Set Of Bids Research Articles

Bid-Aggregation Based Clearing of Day-Ahead Electricity Markets

In this work we propose a heuristic clearing method for a simplified model of European day-ahead electricity auction markets, considering divisible standard bids and block orders. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints, reducing the feasibility region of the clearing problem. By removing bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block orders the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to run multiple threads of the algorithm, using different aggregation patterns. We evaluate the performance of the algorithm via numerical tests.

An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details.

Bundle generation for last-mile delivery with occasional drivers

In this paper, we present the vehicle routing problem (VRP) with occasional drivers (OD) and order bundles (OB). The problem VRP-OD-OB is an extension of the VRP-OD, where instead of assigning one customer per driver, drivers are assigned bundles of customers. To deal with the bundle-to-driver assignment, a bidding system is exploited, in which a company offers a set of bundles and the drivers raise their bids. These bids depend on features such as the drivers’ destination, flexibility in deviating from the shortest path, and willingness to offer service. To generate valuable bundles of customers, we propose two strategies: (i) an innovative approach based on the creation of corridors, and (ii) a traditional approach based on clustering. Through an experimental study, carried out on randomly generated instances and on a real road network, we show that the innovative corridor-based approach strongly outperforms the clustering-based approach. Given a set of bundles and a corresponding set of bids, we provide a mathematical formulation and valid inequalities to solve the VRP-OD-OB. To address larger instances, we design an efficient large neighborhood search-based matheuristic. The results of an extensive computational study show that this method provides near-optimal solutions within very short run times. An analysis of the impact of drivers’ flexibility and willingness levels on the percentage of customers assigned to ODs is presented. Moreover, the case in which ODs dynamically appear at regular time intervals is investigated. Also in this dynamic setting, considerable total cost reductions are shown. Moreover, we derive several important managerial insights, which include the observation that it is not necessary to provide a high number of bundles to achieve good quality solutions. Companies should rather focus on generating fewer but more attractive bundles.

Reference Price Effects in First-price Private-value Auctions

External reference points, such as outside options or budget constraints, are common in bidding environments. We experimentally investigate the impact of external reference points for bidding in first-price auctions with independent private values. We find that the presence of a reference point distorts bidding behaviour. There are “reference price effects” consistent with a preference for certainty and anchoring bias. To establish robustness, we conduct a second experiment that controls for bidders’ first-order beliefs. After controlling for beliefs, the reference price effect dominates. We observe that the imposition of a ceiling on the opponent’s maximum bid set at the upper bound of the risk-neutral Nash equilibrium support is strong enough to reverse the well-documented pattern of overbidding in first-price auctions and indeed to induce significant underbidding. The findings inform on the implications of auction formats that involve reference points, and specifically buy-it-now price options that are common in internet auction listings.

Matching algorithm of the auctions on Ebay

This paper explores eBay auction properties that match buyers and sellers and generates millions of sales every month. eBay’s auction is now a well known mechanism designed to make buyers and sellers feel comfortable doing business without meeting each other. In a theoretical point of view, the current matching algorithm has not solved the online auction problems because the main conditions of agents’ preferences do not satisfy when bidders are unobservable and a set of bidders is not identified. Therefore, we construct a new simplified model of matching with a given object for sale to form a seller-bidder pair to overcome the online auction issues. Specially, our model may extend for the matching mechanism of the job market.

Bidding Styles of Institutional Investors in IPO Auctions

We examine the bidding behavior of institutional investors in initial public offering (IPO) auctions using a hand-collected data set of limit bids. We find that the majority of institutional investors in our sample are “occasional bidders,” who rarely get a share allocation. “Regular bidders” are in a minority but account for the bulk of the demand. They bid conservatively, and only a few of them can be classified as “well-informed bidders,” who place more aggressive bids in hotter IPOs than in colder ones. Our findings suggest that “dirty” auctions that are restricted to institutional investors work as an information extraction mechanism.

Evolutionary stability in the generalized second-price auction

The “generalized second-price auction” is widely employed to sell internet advertising positions and has many equilibria. Analysis of this auction has assumed that myopic players commonly know each others’ position values, and that the resulting equilibrium play is “locally envy-free”. Here, I argue that the appropriate refinement of Nash equilibrium for this setting is evolutionary stability, and show that it implies that an equilibrium is locally envy-free if the whole population of players bids in each auction and the set of possible bids is not too coarse. However, not all locally envy-free equilibria are evolutionarily stable in this case, as I show by example for the popular Vickrey–Clarke–Groves outcome. The existence of evolutionarily stable equilibrium is established when one position is auctioned, as well as for two positions and a large number of bidders.

Differences of opinion, institutional bids, and IPO underpricing

Miller (1977) hypothesizes that IPO underpricing arises because the issue price is based on the average opinion while the aftermarket price is set by a minority of optimistic investors. Using a unique data set of institutional bids for a large sample of Chinese IPOs, we show that the IPO issue price is positively related to the quantity-weighted average bid price and unrelated to the market-clearing bid price. In contrast, the first-day closing price is positively related to the market-clearing bid price and unrelated to the average bid price. Overall, our results provide strong support for Miller's explanation of IPO underpricing.

Efficient mechanisms with information acquisition

This paper studies the design of ex ante efficient mechanisms in environments where a single object is for sale, and agents have positively interdependent values and can covertly acquire information at some cost before participating in a mechanism. We find that ex ante efficient mechanisms discourage agents from acquiring excessive information by pooling or randomization. The optimal pooling regions are those where the semi-elasticity of information acquisition is large. There exists an ex ante efficient mechanism that can be implemented by standard auctions with restrictions on the set of allowable bids. In special cases, this implementation is simple and appealing: standard auctions with discrete bids.

Splitting a Pie: Mixed Strategies in Bargaining Under Complete Information

We characterize the mixed-strategy equilibria for the bargaining game in which two players simultaneously bid for a share of a pie and receive shares proportional to their bids, or zero if the bids sum to more than 100%. Of particular interest is the symmetric equilibrium in which each player's support is a single interval. This consists of a convex increasing density f1(p) on [a, 1-a] and an atom of probability at a, and is unique for given a ∈ (0, .5). The two outcomes with highest probability are breakdown and a 50-50 split. We use the same approach to characterize all symmetric and asymmetric equilibria (such as hawk-dove) that mix over a finite set of bids and for general sharing rules. We extend Malueg's 2010 proof of existence to uniqueness of equilibria with any balanced compact set A ∈ (0,1) as bid supports (but do not characterize them).

Multiobjective Auction-Based Switching-Off Scheme in Heterogeneous Networks: To Bid or Not to Bid?

The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.

An asymmetric multi-item auction with quantity discounts applied to Internet service procurement in Buenos Aires public schools

This article studies a multi-item auction characterized by asymmetric bidders and quantity discounts. We report a practical application of this type of auction in the procurement of Internet services to the 709 public schools of Buenos Aires. The asymmetry in this application is due to firms’ existing technology infrastructures, which affect their ability to provide the service in certain areas of the city. A single round first-price sealed-bid auction, it required each participating firm to bid a supply curve specifying a price on predetermined graduated quantity intervals and to identify the individual schools it would supply. The maximal intersections of the sets of schools each participant has bid on define regions we call competition units. A single unit price must be quoted for all schools supplied within the same quantity interval, so that firms cannot bid a high price where competition is weak and a lower one where it is strong. Quantity discounts are allowed so that the bids can reflect returns-to-scale of the suppliers and the auctioneer may benefit of awarding bundles of units instead of separate units. The winner determination problem in this auction poses a challenge to the auctioneer. We present an exponential formulation and a polynomial formulation for this problem, both based on integer linear programming. The polynomial formulation proves to find the optimal set of bids in a matter of seconds. Results of the real-world implementation are reported.

2 階層サプライチェーンにおける複数顧客との交渉を考慮した並列意思決定フレームワークの検討

By intensification of the competition with the globalization of the production, the dynamic supply chain becomes popular. In dynamic supply chain, the company select the most suitable business partner dynamically, hence the negotiation is necessary for the business partner selection. In the previous study, it revealed that the gross profit of the whole supply chain increased when the negotiation was done based on the price and delivery date which was decided according to the stock quantity and the production schedule. However, the previous study did not consider the simultaneous negotiations with multiple customers because a breakdown of either negotiation affected the production schedule. Therefore, in this study, we propose a simultaneous negotiation framework for multiple customers. In this framework, negotiation is done based on a set of bids which is determined according to the multiple schedules. The effectiveness of the proposed framework is validated through the computational experiments.

A clique-based exact method for optimal winner determination in combinatorial auctions

Given a set of items to sell and a set of combinatorial bids, the Winner Determination Problem (WDP) in combinatorial auctions is to determine an allocation of items to bidders such that the auctioneer’s revenue is maximized while each item is allocated to at most one bidder. WDP is at the core of numerous relevant applications in multi-agent systems, e-commerce and many others. We develop a clique-based branch-and-bound approach for WDP which relies on a transformation of WDP into the maximum weight clique problem. To ensure the efficiency of the proposed search algorithm, we introduce specific bounding and branching strategies using a dedicated vertex coloring procedure and a specific vertex sorting technique. We assess the performance of the proposed algorithm on a large collection of benchmark instances in comparison with the CPLEX 12.4 solver and other approaches. Computational results show that this clique-based method constitutes a valuable and complementary approach for WDP relative to the existing methods.

Research on Cloud Manufacturing Resource Allocation in Distributed Computing Environment

With the rapid development of network information technology, manufacturing presents a development trend of networking, virtualization, intelligence and servitization. Cloud manufacturing (CMg) with a new type of service-oriented manufacturing model is put forward and excellently satisfies the development requirements of modern manufacturing enterprises. With considering the complex characteristics of cloud manufacturing, firstly, we construct Cloud manufacturing resource scheduling system architecture to schedule service task and allocate manufacturing resource, which includes user requirements, task plan, global task scheduling, service collaboration allocation, virtualization and cloud manufacturing resource. Secondly, we propose a comprehensive resource allocation model based on auctions theory to satisfy different users’ request, and then the greedy method is used to search the optimal bid in the bid set. Finally, we implement some simulation experiments, and simulation results show that the solution algorithm of CARA’s winner determination problem gets the same solution as the M-HEU algorithm with less time complexity, and it is more applicable, efficient and effective for online multi-resource allocation.

Combinatorial auctions of railway track capacity in vertically separated freight transport markets

This paper addresses the problem of allocating residual track capacity among multiple competing carriers where infrastructure ownership and train operations are vertically separated to facilitate delivery by train. Two bid set construction techniques are proposed that allow carriers to fully express their preferences for track usage. Both techniques enable the expression of complementary or substitutable relationships among the train slots constructed along predetermined train routes and permit quick configuration of routes from residual track capacity along which new trains can be operated. A winner determination problem that simultaneously accommodates bidding languages of both bid set construction techniques is used to generate an optimal capacity allocation among bidders based on different bidding strategies. Results of computational experiments designed to illustrate the efficiency of the proposed bid construction technique and effectiveness of the framework are presented.

Delegated Bidding and the Allocative Effects of Alternative Accounting Rules

I study the efficiency of three prominent accounting rules in a delegated bidding setting where bidders' incentives are tied to both accounting income and economic surplus. Trade efficiency is maximized (minimized) by the value-in-use method (historical cost method). The exit-value method generates an accounting based winner's curse that results in fire-sale-like valuations. Yet, in the limit, as the number of bidders grows large, the efficiency of the exit-value method converges to that of the value-in-use method.

Decision support for preference elicitation in multi-attribute electronic procurement auctions through an agent-based intermediary

Multi-attribute auctions have become increasingly popular in enterprise procurement. In the auctions, the elicitation of the preference of an auctioneer concerning multiple attributes is a central task in determining the winner(s). Considering the difficulty of explicit elicitation, a preference elicitation framework is proposed to assist the auctioneer in inferring his/her underlying preference model(s). The auctioneer is expected to provide the information of attribute weights, the holistic preference relations concerning a set of reference bids and the comparison information of intensities of preferences between some pairs of bids on all attributes and/or a particular attribute. Based on this information, a linear programming model is constructed to infer the preference model(s) of the auctioneer so that the estimations are as consistent as possible with the given preference statements. Furthermore, a method is also given to select a representative preference model from the set of compatible ones. The framework is implemented by an intelligent buyer agent called e-buyer which has five main components, i.e., a semantic analyzer, a preference elicitation module, a bid evaluation module, a model base, and a database. The e-buyer is embedded into an auction intermediary, and the proposed preference elicitation models are stored in the model base. Several graphical user interfaces are also presented to visualize the future trading intermediaries. Finally, a numerical example is given to illustrate the framework and show the effectiveness of the preference elicitation models.

The Value of Reputation in an Online Freelance Marketplace

Online freelance marketplaces are websites that match buyers of electronically deliverable services with freelancers. While freelancing has grown in recent years, it faces the classic ‘information asymmetry’ problem – buyers face uncertainty over seller quality. Typically, these markets use reputation systems to alleviate this issue, but the effectiveness of these systems is open to debate. We present a dynamic structural framework to estimate the returns to seller reputations in freelance sites. In our model, each period, a buyer decides whether to choose a bid from her current set of bids, cancel the auction, or wait for more bids. In the process, she trades-off sellers’ price, reputation, other attributes, and the costs of waiting and canceling. Our framework addresses ‘dynamic selection’, which can lead to underestimation of reputation, through two types of persistent unobserved heterogeneities – in bid arrival-rates and buyers’ unobserved preference for bids. We apply our framework to data from a leading freelance firm. We find that buyers are forward-looking, that they place significant weight on seller reputation, and that not controlling for dynamics and selection can bias reputation estimates. Using counterfactual simulations, we infer the dollar value of seller reputations and provide guidelines to managers of freelance firms.

The Value of Reputation in an Online Freelance Marketplace

Online freelance marketplaces are websites that match buyers of electronically deliverable services with freelancers. Although freelancing has grown in recent years, it faces the classic “information asymmetry” problem—buyers face uncertainty over seller quality. Typically, these markets use reputation systems to alleviate this issue, but the effectiveness of these systems is open to debate. We present a dynamic structural framework to estimate the returns to seller reputations in freelance sites. In our model, a buyer decides in each period whether to choose a bid from her current set of bids, cancel the auction, or wait for more bids. In the process, she trades off sellers' price, reputation, and other attributes, as well as the costs of waiting and canceling. Our framework addresses dynamic selection, which can lead to underestimation of reputation, through two types of persistent unobserved heterogeneities: bid arrival rates and buyers' unobserved preference for bids. We apply our framework to data from a leading freelance firm. We find that buyers are forward looking, that they place significant weight on seller reputation, and that not controlling for dynamics and selection can bias reputation estimates. Using counterfactual simulations, we infer the dollar value of seller reputations and provide guidelines to managers of freelance firms.