse Auction (DC-RA) algorithm is proposed to further reduce latency by introducing wireless channel modeling between idle vehicles and edge nodes into the incentive mechanism. Experimental results show that the proposed framework improves task execution accuracy by 38% and reduces offloading cost, delay, incentive cost, and auction communication latency by 64.41%, 64.64%, 19%, and 44%, respectively, while more than 60% of tasks are offloaded to high-trust nodes. Full article

Rapid search for evasive targets using multiple Unmanned Aerial Vehicles (UAVs) presents significant challenges, as it requires real-time target-motion prediction, multi-agent coordination, and adherence to kinematic constraints. Existing cooperative search methods often assume non-adversarial target behavior or model target motion independently of UAV actions, which reduces their effectiveness against targets that actively evade based on UAV positions. To address these limitations, this study introduces the Cooperative Rapid Search Algorithm for Evasive Targets (CRS-AET). The proposed framework utilizes graph-theoretic modeling to represent spatial-temporal relationships among UAVs, targets, and environmental grids. A directional gradient-based motion prediction (DG-Prediction) method first estimates probable movement areas of dynamic targets within the graph-structured environment. An improved multi-round auction algorithm with graph-based utility propagation (IMRAA) then optimizes UAV resource allocation. Finally, Dubins-Constrained Trajectory Optimization (DC-RTO) is integrated within a distributed model predictive control (DMPC) scheme to ensure kinematic feasibility. Simulation results across three representative scenarios indicate that CRS-AET enables faster target detection, enhanced area coverage, and more efficient coordination than baseline methods. Hardware-in-the-loop (HIL) experiments further confirm the robustness and practical applicability of the framework in realistic operational environments. Full article

This article investigates the phenomenon of numismatic forgery legitimisation and its impact on the fields of numismatics, archaeology, history and law. Forgery legitimisation is a broad phenomenon that encompasses both physical forgery and the presentation of fake artifacts as genuine in research literature, auction catalogues, and other contexts. Using the qualitative case-study methodology, the authors propose an analytical framework for suspected forgery legitimisation that incorporates a novel classification of forms and types of forgery, as well as socio-legal mens rea elements. The framework also accounts for factors contributing to the legitimisation of forgeries, including lack of competence, low competition in coin catalogue publication, tradition, closed numismatic communities, and insufficient academic and legal attention. Using this framework, the authors examine two cases of legitimisation of fake coins in medieval Polish–Lithuanian numismatics. The analysis shows how repetition across sources can legitimise fake artifacts, complicating later correction and corrupting heritage research, history and museum science, as well as market integrity. The proposed analytical framework can be useful for investigating other dubious artifacts and for developing analysis methods for forgery legitimisation cases. Full article

Multi-unit common value auctions in which bidders submit demand functions are used for a variety of purposes, including selling government debt (Treasury auctions) and allocating liquidity (repo auctions). Typically, either a discriminatory or a uniform-price format is used. In this paper, we consider the incentive for participation by relatively uninformed bidders in the presence of more informed bidders under these formats. We characterize the equilibrium under a discriminatory auction and show that discriminatory pricing inhibits uninformed participation. In contrast, the equilibria we construct under a uniform pricing rule show that profitable uninformed participation can occur. The usefulness of widening participation in Treasury auctions makes the latter format a natural choice in these auctions, providing an explanation for the switch to the uniform-price format in US Treasury auctions. We also apply our results to repo auctions and show that a uniform-price format can reduce the ability of a central bank to steer interest rates. This sheds light on the reason for the switch away from the uniform-price format by several central banks in conducting repo auctions. We also consider the question of information aggregation and show that uniform-price auctions might fail to do so. The results also offer an explanation for the fact that the ECB, as well as several other central banks, prefer to allocate liquidity through a fixed-rate tender rather than either uniform-price or discriminatory auctions. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article