AUCTIONING METHOD FOR AIRSPACE CONGESTING RESOURCE ALLOCATION AND GAME EQUILIBRIUM ANALYSIS

The airspace congestion is becoming more and more severe.Although there are traffic flow management（TFM）initiatives based on CDM widely applied,how to reschedule these disrupted flights of different airlines integrating TFM initiatives and allocate the limited airspace resources to these airlines equitably and efficiently is still a problem.The air traffic management（ATM）authority aims to minimizing the systemic costs of congested airspaces.And the airlines are self-interested and profit-oriented.Being incorporated into the collaborative decision making（CDM）process,the airlines can influence the rescheduling decisions to profit themselves.The airlines maybe hide the flight information that is disadvantageous to them,but is necessary to the optimal system decision.To realize the coincidence goal between the ATM authority and airlines for the efficient,and equitable allocation of airspace resources,this paper provides an auction-based market method to solve the congestion airspace problem under the pre-tactic and tactic stage of air traffic flow management.Through a simulation experiment,the rationing results show that the auction method can decrease the total delay costs of flights in the congested airspace compared with both the first schedule first service（FSFS）tactic and the ration by schedule（RBS）tactic.Finally,the analysis results indicate that if reallocate the charges from the auction to the airlines according to the proportion of their disrupted flights,the auction mechanism can allocate the airspace resource in economy equitably and decrease the delay losses of the airlines compared with the results of the FSFS tactic.

基于拍卖理论的动态多代理同类机调度算法

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents.All agents are self-interested and rational with the aim of maximizing their own objectives,resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information.Within the context,a centralized scheduling approach is unfeasible,and a decentralized approach is considered to deal with the targeted problem.This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’preferences under incomplete information.For this purpose,a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed.In the proposed approach,a dynamic auction procedure is established for dynamic jobs participating in a realtime auction,and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination.In addition,an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently.A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids.Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs.A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Will auctioning promote the renewable energy generation in China?

Renewable energy generation,as part of the global effort to mitigate climate change,will play a central role in reducing greenhouse gas emissions and achieving China's goal of carbon emissions peak before 2030 and carbon neutral before 2060.However,the impact of carbon quota auctions on renewable energy generation has not been sufficiently discussed.The main purpose of this study is to investigate whether China can rely on quota auctions to increase renewable energy generation in the short term,and to demonstrate which is more effective in promoting renewable energy development,policy enforcement or auction constraints?The improved neo-trans-log production model,the multiobjective linear programming model and the dispatch heuristic were used to predict additional emission reduction cost,optimized power mix with different auctioning rates,with economic development,technological progress and the unique characteristics of China's power generation industry being taken into consideration.The results show that the auctioning rate will have little influence on the optimized energy production structure,especially on the share of renewable energy resources;when the total on-grid electricity generation ranges from 7625 to 7926 billion kW h and the auctioning rate ranges from 0% to 5%,policy enforcement will influence the generation of renewable energy to a greater extent than auctioning in the near future.

Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies.To meet the extra demand load,we develop electricity combinatorial reverse auction(CRA)for the purpose of procuring power from diverse energy sources.In this new,smart electricity market,suppliers of different scales can participate,and homeowners may even take an active role.In our CRA,an item,which is subject to several trading constraints,denotes a time slot that has two conflicting attributes,electricity quantity and price.To secure electricity,we design our auction with two bidding rounds:round one is exclusively for variable energy,and round two allows storage and nonintermittent renewable energy to bid on the remaining items.Our electricity auction leads to a complex winner determination(WD)task that we represent as a resource procurement optimization problem.We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity.This solution consists of multiple winning suppliers,their prices,quantities and schedules.We validate our WD approach based on large-scale simulated datasets.We first assess the time-efficiency of our WD method,and we then compare it to well-known heuristic and exact WD techniques.In order to gain an exact idea about the accuracy of WD,we implement two famous exact algorithms for our constrained combinatorial procurement problem.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Electronic Auction Scheme Based on Smart Contract and IPFS

Sealed-bid auctions are a vital transaction tool in the e-commerce field.Traditional centralized auction schemes typically result in severe threats to data integrity,information transparency,and traceability owing to their excessive reliance on third parties,and blockchain-based auction schemes generally suffer from high storage costs and are deficient in functional and architectural design.To solve these problems,this study presents a sealed-bid auction scheme that removes the third-party based on an Ethereum smart contract,ensuring data integrity,openness,and transparency in the execution process.The commitment mechanism and distributed storage system help to significantly reduce the user’s storage cost and protect the privacy of user bids.For the functional design,this study introduces a fulltext-retrieval and dispute-processing module for commodities,which reduces the defects existing in the functional module design of existing auction systems.Furthermore,a prototype auction system on the Ethereum test chain is built to validate the proposed scheme.Experiments show that compared with traditional storage methods,indirect storage based on a distributed storage system of texts and images can reduce the storage cost by at least 50%while ensuring data integrity.Finally,the gas cost at each stage of the auction scheme and the time required for the full-text retrieval of products are recorded to evaluate the scheme performance and analyze the test results.

Cooperative management of an emission trading system:a private governance and learned auction for a blockchain approach

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory,there is little research on using blockchain in carbon emission trading schemes(ETS).This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology.Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security.Considering the augmentation of information interaction during the auction process brought about by blockchain,the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction.In particular,implementing smart contracts can provide a swift and automatic settlement.The simulation results of the proposed system demonstrate the following:(1)fine-grained access is possible with a second delay;(2)the average annual compliance levels increase by 2%when bidders’learning behavior is considered;and(3)the blockchain network can process more than 350 reading operations or 7 writing operations in a second.Novel cooperative management of an ETS platform based on blockchain is proposed.The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision.A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology.This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.

Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells.Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput.The problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in general.The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively.For the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle it.In addition,the DC programming method is adopted to optimize the power allocation subproblem.To decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual method.Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Design of the contingent royalty rate as related to the type of investment

This study investigates the design of the royalty rate in a first-price auction across three types of investments:incremental and lumpy with or without an exogenously given intensity.A bidder’s investment cost comprises private information.This,together with the stochastic evolution of the price of the output generated from the auctioned project,precludes the seller from setting the exact dates of investment with the winner.However,the seller can set the royalty rate to equate the winner’s royalty payment with the winner’s information rent so that the winner acts as if to maximize the seller’s revenue.We derive two main conclusions.First,compared with the case in which investment is lumpy with an exogenously given intensity,the seller can set a lower royalty rate on incremental investment because she can collect additional royalty payments from the winner,who has the option to later expand capacity.Second,the impact of output price uncertainty on the optimal royalty rate for the three types of investments exhibits two different patterns.When investment is either incremental or lumpy with an exogenously given intensity,greater output price uncertainty reduces the royalty rate.When investment is lumpy with variable intensity,greater output uncertainty raises the royalty rate.Our results imply that auctioneers may charge differential royalty rates for different types of investments.

Auction-based profit maximization offloading in mobile edge computing

Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit.In this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ENs.Firstly,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ENs.Thereafter,the bidding willingness of each MD in every round of auction is determined to ensure MD rationality.Furthermore,we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness.Finally,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction.Numerical results verify the performance of the proposed algorithm.Compared with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

遗传算法在多类型的车辆人员排班系统中的应用与研究

公交车辆人员排班的主要问题就是在给定时间点和车次数的情况下,以最小代价覆盖所有的车次。与以往都是针对单类型车辆的人员排班不同,该文主要提供对多类型的车辆人员排班的支持。首先利用高效的Auction算法获取代价最小的车次分组并根据分组情况分配车辆的营运类型;然后使用遗传算法进行随机化搜索以获得最优解。实验表明,遗传算法应用于多类型的公交车辆人员排班具有很好的效果。

不定式作定语时的时间意义

我们大家都知道,不定式作定语时往往表示未来将要发生的事或动作,事情或动作是还未发生的:

基于CPN模型Auction智能合约的形式化验证

区块链智能合约是运行在区块链网络中的代码,它能够根据外部环境条件自动执行相应的规则,完成对应的交易和数字资产的转移.Auction合约是一个公开拍卖的智能合约,广泛应用到竞拍、游戏和博彩等行业,吸引了众多用户参与.近年来,该合约暴露出了拒绝服务攻击漏洞,导致很多参与者无法竞拍成功.为此,本文基于CPN模型对Auction合约进行形式化验证,检测漏洞并确定漏洞位置.首先使用CPN中的建模工具分别对Auction合约整体、无攻击操作和有攻击操作进行建模,然后使用CPN中的仿真工具对合约的执行过程进行仿真.结果表明,通过该方法,不仅可以发现和定位Auction合约的逻辑漏洞,而且也可以发现Auction合约语言的局限性.

最短路径Auction算法及其在路径诱导中的应用

通过采用C#语言程序,对比传统路径算法,并在实际道路网络和随机网络中进行了试验,测试了算法在求解网络"一对一"最短路径问题时的运算性能.结果表明,Auction算法在求解此类问题时,体现算法自身原理的优势,虽然整体性能表现不及经典的路径算法,即迭代步骤略多,但仍有改进的余地.

最短路问题的Auction算法在无圈网络中的改进

提出了Auction算法在无圈网络中的一种改进。在改进的新算法中,采取了新的推进(extension)方式,从而成功地降低了算法的复杂性。改进后算法的复杂性为O(m),此处m是图的弧数。

Commission Strategy of the Auction House

In a SIPV model, when the commission proportion is not certain, but related with bargain price, generally, it is a linear function of the bargain price, this paper gives bidders＇ equilibrium bidding strategies in the first-and secondprice auctions. We find that the equilibrium strategies in second-price auction are dominant strategies. For seller or auction house, whether the fixed proportion or the unfixed proportion is good is not only related with constant item and the linear coefficient of the linear function, the size of the fixed commission proportion, but also related with the value of the item auctioned. So, in the practical auctions, the seller and the auction house negotiated with each other to decide the commission rules for their own advantage.

Quantum Secure Direct Communication and Quantum Sealed-Bid Auction with EPR Pairs

I present a new protocol for three-party quantum secure direct communication （QSDC） with a set of ordered M Einstein-Podolsky-Rosen （EPR） pairs. In the scheme, by performing two unitary operations and Bell state measurements, it is shown that the three legitimate parties can exchange their respective secret message simultaneously. Then I modify it for an experimentally feasible and secure quantum sealed-bid auction （QSBD） protocol. Furthermore, I also analyze th^ecurity of the protocol, and the scheme is proven to be secure against the intercept-and-resend attack, the disturbancb attack and the entangled-and-measure attack.

Multi-robot task allocation for exploration

The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.