Search Results (62)

Determination of the right price is vital for the success of newly developed food products. This study examined the market prices and their determinants for five ready-to-cook catfish products: Panko-Breaded Standard Strips (PBSS), Panko-Breaded Standard Fillet (PBSF), Panko-Breaded Delacata Fillet (PBDF), Sriracha-Marinated Delacata Fillet (SMDF), and Sesame-Ginger-Marinated Delacata Fillet (SGMDF). Market prices were derived using Vickrey’s second-price auction, where the second-highest bid represents the market price. We analyzed experimental auction data from 121 consumers using a logit model to estimate the probability of offering the market price based on product sensory attributes, socio-demographic characteristics of the participants, and the level of competition (panel size). Consumers’ willingness-to-pay (WTP) was elicited in two rounds: before tasting (visual evaluation) and after tasting (organoleptic evaluation) the products. Breaded products received higher market prices than marinated products, with PBDF ranked highest. Sensory traits, especially taste, along with income, education, and grocery shopping involvement, significantly influenced the formation of market price. Increased competition elevated the market prices. Both product features and consumer characteristics significantly affect market price outcomes, and experimental auctions provide a robust tool for understanding consumer behavior toward newly developed food products. Full article

The intrinsic relationship between food and health has led to growing interest in functional foods, particularly among athletes seeking to optimize performance and recovery. This study investigates the impact of product information and sensory attributes on athletes’ willingness to pay for an innovative high-protein bread. Utilizing a two-treatment experimental design, athletes were exposed to sensory evaluations either before or after receiving information. A combination of hedonic sensory analysis and economic evaluation assessed preferences through a non-hypothetical auction. Findings show that both sensory attributes—especially taste and aroma—and product information significantly influenced willingness to pay. The order of presentation played a crucial role: providing information first enhanced perceived value more strongly. While sensory evaluation moderately increased willingness to pay, product information had a stronger impact. A key contribution of this study is its novel evidence on how athletes balance sensory and informational cues in food evaluation—an aspect rarely explored. Contrary to assumptions that athletes ignore sensory quality due to their focus on nutrition, they did value sensory aspects, though they prioritized product information. These findings suggest that developing functional foods for athletes should integrate nutritional benefits and sensory appeal, as both elements contribute to acceptance and potential market success. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Distribution transformers serve as critical nodes in smart grids, and management of their recycling plays a vital role in the full life-cycle management for electrical equipment. However, the traditional manual dismantling methods often exhibit a low metal recovery efficiency and high levels of hazardous substance residue. To facilitate green, cost-effective, and fine-grained recycling of distribution transformers, this study proposes a fine-grained dismantling decision-making system based on a knowledge graph subgraph comparison and multimodal fusion perception. First, a standardized dismantling process is designed to achieve refined transformer decomposition. Second, a comprehensive set of multi-dimensional evaluation metrics is established to assess the effectiveness of various recycling strategies for different transformers. Finally, through the integration of multimodal perception with knowledge graph technology, the system achieves automated sequencing of the dismantling operations. The experimental results demonstrate that the proposed method attains 99% accuracy in identifying recyclable transformers and 97% accuracy in auction-based pricing. The residual oil rate in dismantled transformers is reduced to below 1%, while the metal recovery efficiency increases by 40%. Furthermore, the environmental sustainability and economic value are improved by 23% and 40%, respectively. This approach significantly enhances the recycling value and environmental safety of distribution transformers, providing effective technical support for smart grid development and environmental protection. Full article

With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurately reflect road conditions. This paper proposes an efficient and fair map-data-sharing mechanism for vehicular networks. To encourage vehicles to share data, we introduce a reputation unit to resolve the cold-start issue for new vehicles, effectively distinguishing legitimate new vehicles from malicious attackers. Considering both the budget constraints of map companies and heterogeneous data collection capabilities of vehicles, we design a fair incentive mechanism based on the proposed reputation unit and a reverse auction algorithm, achieving an optimal balance between data quality and procurement costs. Furthermore, the scheme has been developed to facilitate mutual authentication between vehicles and Roadside Unit(RSU), thereby ensuring the security of shared data. In order to address the issue of redundant authentication in overlapping RSU coverage areas, we construct a Merkle hash tree structure using a set of anonymous certificates, enabling single-round identity verification to enhance authentication efficiency. A security analysis demonstrates the robustness of the scheme, while performance evaluations and the experimental results validate its effectiveness and practicality. Full article

Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal Optimization (OL-GJO) to assign distributed edge capabilities to diminish the energy consumption and delay in IoT-based smart cities. In the context of IoT-based smart cities, a three-layered architecture is developed, comprising the IoT system, the Unmanned Aerial Vehicle (UAV)-assisted edge layer, and the cloud layer. Moreover, the controller positioned at the edge of UAV helps determine the number of tasks. The proposed approach, based on opposition-based learning, is put forth to offer effective computing resources for delay-sensitive tasks. The multi-joint symmetric optimization uses OL-GJO, where opposition-based learning confirms a symmetric search process is employed, improving the task scheduling process in UAV-assisted edge computing. The experimental findings exhibit that OL-GJO performs in an effective manner while offloading resources. For 200 tasks, the delay experienced by OL-GJO is 2.95 ms, whereas Multi Particle Swarm Optimization (M-PSO) and the auction-based approach experience delays of 7.19 ms and 3.78 ms, respectively. Full article

UAV-assisted mobile edge computing combines the flexibility of UAVs with the computing power of MEC to provide low-latency, high-performance computing solutions for a wide range of application scenarios. However, due to the highly dynamic and heterogeneous nature of the UAV environment, the optimal allocation of resources and system reliability still face significant challenges. This paper proposes a two-stage optimization (DSO) algorithm for UAV-assisted MEC, combining Stackelberg game theory and auction mechanisms to optimize resource allocation among servers, UAVs, and users. The first stage uses a Stackelberg game to allocate resources between servers and UAVs, while the second stage employs an auction algorithm for UAV-user resource pricing. Blockchain smart contracts automate task management, ensuring transparency and reliability. The experimental results show that compared with the traditional single-stage optimization algorithm (SSO), the equal allocation algorithm (EAA) and the dynamic resource pricing algorithm (DRP), the DSO algorithm proposed in this paper has significant advantages by improving resource utilization by 7–10%, reducing task latency by 3–5%, and lowering energy consumption by 4–8%, making it highly effective for dynamic UAV environments. Full article

The current study examines the impact of health claims on consumer preferences and willingness to pay (WTP) for functional snack bars, focusing on anti-inflammatory and antioxidant properties. Through an experimental auction involving 175 participants, this study investigates how providing clear information on product health benefits influences consumer interest and WTP while analysing the role of individual health consciousness (HC) in shaping these preferences. The results indicate that detailed health claims positively affect consumer WTP for functional snack bars compared to standard options. Although both anti-inflammatory and antioxidant claims attract consumer interest, no significant difference in WTP was observed between the two, suggesting similar perceived value for these distinct benefits. However, highly health-conscious consumers demonstrate a stronger preference and WTP for anti-inflammatory options, indicating that HC influences specific health claim valuation. These findings underscore the importance of effective health-related messaging in promoting functional foods and suggest that general health claims may resonate more broadly with consumers than specialised ones. This study’s results enhance the current knowledge on functional foods, especially snack bars, offering valuable insights for manufacturers aiming to implement targeted marketing strategies and public health initiatives focused on promoting healthier dietary choices. Full article

Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that limits the maximum number of clusters is used to cluster the target points of the task. The clustering results are assigned to the multi-robot system using the PSO algorithm based on the distances between the robots and the centers of the clusters, which divides the multi-robot task assignment problem into a multiple traveling salesmen problem. Then, the PSO algorithm is used to optimize the ordering of the task sets in each cluster for the multiple traveling salesmen problem. An experimental verification platform is established by building a simulation and physical experiment platform utilizing the Robot Operating System (ROS). The findings indicate that the proposed algorithm outperforms both the clustering-based market auction algorithm and the non-clustering particle swarm algorithm, enhancing the efficiency of collaborative operations among multiple robots. Full article

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency. Full article

The organic food market’s rapid expansion necessitates an understanding of factors influencing consumer behavior. This paper investigates the impact of packaging colors on perceptions and willingness to pay (WTP) for organic foods, utilizing an experimental auction among university students. Drawing on previous research, we explore how colors influence perceived healthiness, premiumness, trust, and sustainability. The results indicate nuanced responses to different colors, emphasizing the need for businesses to adopt tailored packaging strategies. White and green dominate organic food packaging, aligning with associations of freshness and health. However, the study uncovers varied consumer responses, suggesting a more intricate relationship between color, trust, premiumness, and healthiness perceptions. Demographic factors such as age, gender, income, and residence areas influence WTP for organic foods with different colors, emphasizing the importance of diverse consumer segments in marketing strategies. Trust and perceived premiumness significantly influence WTP, highlighting their pivotal role in consumer valuation. The results highlight that green packaging builds trust among non-organic buyers, while organic buyers are influenced by a broader range of colors that emphasize premiumness and healthiness. The study concludes that businesses in the organic food market should carefully consider color choices in branding and packaging to effectively communicate product qualities and align with consumer values. Full article

Multi-objective optimization problems (MOPs) constitute a vital component in the field of mathematical optimization and operations research. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a MOP into a set of single-objective subproblems and approximates the true Pareto front (PF) by optimizing these subproblems in a collaborative manner. However, most existing MOEA/Ds maintain population diversity by limiting the replacement region or scale, which come at the cost of decreasing convergence. To better balance convergence and diversity, we introduce auction theory into algorithm design and propose an auction-based matching (ABM) mechanism to coordinate the replacement procedure in MOEA/D. In the ABM mechanism, each subproblem can be associated with its preferred individual in a competitive manner by simulating the auction process in economic activities. The integration of ABM into MOEA/D forms the proposed MOEA/D-ABM. Furthermore, to make the appropriate distribution of weight vectors, a modified adjustment strategy is utilized to adaptively adjust the weight vectors during the evolution process, where the trigger timing is determined by the convergence activity of the population. Finally, MOEA/D-ABM is compared with six state-of-the-art multi-objective evolutionary algorithms (MOEAs) on some benchmark problems with two to ten objectives. The experimental results show the competitiveness of MOEA/D-ABM in the performance of diversity and convergence. They also demonstrate that the use of the ABM mechanism can greatly improve the convergence rate of the algorithm. Full article

As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes. Full article

This study addresses the multi-item, multi-sourcing supplier-selection and order-allocation problem. We propose an iterative procurement combinatorial auction mechanism that aims to reveal the suppliers’ minimum acceptable selling prices and assign orders optimally. Suppliers use a flexible bidding language to submit procurement bids. The buyer solves a Mixed Integer Non-linear Programming (MINLP) model to determine the winning bids for the current auction iteration. We introduce a buyer’s profit-improvement factor that constrains the suppliers to reduce their selling prices in subsequent bids. Moreover, this factor enables the buyer to strike a balance between computational effort and optimality gap. We develop a separate MINLP model for updating the suppliers’ bids while satisfying the buyer’s profit-improvement constraint. If none of the suppliers can find a feasible solution, the buyer reduces the profit-improvement factor until a pre-determined threshold is reached. A randomly generated numerical example is used to illustrate the proposed mechanism. In this example, the buyer’s profit improved by as much as 118% compared to a single-round auction. The experimental results show that the proposed mechanism is most effective in competitive environments with several suppliers and comparable costs. These results reinforce the importance of fostering competition and diversification in a supply chain. Full article

Japan’s susceptibility to and severity of floods have necessitated flood control policies by the government. “Overflowing flood control”, in which the floods due to torrential rains are systematically diverted to agricultural lands in the upper to middle reaches, is one of them. More information is needed on the public assessment of the overflowing flood control policy, and this research seeks to bridge this gap. Data evaluating rice affected by the policy were collected from a random nth-price auction using a developed online system. The sample consisted of 47 consumers living in the downstream areas of the Edogawa River, one of Japan’s first-class, or prime, rivers. Data on their attitudes toward the policy were collected with a questionnaire. Multiple ordered probit models are used for regression analysis. The results show that the sample respondents were willing to pay an average of JPY 1578 for 5 kg of rice, slightly higher than the national average rice production cost, and that 36% of the sample agreed with the flood control policy, which is positively associated with large families or owning many assets. Full article