Double Auction Model Research Articles

A novel supply-demand matching model for shared manufacturing resources

ABSTRACT Shared manufacturing is an innovative application of the sharing economy in manufacturing field, aiming to optimize capacity utilization and improve the efficiency of supply-demand matching. The supply-demand matching of manufacturing resources is a key issue on the shared manufacturing platform. In practice, shared manufacturing is proposed based on the objective of optimizing capacity utilization of idle manufacturing resources. In addition, multi-attributes on manufacturing resources are negotiated simultaneously, such as price, quality, and delivery time. However, the existing supply-demand matching models neither consider the optimization objective of capacity utilization nor multi-attribute negotiation requirements. The objective of this research is to propose a novel supply-demand matching model for the shared manufacturing resources, including a double auction-based supply-demand matching relationship determination and a multi-attribute negotiation-based transaction detail determination. In the first stage, a double auction model of manufacturing resources is proposed to determine the matching relationship between provider and demanders. In the proposed double auction model, utility functions for both parties are constructed to express their personalized preferences on bidding attributes; bidding strategies for both parties are determined to generate bidding values; a winner determination model is proposed to determine the matched provider and demander of manufacturing resources maximizing social welfare and capacity utilization. In the second stage, a multi-attribute negotiation model is developed to determine the concrete deal information of manufacturing resources, such as transaction prices, quality, and delivery time. Finally, the feasibility and efficiency of the proposed model is verified through numerical examples.

A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.

EWA‐DA‐based resource allocation method for 5G networks

AbstractIn 5G networks, most D2D work focuses on optimizing system throughput without addressing economic constraints. This article encourages users to exchange resources and apply honest bidding tactics to attain high auction efficiency. This study describes an experience‐weighted attraction double auction (EWA‐DA) technique for allocating resources in 5G D2D networks. The suggested method classifies mobile users into seller cellular users (SCU) and buyer cellular users' (BCU). The SCU transmits the beacon packet, and the nearby cellular users create a D2D group in response to the beacon considered BCU. Then, the bid values of the buyer and seller cellular users are transmitted to the base station side. The BS uses an experience‐weighted attraction‐learning model to obtain accurate bidding prices. Lastly, an ask‐to‐mean‐value double auction model was utilized instead of accurate bid values. Extensive simulation results demonstrate that the suggested strategy delivers excellent auction efficiency and fair earnings for both seller and buyer users. In addition, we identified that the participating users accomplished truthful bidding and used fewer resource blocks than existing techniques.

A truthful dynamic combinatorial double auction model for cloud resource allocation

Dynamic auction-based resource allocation models require little global price information, are decentralized and suitable for the distributed systems like cloud computing. For the cloud computing market, we proposed a Truthful Dynamic Combinatorial Double Auction (TDCDA) model to improve the social welfare and resource utilization. In our model, multiple cloud service providers and cloud users bid for various resources in a dynamic environment. We adopted a payment scheme to ensure truthfulness for all participants, which motivates bidders to reveal their true preferences. Since the combinatorial auction allocation with goal of economic efficiency is NP-hard, we developed a greedy mechanism to achieve the approximately efficient solution. Considering both parties’ interests and the resource scarcity, this model also ensures fairness and balances resource allocation. The proposed model is proven to be approximately efficient, incentive compatible, individually rational and budget-balanced. Simulation results show that the model not only achieves economic efficiency, but also improves resource allocation and meets resource needs for more cloud users.

A Dynamic Combinatorial Double Auction Model for Cloud Resource Allocation

A Dynamic Combinatorial Double Auction Model for Cloud Resource Allocation

A truthful and budget-balanced double auction model for resource allocation in cloud computing

A truthful and budget-balanced double auction model for resource allocation in cloud computing

A multi-attribute double auction and bargaining model for emergency material procurement

Emergency material procurement (EMP) is not only a significance segment of the emergency preparedness, but also an important guarantee for disaster relief. The emergency materials have the different quality attributes towards ordinary commodities, so attribute requirements need to be considered in the procurement activity. This research designed a two-stage MADA-B mechanism to study the supply-demand matching of emergency materials with multiple attributes. In the first stage, considering the differentiated needs of multiple buyers (the government and non-governmental organizations) and the differentiated capacity of multiple suppliers, a multi-attribute double auction (MADA) model was constructed. We analyzed the benchmark bidding strategy of buyers and sellers, and then proposed a winner determination model whose objective is maximizing the total bid-ask spreads. In the second stage, considering the buyer's and the seller's discount rate as private information, an infinite bargaining model was established. We analyzed the bargaining process and gave the method to determine the transaction price and quantity. After that, we designed a numerical example to simulate the practice under the background of purchasing multi-attribute emergency ration package (ERP). The research shows that the auction-bargaining mechanism can not only match the attribute between buyers and sellers, but also can scientifically determine the transaction price and quantity according to the wishes of both sides. It has good feasibility and applicability in the emergency management practice. • A multi-attribute double auction and bargaining mechanism is proposed. • It aims to solve the participant matching problem in emergency material procurement. • It can reach an equilibrium on attribute matching, transaction price and quantity. • The strategies of participants in both auction and bargaining stages are given. • The bargaining has no period restriction and is under incomplete information.

Digital Twin Consensus for Blockchain-Enabled Intelligent Transportation Systems in Smart Cities

Digital Twin (DT) has become the key technology in the Intelligent Transportation Systems (ITS) in smart cities to keep the health and reliability of various DT requesters, such as private vehicles, public transportation, energy systems, etc. The combination of DT and ITS can further release the potential of participants in smart cities and guarantee their efficiency and reliability. Despite the advantages of DT-enabled ITS, not all requesters need the same level of DT service due to the highly dynamic nature of ITS. Safe and reliable matching between DT and ITS still needs to be resolved. To address these issues, we propose the blockchain-enabled Digital Twin as a Service (DTaaS) for ITS. First, we propose an on-demand DTaaS architecture to fully utilize the sensing capabilities of ITS and the macro perspective of DT. Second, a double-auction model and a price adjustment algorithm are proposed to realize the optimal DT matching for ITS requesters and ensure the benefits of participants. Third, a permissioned blockchain and a novel DT-DPoS consensus mechanism are established to enhance the security and efficiency of DTaaS. Simulation shows that the proposed DTaaS and double-auction can efficiently stimulate and facilitate DT transactions. The proposed DT-DPoS also has obvious advantages.

Last-mile drone delivery combinatorial double auction model using multi-objective evolutionary algorithms

This study proposes a combinatorial double auction bi-objective winner determination problem for last-mile delivery using drone. Prior studies are limited on solving mixed integer model, which are not efficient for large-scale scenario. However, this is not practical in real cases as the computation time to obtain the solution is longer due to number of combinations of packages and participants anticipated in the last-mile delivery platform. Four multi-objective evolutionary algorithms (MOEAs) with the decomposed winner determination problem model are experimented. This study is able to yield Pareto optimal solutions from multiple runs of mixed linear integer programming (MILP) using different objectives weights in the model. Unmanned aerial vehicle, or drone, has potential to reduce cost and save time for last-mile logistic operations. The result positively shows MOEAs are more efficient than MILP in yielding a set of feasible solutions for undertaking complex winner determination problem models. The percentage of improvement in terms of time spent identifying the best option is almost 100%. This is likely an unprecedented research in drone where combinatorial double auction is applied to complex drone delivery services and MOEAs are used to solve the associated winner determination problem model.

Efficient Allocations in Double Auction Markets

This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, double auction clears with zero trades precisely when the agents’ current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith’s “invisible hand” in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations.

STMTO: A smart and trust multi-UAV task offloading system

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory. Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

Efficient dynamic resource provisioning based on credibility in cloud computing

Cloud Computing is a growing technology in which resources are provided as a service. The efficiency in providing the resources as a service depends on various factors. One of the major concerns is the suitable allocation of resources to the job. Hence, this paper introduces an Auction based model (CMCDA) for selecting the best customer-providers pairs based on credibility for resource provisioning in cloud computing environment. The Credibility based Multi-attribute Combinative Double Auction (CMCDA) model reduces the complexity in providing the resources for the execution of jobs and fulfill the expectations of both the customers and providers in cloud computing environment. The model also finds the best customer-providers pairs in an efficient way by calculating the credibility values before the resource provision. The highest credibility values pairs are selected as the best customer-providers pairs in the list. Here, the credibility value represents the level of customers and providers satisfaction. The time complexity of the proposed CMCDA algorithm is O(nlog(n)), Since the algorithm only goes through the sorted bid and tries to match them, being executed at most l(n + m) times. The performance of the proposed CMCDA is compared with the Combinatorial Double Auction Resource Allocation (CDARA) model which is the existing cloud double auction model, using CloudAuction simulator. The experimental results demonstrate that the proposed CMCDA performs efficiently than the existing CDARA model for resource provisioning in cloud environment.

A cloud priority-based dynamic online double auction mechanism (PB-DODAM)

Double auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.

A feedback-based combinatorial fair economical double auction resource allocation model for cloud computing

Resource provisioning by cloud computing is one of the emerging fields for providing computing services anywhere at any time without physical barriers. It not only reduces the cost of set-up and maintenance of infrastructure at the on-site level but also helps in optimal resource utilization and reduction of e-waste generation to save the environment. It provides versatile resources to run different types of applications efficiently without worrying about delay or failure. Auction emerges as one of the most promising pricing schemes for the provisioning of resources in cloud computing. Providers and customers have different mind-set. The providers desire to earn more profit and lead the market, while customers prefer economical rates with high quality of service for the resources. In the virtual world, the providers’ genuineness is crucial, and the feedback from the customers plays a vital role that quantifies this genuineness.This paper proposes a Feedback-based Combinatorial Fair Economical Double Auction Resource Allocation Model (FCFEDARA) that investigates the genuineness of providers based on the offered prices for the required resources along with the feedback given by the customers. The proposed framework provides a common platform for the customers to access resources from different providers at optimal prices, as well as prioritizes genuine providers with good feedback over in-genuine providers with bad feedback. In the combinatorial double auction model, customers and providers submit their bundle bid along with a list of resources in the auction. The proposed model takes care of the truthfulness of the providers, penalizes market spoilers, and gives priority to those providers who have good customer feedback. Three different cases with their implications have analyzed, and evaluated based on the bid, configuration of resources, and customer feedback. Case 1 focuses on feedback given by the customers. In Case 2 and Case 3, providers’ genuineness, as well as their feedback from the customers, are key factors to determine winner providers. It is in contrast with earlier models where genuineness is based only on the bid and configuration of resources offered by providers. The proposed model was simulated on the CloudSim simulator, a Java-based simulator that provides a cloud computing environment, and results are evaluated and compared with two popular existing models: CDARA, its variant model, and the author’s previous approach (CFEDARA). The experimental results and the graphs have clearly shown the promotion of genuine providers with good feedback, fair allocation with economical pricing while maintaining the overall profit of the system. The participation ratio of customers and providers is maintained to sustain demand and supply in the virtual market.

A cloud dynamic online double auction mechanism (DODAM) for sustainable pricing

The Cloud is a new computing paradigm that has experienced exponential growth in recent years. Therefore, success in the cloud services industry can be a challenge, as satisfying both cloud service users and providers is challenging. Cloud users expect to receive a higher service quality with the lowest possible prices. On the other hand, cloud service providers need to maximize their revenues by utilizing energy-efficient resources while adopting prices that attract the most customers. As such, the success of a business in cloud computing depends on the equal consideration of cloud service providers’ and users’ needs. This paper proposes a long-term organic solution based on a double auction model that encompasses the varying natures of both strategic players and cloud resources. We develop the dynamic online double auction mechanism (DODAM) and coin the concept of sustainable pricing for cloud computing services. A dynamic online platform that is capable of realistically modelling cloud ecosystems distinguishes the DODAM model from other studies using a double auction mechanism. Based on experimental results, we prove that the proposed model outweighs other double auction models in terms of the social welfare and truthfulness pillars.

Efficient Allocations in Double Auction Markets

This paper proposes a simple descriptive model of discrete-time double auction markets for divisible assets. As in the classical models of exchange economies, we consider a finite set of agents described by their initial endowments and preferences. Instead of the classical Walrasian-type market models, however, we assume that all trades take place in a centralized double auction where the agents communicate through sealed limit orders for buying and selling. We find that, under nonstrategic bidding, the double auction clears with zero trades precisely when the agents' current holdings are on the Pareto frontier. More interestingly, the double auctions implement Adam Smith's invisible hand in the sense that, when starting from disequilibrium, repeated double auctions lead to a sequence of allocations that converges to individually rational Pareto allocations.

Double Auction Mechanisms For Dynamic Autonomous Electric Vehicles Energy Trading

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced.

A double auction based mathematical market model and heuristics for internet-based secondhand durable good markets

Trading of used goods in secondhand markets is considered to have both environmental and economic benefits. For the trading of used goods as well as new ones, this study proposes a periodic double auction model for Internet-based electronic markets whereby each bidder can place both asks and bids for selling and purchasing goods respectively during a trading period. The proposed model allows each bidder to place a spending limit order such that the difference between the cost of the purchased goods and the income obtained from the sold goods does not exceed this limit. Furthermore, since a bidder may be indifferent to multiple goods, e.g. copies of a same title, the model also provides a mechanism so that bidders may combine a number of bids inside a set and put a limit on the number of goods to be purchased in this set. The model is mathematically defined, and the corresponding winner determination problem is formulated using linear integer programming. Since the problem is shown to be NP-hard, a number of heuristic methods are also proposed. Performances of these methods are evaluated on a comprehensive test suite and statistical analyses of the results are presented. Furthermore, possible economical contribution of the model is also evaluated. The results indicate that the proposed model can be used efficiently in large-scale markets with tens of thousands of bidders.

Auction models with resource pooling in modern supply chain management

Purpose In this paper, the main focus is on supply and demand auction systems with resource pooling in modern supply chain from a theoretical modeling perspective. The supply and demand auction systems in modern supply chains among manufacturers and suppliers serve as information sharing mechanisms. The purpose of this paper is to match the supply and demand such that a modern supply chain can achieve incentive compatibility and economic efficiency. The authors design such a supply and demand auction system that can integrate resources to efficiently match the supply and demand. Design/methodology/approach The authors propose three theoretic models of modern supply chain auctions with resource pooling according to the Vickrey auction principle. They are supply auction model with demand resource pooling, demand auction model with supply resource pooling, and double auction model with demand and supply resource pooling. For the proposed auction models, the authors present three corresponding algorithms to allocate resources in the auction process by linear programming, and study the incentive compatibility and define the Walrasian equilibriums for the proposed auction models. The authors show that the solutions of the proposed algorithms are Walrasian equilibriums. Findings By introducing the auction mechanism, the authors aim to realize the following three functions. First is price mining: auction is an open mechanism with multiple participants. Everyone has his own utility and purchasing ability. So, the final price reflects the market value of the auction. Second is dynamic modern supply chain construction: through auction, firm can find appropriate partner efficiently. Third is resources integration: in business practices, especially in modern supply chain auctions, auctioneers can integrate resources and ally buyers or sellers to gain more efficiency in auctions. Originality/value In the paper, the authors propose three theoretic models and corresponding algorithms of modern supply chain auctions with resource pooling according using the Vickrey auction principle, which achieves three functions: price mining, dynamic modern supply chain construction and resources integrating. Besides, these proposed models are much closer to practical settings and may have potential applications in modern supply chain management.

An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud‐edge computing

SummaryCrowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud‐edge computing is designed in this paper. Technically, a double action model under the cloud‐edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism.