Tractable combinatorial auctions and b-matching

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User acceptance of complex electronic market mechanisms: Role of information feedback

In re-entrant flow shop, an optimal resource allocation becomes more difficult as the number of product types, equipment, and repetition of processes increases. Therefore, there is need to consider a new robust method to resolve this resource allocation problem. In this paper, we propose an optimization method that uses combinatorial auction to search solution space efficiently. The proposed method is a high-speed method based on a bidder’s utility to solve the resource allocation problem in practical calculating time. Here, the utility means the levels on degree of content obtained by bids and is used to make the neighborhood efficiently in the combinatorial auction. We have applied this method to two objective problems and have obtained quasi-optimal solutions. Computation time of a model has reduced from 56 to 77 % and that of the other has reduced 91 %. This indicates that a combinatorial auction with utility is efficient method to search solution space.

Online auctions, including online Combinatorial Auctions, are important examples of e-commerce applications. In this paper, a Combinatorial Auction Web Platform (CAWP) is introduced. The platform enables both product selling and buying capabilities that can be realized in a combinatorial way. CAWP supports a Sealed-Bid Single-Unit type of Combinatorial Auctions. Easy customization for any selected problem domain is a distinguished feature of CAWP. Platform users are not expected to have any technical knowledge about how to solve the Winner Determination Problem (WDP) known to be critical for profit maximization of the auctioneers in Combinatorial Auctions.

The use of a combinatorial auction is believed to be an effective way to distribute spectrum to buyers who have diversified valuations for different spectrum combinations. However, the allocation of spectrum with combinatorial auctions mainly aims at optimizing over certain utility functions, e.g., social welfare, but ignores individual preferences of buyers and sellers, who have incentives to deviate from globally optimal allocation results to improve their own utility. In this paper, we explore the possibility of designing a new stable matching algorithm for combinatorial spectrum allocations. Starkly different from existing efforts on spectrum matching mechanism design, our proposed combinatorial spectrum matching framework not only allows buyers to express preferences towards spectrum combinations (rather than individual channels), but also computes the payment that should be transferred from buyers to sellers. Payment determination, while essential in spectrum exchange, has never been addressed in existing spectrum matching frameworks. We design a novel algorithm to achieve a stable combinatorial spectrum matching and to compute the corresponding payment profiles. We conducted an extensive array of experiments to compare the performance of stable combinatorial spectrum matching with spectrum auctions. It is shown that the combinatorial spectrum matching sacrifices little allocation efficiency in terms of social welfare and spectrum utilization, but achieves a much higher individual buyer utility, which will incentivize buyers to participate and comply with the allocation results.

Combinatorial auctions are multiple-item auctions in which bidders may place bids on any package (subset) of goods. This additional expressibility produces benefits that have led to combinatorial auctions becoming extremely important both in practice and in theory. In the computer science community, auction design has focused primarily on computational practicality and incentive compatibility. The latter concerns mechanisms that are resistant to bidders misrepresenting themselves via a single false identity; however, with modern forms of bid submission, such as electronic bidding, other types of cheating have become feasible. Prominent amongst them is false-name bidding; that is, bidding under pseudonyms. For example, the ubiquitous Vickrey-Clarke-Groves (VCG) mechanism is incentive compatible and produces optimal allocations, but it is not false-name-proof–bidders can increase their utility by submitting bids under multiple identifiers. Thus, there has recently been much interest in the design and analysis of false-name-proof auction mechanisms. These false-name-proof mechanisms, however, have polynomially small efficiency guarantees: they can produce allocations with very low economic efficiency/social welfare. In contrast, we show that, provided the degree to which different goods are complementary is bounded (as is the case in many important, practical auctions), the VCG mechanism gives a constant efficiency guarantee. Constant efficiency guarantees hold even at equilibria where the agents bid in a manner that is not individually rational. Thus, while an individual bidder may personally benefit greatly from making false-name bids, this will have only a small detrimental effect on the objective of the auctioneer: maximizing economic efficiency. So, from the auctioneer's viewpoint the VCG mechanism remains preferable to false-name-proof mechanisms.

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

This paper proposes a method which is to achieve an optimal scheduling and stock allocation planning simultaneously using a combinatorial auction under mass customization system. Proposal method decides a production scheduling and material stock allocation planning to aim minimizing the delay from acceptable delivery time. A numerical example is then discussed to demonstrate its suitability in obtaining an optimal plan under negotiations between manufacturer and customers.

The Submodular Welfare Maximization problem (SWM) captures an important subclass of combinatorial auctions and has been studied extensively from both computational and economic perspectives. In particular, it has been studied in a natural online setting in which items arrive one-by-one and should be allocated irrevocably upon arrival. In this setting, it is well known that the greedy algorithm achieves a competitive ratio of 1/2, and recently Kapralov et al. (2013) showed that this ratio is optimal for the problem. Surprisingly, despite this impossibility result, Korula et al. (2015) were able to show that the same algorithm is 0.5052-competitive when the items arrive in a uniformly random order, but unfortunately, their proof is very long and involved. In this work, we present an (arguably) much simpler analysis that provides a slightly better guarantee of 0.5096-competitiveness for the greedy algorithm in the random-arrival model. Moreover, this analysis applies also to a generalization of online SWM in which the sets defining a (simple) partition matroid arrive online in a uniformly random order, and we would like to maximize a monotone submodular function subject to this matroid. Furthermore, for this more general problem, we prove an upper bound of 0.576 on the competitive ratio of the greedy algorithm, ruling out the possibility that the competitiveness of this natural algorithm matches the optimal offline approximation ratio of 1-1/e.

In this paper we present a distributed system that is capable of solving the Winner Determination Problem (WDP) for combinatorial auctions by use of a genetic algorithm. Combinatorial auctions are simple, allowing agents to bid on combination of items rather than making several individual bids. These types of auctions lead to more economical efficient allocations of goods in situations where the agents' have preference over bundles of goods. However, determining the winner(s) has been shown to be a NP-complete problem. This means that there is no simple way to solving the WDP. Our approach is to use a distributed system that solves the WDP using a genetic algorithm that is being distributed. In this way we are able to take advantage of the benefits that distributed systems offer. The results obtained from the system are compared to IBM's algorithm, known as CPLEX.

Procurement mechanisms used by businesses have evolved from simple single‐item auctions to complex multi‐unit, multi‐attribute, and multi‐object auctions and their combinations. These state‐of‐the‐art mechanisms offer many economic benefits but also introduce challenges such as the increased complexity in decision‐making for the participants. Our primary goal in this study is to study how decision complexity affects the economic outcomes and acceptability of an advanced economic mechanism: the continuous combinatorial auction. We identify three aspects of complexity—auction size, competition, and the number of active bids. We examine these complexity aspects with five types of auctions conducted with a general consumer population in an experimental environment with real payoffs. We find that various aspects of complexity affect economic outcomes in different ways. Furthermore, competition is a critical factor for conducting efficient auctions. We also conduct a secondary analysis of bidder behavior through a granular examination of clickstream data collected during the auctions. We find that decision complexity influences bidder strategies leading to differences in auction outcomes. Based on our analysis, we develop valuable insights for auction practitioners.

We discuss the optimal allocation problem in combinatorial auctions, where the items are allocated to bidders so that the sum of the bidders' utilities is maximized. In this paper, we consider the case where utility functions are given by quadratic functions; the class of such utility functions has a succinct representation but is sufficiently general. The main aim of this paper is to show the computational complexity of the optimal allocation problem with quadratic utility functions. We consider the cases where utility functions are submodular and supermodular, and show NP-hardness and/or polynomial-time exact/approximation algorithms. These results are given by using the relationship with graph cut problems such as the min/max cut problem and the multiway cut problem.

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers’ revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.

Given the profound integration of the sharing economy and the energy system, energy storage sharing is promoted as a viable solution to address the underutilization of energy storage and the challenges associated with cost recovery. While energy storage sharing offers various services for system operation, a significant question remains regarding the development of an optimal allocation model for shared energy storage in diverse application scenarios and the proposal of efficient solving algorithms. This paper presents the design of a computable combinatorial mechanism aimed at facilitating energy storage sharing. Leveraging the distinct characteristics of buyers and sellers engaged in energy storage sharing, we propose a combinatorial auction solving algorithm that prioritizes and incorporates the offers of shared energy storage, accounting for temporal variations in the value of energy resources. The numerical results demonstrate that the proposed solving algorithm achieves a computation time reduction of over 95%, adequately meeting the practical requirements of industrial applications. Importantly, the proposed method maintains a high level of computational accuracy, ranging from 92% to 98%, depending on the participants and application scenarios. Hopefully, our work is able to provide a useful reference for the further mechanism design for energy storage sharing.

Decision Making in Recurrent Neuronal Circuits