Optimal Bidding Price Research Articles

Optimal Strategy Modeling for Price-Time Biparameter Construction Bidding

In the price-time biparameter construction bidding system, each contractor submits a bid price and construction time to complete the project, which are then aggregated to a total combined bid (TCB) by the client, and the contractor with the lowest TCB is awarded the project. Since bid price can be set as construction cost plus an appropriate markup and construction cost usually depends on construction time, TCB can be expressed as a function of time. By minimizing such a TCB function, the optimal construction time can be obtained, from which the optimal construction cost and bid price and hence the optimal price-time bidding strategy can be sequentially decided. While examining the whole optimal bidding process, this paper focuses on three aspects to enhance the key ideas: discussing the properties of the general and the quadratic time-cost functions, deducing the optimal bidding formulas with quadratic time-cost relationship, and illustrating the procedures for estimating the quadratic time-cost function using a few experience data and the linear regression method. The in-depth examination of the price-time biparameter bidding model in the paper can suggest ideas and methodologies to construction bidding or contractor selection with more criteria.

New Sealed-Bid Electronic Auction with Fairness, Security and Efficiency

Electronic sealed-bid auction schemes usually have a common drawback, the third party (auction host) can conspire with a malicious bidder to leak all bidding prices before the opening stage. It results in the malicious bidder wining the auction with an optimal bidding price. Recently, Liaw et al. proposed an auction protocol for electronic online bidding in which they designed a deposit deduction certification for government procurement. However, it also has above mentioned flaw. Moreover, we further found that there were some extra security drawbacks in their protocol. First, the bidder can forge a bidding receipt to claim that he/she is a valid auction winner. Second, it may suffer from the third party forging attack. Third, their protocol leaked some bidders' private information to the third party, such as the bidder's bank account number and the authorization code. Thus, it cannot protect the bidder's privacy at all. In this paper, we not only point out the drawbacks from the previous scheme but also propose a new electronic auction scheme to overcome the above mentioned drawbacks. Furthermore, the computational complexity can be decreased in our online sealed-bid auction scheme.

Bidding under price uncertainty in multi-unit pay-as-bid procurement auctions for power systems reserve

In this paper a methodology for profit maximized bidding under price uncertainty in a day-ahead, multi-unit and pay-as-bid procurement auction for power systems reserve is proposed. Within this novel methodology a bidder is considered to follow a Bayes-strategy. Thereby, one bidder is assumed to behave strategically and the behavior of the remaining is summarized in a probability distribution of the market price and a reaction function to price dumping by the strategic bidder. With this approach two problems arise: First, as a pay-as-bid auction is considered, no uniform price and therefore no single probability distribution of the market price is readily available. Second, if historic bidding data of all participants are used to estimate such a distribution and market power is a relevant factor, the bid of the strategically behaving bidder is likely to influence the distribution. Within this paper for both of the problems solutions are presented. It is shown that by estimating a probability of acceptance the optimal bidding price with respect to a given capacity can be calculated by maximizing a stochastic non-linear objective function of expected profit. Taking the characteristics of recently established markets in Germany into account, the methodology is applied using exemplary data. It is shown that the methodology helps to manage existing price uncertainties and hence supports the trading decisions of a bidder. It is inferred that the developed methodology may also be used for bidding on other auction markets with a similar market design.

Supplier's optimal bidding strategy in electricity pay-as-bid auction: Comparison of the Q-learning and a model-based approach

In this paper, the bidding decision making problem in electricity pay-as-bid auction is studied from a supplier's point of view. The bidding problem is a complicated task, because of suppliers’ uncertain behaviors and demand fluctuation. In a specific case, in which, the market clearing price (MCP) is considered as a continuous random variable with a known probability distribution function (PDF), an analytic solution is proposed. The suggested solution is generalized to consider the effect of supplier market power due to transmission congestion. As a result, an algebraic equation is developed to compute optimal offering price. The basic assumption in this approach is to take the known probabilistic model for the MCP. The above-mentioned method, called model-based approach, is not more applicable in a realistic situation. In order to overcome the drawback of this method, which needs information about the MCP and its PDF, the supplier learns from past experiences using the Q-learning algorithm to find out the optimal bid price. The simulation results of the model-based and Q-learning methods are compared on a studied system. It is shown that a supplier using the Q-learning algorithm is able to find the optimal bidding strategy similar to one obtained by the model-based approach. Furthermore, to analyze a more realistic situation, the suppliers’ behaviors are modeled using a multi-agent system. Simulation results illustrate that the studied supplier finds the optimal bidding strategy in power market using the Q-learning algorithm.

Articulation Compatibility in Eliciting Price Bids

Do consumers prefer auctions that allow them to place more precise bids to auctions that accept less precise bids? Can consumers accurately estimate their need for price-elicitation precision? This research addresses these questions by applying the notion of compatibility to the relationship between consumers' bidding price uncertainty and the precision implied by the price-elicitation task. Data from four experiments show that when consumers are uncertain about the optimal bidding price, decision tasks requiring elicitation of precise bids lead to lower decision confidence, and vice versa. It is further shown that consumers display stronger preference for high-precision auctions, even though such auctions are associated with less confident pricing decisions.

Risk analysis of bidding strategies in an electricity pay as bid auction: A new theorem

Abstract Considering the uncertainties in the power market, the bidding problem has an important role for the power supplier to reach his goals, and using risk management methods to protect against the market risk is unavoidable. Thus, in this paper, the bidding decision making problem is formulated from a supplier’s viewpoint in a spot market. The spot market works based on a pay as bid auction or a discriminatory price auction. The market clearing price (MCP) is uncertain, and we consider a probabilistic model for it. Regarding the literature of the bidding problem and forecasting methods of MCP, a normal probability density function, pdf ( N ( μ m , σ m )), is a proper distribution for the MCP. The statistical parameters of MCP vary on different times (peak and off peak), and considering the concept of supplier risk, their effects on the supplier expected benefit and expected sell from selling energy will be discussed analytically. An important section of this research work concerns the optimal bidding strategy when μ m and σ m vary in different conditions of the power market. Thus, the coefficient of variation index (CV), as a proper measure, mathematically defined as σ m divided by μ m , is introduced to measure the market risk index. In this paper, the CV index is used to analyze and manage the supplier risk and introduce the optimal strategy. Then, for a constant amount of the CV as a theorem, it is proved that: (1) the maximum of the expected sell occurs at a constant level of the supplier risk and (2) the optimal bid price linearly depends on the standard deviation of the MCP. This theorem is generalized for the case that the expected value of the supplier benefit is considered as an objective function in the bidding process. Some numerical examples are presented, and application of the proposed theorem is discussed.

Bidder's strategy under group-buying auction on the internet

The group-buying auction is a new kind of dynamic pricing mechanism on the Internet. It is a variant of the sellers' price double auction, which makes the bidders as a group through Internet to get the volume discounts, i.e., the more bidders bid, the lower the price of the object being auctioned becomes. In this paper, we analyze the group-buying auction under some assumptions, such as that the independent private values (IPVs) model applies and bidders are risk neutral and symmetric, etc., and build an incomplete information dynamic game model to illustrate the bidders' bidding process. It proves that for the bidders there exists a weakly dominant strategy S, i.e., no matter when a bidder arrives at the auction and what the bidding history is, the highest permitted bid price that is no greater than his value to the object is always his optimal bid price but may not be the unique one.

An Analysis of Bid-Price Controls for Network Revenue Management

Bid-prices are becoming an increasingly popular method for controlling the sale of inventory in revenue management applications. In this form of control, threshold—or “bid”—prices are set for the resources or units of inventory (seats on flight legs, hotel rooms on specific dates, etc.) and a product (a seat in a fare class on an itinerary or room for a sequence of dates) is sold only if the offered fare exceeds the sum of the threshold prices of all the resources needed to supply the product. This approach is appealing on intuitive and practical grounds, but the theory underlying it is not well developed. Moreover, the extent to which bid-price controls represent optimal or near optimal policies is not well understood. Using a general model of the demand process, we show that bid-price control is not optimal in general and analyze why bid-price schemes can fail to produce correct accept/deny decisions. However, we prove that when leg capacities and sales volumes are large, bid-price controls are asymptotically optimal, provided the right bid prices are used. We also provide analytical upper bounds on the optimal revenue. In addition, we analyze properties of the asymptotically optimal bid prices. For example, we show they are constant over time, even when demand is nonstationary, and that they may not be unique.

Sealed bid auctions: an application to the building industry

This paper examines some of the theory and practice of competitive tendering in the building iildustry. Allocation of building contracts by means of competitive tendering is one type of sealed bid auction. T\vo main strands of the theory of sealed bid auctions are developed. The first strarul identifies optimal mark-up given that each tender competition occurs in isolution. The second strand treats explicitly the sequential nature of tendering and resource constraints of tenderers. It emerges that the first strand is a special case of the second. A simple optimal bid price rule is identijed, which indicates that bid price for any one contractor will be affected by theresources availuble to the contractor and general market conditions. It is shown that bid prices will be more competitive the better the information available to tenderers, the more carefully is the tender list constructed and the greater the number of firms invited to tender. It is further shown, however, that little is gained by having more...

Optimal Bid Prices for Unit Price Contract

In this type of contract, the work is broken down by the owner into bid items with an estimated work quantity for each item. In preparing a bid, a contractor estimates the direct cost of each item and then has to spread overhead, profit, and contingency to the bid items, so that these amounts will be recovered when the project is complete. The contractor's bidding strategy is complicated by the uncertainties of the work quantities and which items may be deleted from the contract. To minimize the cash investment required by the contract and maximize the expected profit, it is beneficial to unbalance the bid prices by allocating a larger than proportional share of the margin and overhead to those items performed early in the life of the project and those likely to overrun the owner's estimated quantity. This paper develops an easily implemented model of the preceding problem which permits the calculation of optimal unbalanced bid prices that will maximize the expected net worth of the project for a given desired margin.

On Optimal Bidding Strategies

This paper determines the optimal bid price for a fine bidding against several other firms, when each firm estimates its own cost and the cost estimates of the prices of other firms. Game theoretic and decision theoretic approaches are compared.

Technical Note—A Note on Cost Estimation and the Optimal Bidding Strategy

This note determines the optimal bid price for a firm bidding against several other firms for a contract, when each firm knows the expected value of its own cost and a probability distribution of the other firms' cost. The optimal bid price is calculated as a function of the firm's own expected cost, first for the case of two identical firms and then for several firms.

Profit Analyses and Sequential Bid Pricing Models

A bid pricing strategy based upon opportunity costs is presented for the firm confronted with the problem of competitively pricing a sequence of sealed tenders for future undifferentiated but interrelated contract work. Each contract, if awarded, will require the expending of predetermined amounts of several restricted resources at a later time. A goal of the bidding strategy is to determine a price structure of the winning bids which maximizes the total contribution over direct costs associated with the time period of resource utilization. According to various levels of complexity of data, several models of the problem are developed together with optimal bid price rules. Each optimal rule involves scarce resource “cost” charges for future opportunities and competitive advantages according to the general bid rule form: OPTIMAL BID PRICE = DIRECT COSTS + OPPORTUNITY COSTS + COMPETITIVE ADVANTAGE FEE. Experiences are also given of an adaptive and conditional implementation of the general bid rule for a major chemical manufacturer in a complex business area where sales orders are determined by competitive bidding.