Combinatorial Reverse Auction Research Articles

A Truthful Combinatorial Reverse Auction Mechanism for Crowdshipping

Crowdshipping is an emerging logistics paradigm that leverages excess capacities of occasional couriers to transport goods. This paper investigates the problem that a crowdshipping platform recruits occasional couriers to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational and has high computation efficiency.

Two-stage stochastic nonlinear winner determination for logistics service procurement auctions under quantity discounts

Quantity discount is a frequently adopted scheme that has not been explicitly investigated in logistics service procurement auctions. This paper focuses on a revised winner determination problem under quantity discounts and demand uncertainty for a fourth-party logistics (4PL) provider in a combinatorial reverse auction. To characterize our research problem, a two-stage stochastic nonlinear programming model is constructed. Inspired by the idea of sample average approximation (SAA), the nonlinear model is reformulated as a deterministic mixed integer linear programming model by using a linearization technique with superior expressions. Since the reformulation has a large number of decision variables and constraints, we integrate SAA with a dual decomposition Lagrangian relaxation technique (DDLR) to develop a solution method called SAA-DDLR. Numerical experiments are conducted to illustrate the effectiveness and applicability of our model and method. Sensitivity analysis reveals that both the 4PL and 3PLs can benefit from the quantity discount scheme. Managerial insights are drawn for the 4PL to run a cost-effective logistics system in the presence of quantity discounts.

Optimisation models for the procurement through reverse combinatorial auctions in the logistics and food industries

Optimisation models for the procurement through reverse combinatorial auctions in the logistics and food industries

SEAL: A Strategy-Proof and Privacy-Preserving UAV Computation Offloading Framework

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

An enhanced approach for solving winner determination problem in reverse combinatorial auctions

<span>When a disaster occurs, the single agent does not have complete knowledge about the circumstances of the disaster. Therefore, the rescue agents should coordinate with each other to perform their allocated tasks efficiently. However, the task allocation process among rescue agents is a complex problem, which is NP-complete problem, and determining the rescue agents that will perform the tasks efficiently is the main problem, which is called the winner determination problem (WDP). This paper proposed an enhanced approach to improve rescue agents’ tasks allocation processes for WDP in reverse combinatorial auctions. The main objective of the proposed approach is to determine the winning bids that will perform the corresponding tasks with minimum cost. The task allocation problem in this paper was transformed into a two-dimensional array, and then the proposed approach was applied to it. The main contribution of the proposed approach is to shorten the search space size to determine the winners and allocate the corresponding tasks for a combination of agents (i.e., more than two agents). The proposed approach was compared to the genetic algorithm regarding the execution time, and the results showed good performance and effectiveness of the proposed approach.</span>

Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework

Combinatorial auctions have seen limited applications in large-scale consumer-oriented marketplaces, partly due to the substantial complexity to keep track of auction status and formulate informed bidding strategies. We study the bidder support problem for the general multi-item multi-unit (MIMU) combinatorial auctions, where multiple heterogeneous items are being auctioned and multiple homogeneous units are available for each item. Under two prevalent bidding languages (OR bidding and XOR bidding), we derive theoretical results and design efficient algorithmic procedures to calculate important bidder support information, such as the winning bids of an auction and the minimum bidding value for a bid to win an auction either immediately or potentially in the future. Our results unify the theoretical insights on bidder support problem for different bidding languages as well as different special cases of general MIMU auctions, namely the single-item multi-unit (SIMU) auctions and the multi-item single-unit (MISU) auctions. We also consider auctions with additional bidding constraints, including batch-based combinatorial auctions and hierarchical combinatorial auctions, as well as the combinatorial reverse auctions, all of which have relevant practical applications (e.g., industrial procurements). Our results can be readily extended to solve the bidder support problems in these auction mechanisms.

A Privacy-Preserving Incentive Mechanism for Data Offloading in Satellite-Terrestrial Crowdsensing

Data offloading algorithm is the foundation of urban Internet of Things, which has gained attention for its large size of user engagement, low cost, and wide range of data sources, replacing traditional crowdsensing in areas such as intelligent vehicles, spectrum sensing, and environmental surveillance. In data offloading tasks, users’ location information is usually required for optimal task assignment, while some users in remote areas are unable to access base station signals, making them incapable of performing sensing tasks, and at the same time, there are serious concerns about users’ privacy leakage about their locations. Until today, location protection for task assignment in data offloading has not been well explored. In addition, existing privacy protection algorithms and data offloading task assignment mechanisms cannot provide personalized protection for different users’ privacy protection needs. To this end, we propose an algorithm known as differential private long-term privacy-preserving auction with Lyapunov stochastic theory (DP-LAL) for data offloading based on satellite-terrestrial architecture that minimizes the total payment. This not only gives an approximate optimal total payment in polynomial time but also improves the issue of poor signal in remote areas. Meanwhile, satellite-terrestrial data offloading architecture integrates wireless sensor networks and cloud computing to provide real-time data processing. What is more, we have considered long-term privacy protection goals. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. More importantly, we use Lyapunov optimization theorem to jointly optimize queue stability and total payment. We prove that our algorithm is of high efficiency in computing and has good performance in various economic attributes. For example, our algorithms are personally rational, budget-balanced, and true to the buyer and seller. We use large-scale simulations to evaluate the proposed algorithm, and compare our algorithm with existing algorithms, our algorithm shows higher efficiency and better economic properties.

An incentive mechanism based on endowment effect facing social welfare in Crowdsensing

Mobile CrowdSensing(MCS) is a new type of network which needs a large number of users to collect data to complete the sensing task and requires users to have a high level of participation. In the current research, the incentive purpose is mainly achieved by paying certain rewards to service providers. However, due to the demand for data quality and quantity, platforms often have high consumption. Most of the current studies assume that the evaluation of an item by a node is not affected by its ownership status. But behavioral economics points out that because of the existence of endowment effect, the evaluation of the value of an item by a node is greater when it is owned than when it is not owned, thereby affecting the value evaluation strategy of the node. Therefore, inspired by the phenomenon that enterprises use share dividends to stimulate endowment effect of employees in daily life and thus motivate employees to maintain loyalty, the paper constructs a mapping relationship between it and MCS and designs a Reverse Combinatorial Auction Based Endowment Effect (RCBEE) incentive mechanism. The paper designs endowment assets and assigns them to selected nodes according to the relationship between multiple coefficients of nodes, so as to introduce the endowment effect. The paper analyzes the change of the node on the evaluation value of endowment assets, changes the node income, and reconstructs the income matrix. On the one hand, the RCBEE mechanism continuously strengthens the node’s endowment intensity by setting the holding period; on the other hand, it introduces the contribution threshold to trigger the node’s endowment effect, so that users can maximize the incentive goal of social welfare by reducing the bid price or completing more tasks. Simulation experiments show that, compared with the traditional incentive mechanism, RCBEE reduces the payment cost and increases the social welfare.

Fog-Integrated Cloud Architecture enabled multi-attribute combinatorial reverse auctioning framework

Fog computing is an emerging service-oriented market in conjunction with Cloud computing to fulfill the resource demand of mobile users as well as IoT users for real-time applications. Auctioning in Fog computing is highly challenging due to mobility, dynamic pricing, real-time demand in comparison to Cloud based auctioning models. Further, due to users’ mobility and limited Fog resources, existing reverse auction techniques developed for Cloud computing model cannot directly be applied for the resource procurement in Fog-Integrated Cloud Architecture (FICA). Therefore, a reverse auction-based model which includes customer, auctioneer, Fog provider, Cloud provider, and Fog & Cloud provider together as auction participants, is proposed in this work. The proposed model, for resource provisioning using a multi-attribute combinatorial reverse auction, is named as Fog-Integrated Cloud Auctioning Model (FICAM). FICAM pricing scheme includes three types of resources depending on their requirement i.e., local Fog, remote Fog, and Cloud. A truthful, robust, and fair algorithm for resource allocation is proposed considering response time, data source mobility requirements, and Fog resource limitations. To encourage providers to bid truthfully, the Vickrey model is extended. FICAM also introduces a new algorithm for resource procurement in which instead of giving all resources of the bundle, only the required resources at a time are given to the customer with the bundle discount. The discount is based on a certain threshold in the ratio of the availed amount of resources to the offered amount of resources. Rigorous experimentation exhibits that the proposed model offers a low resource procurement cost in polynomial time as compared to other state of the art algorithms.

Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure.

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate.

Optimization Models for the Procurement through Reverse Combinatorial Auctions in the Logistics and Food Industries

Optimization Models for the Procurement through Reverse Combinatorial Auctions in the Logistics and Food Industries

A two-stage stochastic winner determination model integrating a hybrid mitigation strategy for transportation service procurement auctions

Disruption is one of the important and challenging factors in transportation service procurement auctions. Existing winner determination models tend to ignore bidders’ disruption risks and its dire consequences. This paper particularly studies a revised winner determination problem with disruption risks of bidders for a fourth party logistics (4PL) provider to purchase transportation services via combinatorial reverse auctions. Integrating a hybrid mitigation strategy that includes fortification, reservation and outside option policies to deal with disruptions, a new two-stage stochastic winner determination model is constructed. Based on the characteristics of the deterministic equivalent reformulation, a scenario-based approximation approach is developed as solution method. An upper bound can be obtained by using a scenario reduction approach, and a lower bound can be derived by employing a problem-based relaxation method or an efficient dual decomposition Lagrangian relaxation approach. Numerical experiments are conducted to illustrate the effectiveness and applicability of the proposed model and method, since the gap between the upper bound and lower bound is small. Comparison analysis indicates that our strategy is better than some known strategies, and would have a more significant influence on cost minimization of the 4PL as the probability of disruption becomes higher. Managerial implications are drawn for 4PLs to provide high quality of transportation services under disruptions.

Fast core pricing algorithms for path auction

Path auction is held in a graph, where each edge stands for a commodity and the weight of this edge represents the prime cost. Bidders own some edges and make bids for their edges. The auctioneer needs to purchase a sequence of edges to form a path between two specific vertices. Path auction can be considered as a kind of combinatorial reverse auctions. Core-selecting mechanism is a prevalent mechanism for combinatorial auction. However, pricing in core-selecting combinatorial auction is computationally expensive, one important reason is the exponential core constraints. The same is true of path auction. To solve this computation problem, we simplify the constraint set and get the optimal set with only polynomial constraints in this paper. Based on our constraint set, we put forward two fast core pricing algorithms for the computation of bidder-Pareto-optimal core outcome. Among all the algorithms, our new algorithms have remarkable runtime performance. Finally, we validate our algorithms on real-world datasets and obtain excellent results.

Joint Scheduling and Incentive Mechanism for Spatio-Temporal Vehicular Crowd Sensing

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers' mobile devices equipped with on-board sensors for various urban sensing tasks. Because of the importance of ensuring satisfactory spatio-temporal sensing coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize the task completion rate under drivers' traveling constraints. Different from prior work, we propose Hector, a joint trajectory scheduling and incentive mechanism for spatio-temporal UVCS systems, which concentrates on capturing the interactive effects between scheduling and incentive mechanisms. Technically, we first reduce the dimensions of the original scheduling problem by mapping it into an augmented set cover problem with spatio-temporal constraints. Then, based on reverse combinatorial auctions, we design Hector, whose incentive mechanism with the presence of uncertain future trajectory information makes scheduling and compensation decisions in real-time. Specifically, Hector is truthful, individual rational and computationally efficient. Furthermore, the social cost yielded by Hector is close-to-optimal, and the approximation ratio is Hm. The advantageous properties of Hector are verified by both rigorous theoretical analysis and extensive simulations based on the real world datasets in the Chinese city Shenzhen which consists of 726,000 taxi trajectories.

A Truthful and Fair Multi-Attribute Combinatorial Reverse Auction for Resource Procurement in Cloud Computing

Resource procurement using reverse auction in Cloud computing is an interesting but a complex problem as it involves many attributes and constraints. Reverse auction is a mechanism in which a customer prepares call for proposal of resource requirement and publicizes it in order to get the attention of eligible service providers. This work proposes a multi-attribute combinatorial reverse auction for Cloud resource procurement which considers price as well as non-price attributes such as quality of service parameters, reputation etc. in the determination of winning service providers. For this, the problem is formulated using approximation algorithm and near optimal solution is obtained in polynomial time. Auction mechanism allows providers to reveal true information in order to maximize their profit. It also imposes a penalty on the providers who cheat i.e., do not offer the agreed upon services. This makes the system robust as it maintains the utility of the customer. It also maintains a healthy competition among the providers. Performance evaluation and a comparative study with some base line models exhibit that the proposed method performs better.

A mixed procurement model for humanitarian relief chains

Purpose The purpose of this paper is to improve the relief procurement process as one of the most important elements of humanitarian logistics. For doing so, a novel two-round decision model is developed to capture the dynamic nature of the relief procurement process by allowing demand updating. The model accounts for the supply priority of items at response phase as well. Design/methodology/approach A mixed procurement/supply policy is developed through a mathematical model, which includes spot market procurement and a novel procurement auction mechanism combining the concepts of multi-attribute and combinatorial reverse auctions. The model is of bi-objective mixed-integer non-linear programming type, which is solved through the weighted augmented e-constraint method. A case study is also provided to illustrate the applicability of the model. Findings This study demonstrates the ability of proposed approach to model post-disaster procurement which considers the dynamic environment of the relief logistics. The sensitivity analyses provide useful managerial insights for decision makers by studying the impacts of critical parameters on the solutions. Originality/value This paper proposes a novel reverse auction framework for relief procurement in the form of a multi-attribute combinatorial auction. Also, to deal with dynamic environment in the post-disaster procurement, a novel two-period programming model with demand updating is proposed. Finally, by considering the priority of relief items and model’s applicability in the setting of relief logistics, post-disaster horizon is divided into three periods and a mixed procurement strategy is developed to determine an appropriate supply policy for each period.

Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers’ quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.

Core-Selecting Auctions for Autonomous Vehicle Public Transportation System

With the technological advancements, the autonomous vehicle (AV) is expected to play an active role in the future transportation system. An AV-based public transportation system was recently proposed to unleash the full capability of AVs to provide effective and flexible transportation services. It establishes a new public transportation market that can accommodate multiple vehicle operators. Such multi-tenant system encourages market competition for better quality of service. The pricing process in the original proposal was designed based on the Vickrey–Clarke–Groves mechanism, but it is vulnerable to the problems of payoff reduction and shill-bidding. In this paper, we reinvestigate the pricing process to address these vulnerabilities. We formulate it as core-selecting reverse combinatorial auctions and investigate the properties of the “core.” We establish a core-selecting mechanism that can maximize customer's utility and prevent shill-bidding. We analyze the theoretical properties of the formulated auctions and the core-selecting mechanism. We verify the results with extensive simulations. The simulation results show that the core-selecting mechanism can result in lower service charge, and suppress untruthfulness, shill-bidding, and coalition formation. It can produce auction results in linear computation time, making it scalable and practical.

Mobile Network Operator and Mobile User Cooperation for Customized D2D Data Services

The explosive growth of smart devices has led to the evolution of multimedia data (mainly video) services in mobile networks. It attracted many mobile network operators (MNO) to deploy novel network architectures and develop effective economic policies. Mobile data offloading through smart devices (SDs) by exploring device-to-device (D2D) communications can significantly reduce network congestion and enhance quality of service at a lower cost, which is the key requirement of upcoming 5G networks. This reasonable cost solution is useful for attracting mobile users to participate in the offloading process by paying them proper incentives. It is beneficial for MNOs as well as mobile users. Moreover, D2D communications promise to be one of the prominent services for 5G networks. In this paper, we present a combinatorial optimal reverse auction (CORA) mechanism, which efficiently selects and utilizes available high-end SDs on the basis of available resources for offloading purposes. It also decides the optimal pricing policy for the selected SDs. The efficiency of CORA has been realized in terms of bandwidth and storage demand. Subsequently, we implement caching in SDs, eNodeB (eNB), and evolved packet core (EPC) with the help of our novel video dissemination cache update algorithm to solve the latency or delay issues in the offloading process. Due to high popularity, we specifically focus on video data. Simulation results show that the proposed SD caching scenario curtails the delay up to 75% and the combined cache (CC) scenario slashes the delay varying from 15 to 57%. We also scruitinize the video downloading time performance of various cache scenarios (i.e., CC, EPC cache, eNB cache, and SD cache scenarios).

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 non-intermittent 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.