Bidding Situations Research Articles

Optimal bidding in emission constrained economic dispatch

Emission levels play a crucial role for the thermal power companies to limit their flue gas in the competitive market. Realistic power bidding optimizations demand the responsibility of power regulators for searching of the optimal power bidding from economic emission aspect. Different discrete bidding situations have been analyzed in this attempt when a number of thermal power generating utilities demand bidding price at different percentages of their respective marginal price. These utilities compete among themselves not only for gaining maximum profit, but also they are made bound here to offer energy price along with flue gas emission level as minimum as possible. Pareto optimization has been searched by establishing Nash equilibrium of Game Theory considering minimum total objective of fuel cost and flue gas emission from the consumers’ aspect along with maximum profit gain for the power bidders. These multi-objective optimization results show that at economic and environmentally constrained situations, power bidders can earn more profit not only at optimally higher bidding price, but also at optimally lower bidding price along with selling higher quantity of electricity. Those bidders who bid at optimally lower marginal price along with selling lower quantity of electricity to satisfy minimum total objective are found suffering loss. Overall study shows that cost functions and emission functions as well as marginal prices of respective units play important role in different environmentally constrained bidding situations. An Indian Utility of 62 buses has been used for study, and program code is written in MATLAB R2013b platform.

Experimental Investigation of the Impact of Risk Preference on Construction Bid Markups

Risk preference plays an important role in decision-making. The objective of this paper is to show how much risk preferences influence bid price decision-making in bidding for construction contracts. Based on the literature review and interviews, we designed a bidding situation and conducted an experiment to test decision maker responses. 172 participants with lengthy experience in bidding for construction work took part in the experiment. Both descriptive statistics and regression analysis are used for deep information mining of the data. We find that risk preference has a significantly positive effect on bid mark-ups. Based on a comparison of the influences of risk preferences on the bid price in different competitive bidding situations, we find that the bid price decreases with the number of bidders and it is even lower when the number of bidders is unknown. Therefore, considering both risk preference and the number of bidder is important to make a reasonable bid decision making. Finally, advice is also provided to construction companies for evaluating the leader’s decisions and decision-making team.

Multi-period resource allocation for estimating project costs in competitive bidding

In competitive bidding for project contracts, contractors estimate the cost of completing a project and then determine the bid price. Accordingly, the bid price is markedly affected by the inaccuracies in the estimated cost. To establish a profit-making strategy in competitive bidding, it is crucial for contractors to estimate project costs accurately. Although allocating a large amount of resources to cost estimates allows contractors to prepare more accurate estimates, there is usually a limit to available resources in practice. To the best of our knowledge, however, none of the existing studies have addressed the resource allocation problem for estimating project costs in competitive bidding. To maximize a contractor’s expected profit, this paper develops a multi-period resource allocation method for estimating project costs in a sequential competitive bidding situation. Our resource allocation model is posed as a mixed integer linear programming problem by making piecewise linear approximations of the expected profit functions. Numerical experiments examine the characteristics of the optimal resource allocation and demonstrate the effectiveness of our resource allocation method.

Reducing Risk by Leveraging Information for a Competitive Advantage in Bidding

INTRODUCTION Competitive bidding is an integral part of many businesses generating substantial revenues. On products that are standard or similar, the primary purchase criterion is price. Within this highly competitive environment, it is common practice in many industries to require all interested firms to submit a sealed bid for a given contract to supply goods or services. This includes many manufacturers and service organizations that are selling to other manufacturers of original equipment, as well as wholesalers, retailers, federal, state, and municipal governments, and others. The contract is awarded to the firm with the lowest bid. The process of developing a bid requires the manager to rely on a high level of quantitative and intuitive skills in determining the price (Rothkopf, 2007). The bidding system is part of a marketing decision support system (MDSS) used by either a sales or mark3eting group. MDSS was described for a division of General Electric by Lillis and McIvor (1985). Their MDSS bidding system provided an analysis of the data but no model to assess the probability of winning. In most situations, the manager needs a model to assist in responding to all contracts. Therefore, the components of a bidding support system should include a bidding model as depicted in Figure 1. [FIGURE 1 OMITTED] This case study describes the development of a system for pricing contracts in the pharmaceutical industry. The decision process involves the collection and assembly of the information to be used in a model to assist the manager in evaluating contract bids. The bidding model focuses on that aspect of competitive bidding wherein a manager routinely bids for the right to supply his company's products to another over a specific period of time, usually one year. The contracts involve similar, non patented products that may be identical or interchangeable, permitting the purchaser to buy from anyone of several competing sources. A large customer base creates a high volume of activity, with several bids every day for a product. With a large number of contracts generated for each product, the importance of an individual bid diminishes. Thus, no single contract jeopardizes the survival of the company or has a significant impact on the company's profitability. Substantial research efforts were devoted to different aspects of the bidding problem. Comprehensive bibliographies on competitive bidding were compiled by Stark and Rothkopf (1979) and Rothkopf and Harstad (1994). Engelbrecht-Wiggans (1980) reviewed the state of the art in his survey on auctions and bidding models. A survey by Laffont (1997) focused on the application of game theory to auction data, and Rothkopf and Park (2001) defined the issues to be resolved in designing an auction. Quantitative approaches to competitive bidding can trace their roots to the research of Friedman (1956), who published the earliest biographical listing. The models proposed by Friedman evaluated an individual contract using expected value. Construction projects (Chen, 2007; Dyer & Kagel, 1996; Elmaghraby, 1990; Skitmore, 2002), federal government contracts (Samuelson, 1986), and oil lease procurement (Keefer, Smith, & Back, 1991) research assesses an individual contract where the bidder carefully evaluates the value of the contract as it relates to the survival of the company or in terms of a substantial impact on the company's profitability (Pfeifer & Schmidt, 1990). Here, the bidder has a long period of time (weeks or months) in which to determine a single bid. More complex single auctions such as withdrawable bid situations (Harstad & Rothkopf, 1995; Rothkopf, 1991) have also been modeled. In contrast to the one-shot situation, this paper deals with a sequence of bids, each prepared in less than an hour and each of which has a small impact on profit. A sequence of bids to supply products or services presents a more complex situation. …

The introduction of the exemption system for foreign profits and its effects on international acquisitions – the UK and Japan regaining international tax competitiveness?

Purpose – Both, the UK and Japan abolished the tax credit system for foreign source dividends in 2009 in favour of the exemption system. With the move towards a dividend exemption system the governments intended to enhance the international tax competitiveness of their countries. The purpose of this paper is to evaluate the implications of substituting the credit system for the exemption system in the UK and Japan on cross-border transaction prices when competing for international acquisitions. Design/methodology/approach – The paper uses an economic model under certainty to analyse the changes in cross-border marginal purchase and seller prices as a result of the introduction of the newly introduced dividend exemption system. Findings – Shifting to an exemption system has ambiguous effects on the ability to compete for foreign acquisitions: investors from both countries are able to pay higher prices in the course of acquisitions, but while investors from the UK become more competitive, the relative competitive position for Japanese investors hardly changes and remains relatively constrained, independent of the form of double taxation relief. Thus the author verifies that the international tax regime is not the only determinant influencing the competitive position, ranking second to, e.g., the interaction with international tax rate differentials. Originality/value – The international tax reforms in UK and Japan in 2009 offer a unique opportunity to study the impact of international tax policy on the international tax competitiveness of multinational firms in the course of foreign acquisitions. Evidence from this paper is not exclusively applicable to the UK and Japan setting. The observed effects shed new light on the intensified debate in the USA of changing the international tax system by analysing the impact on the bidding situation in international acquisitions in a real-world transition scenario.

An order acceptance strategy under limited engineering man-hours for cost estimation in Engineering–Procurement–Construction projects

Accurate cost estimation is essential for any Engineering–Procurement–Construction (EPC) contractor accepting profitable projects because the project price is determined prior to receiving the contract. Therefore the contractor needs to ensure engineering man-hours (MH) in order to estimate project costs accurately as well as carry out the accepted orders. In this paper, we develop MH based order acceptance strategies and investigate their effects on the total expected profit through a long-term operation in EPC projects under a competitive bidding situation. To this end we build a simulation model describing relations among the volume of MH for cost estimation, accepted orders, revenues, and profits in EPC projects. Using our model, we show that the strategy, which maintains the appropriate balance of MH for cost estimation and project execution under the variability of accepted orders with competitive bidding situations, improves the total expected profit in EPC projects.

A sequential competitive bidding strategy considering inaccurate cost estimates

This paper develops a stochastic dynamic programming model for establishing an optimal sequential bidding strategy in a competitive bidding situation. In competitive bidding, a contractor usually sets the bid price of each contract by putting a markup on the estimated cost, and consequently, the bid price is affected by a cost estimation error. We take a scenario-based approach to determine the optimal markup in consideration of the effect of inaccurate cost estimates. We also introduce a value-at-risk constraint to mitigate the risk of suffering a large loss. Numerical results show that our model increases the average profit and reduces the profit volatility risk.

Choice of subcontractor in markets with asymmetric information: reputation and price effects

Purpose – Buyers assessing bids from suppliers of experience services face both an adverse selection and a potential moral hazard problem. The purpose of this study is to examine the relative importance of various signals of supplier reputation conveying information about unobserved supplier quality, which is important for identifying the best tender; and whether price is contingent on supplier reputation and on buyer's quality sensitiveness in a competitive bidding situation. Design/methodology/approach – This study builds on a conjoint experiment where 19 contractors consider alternative scenarios representing tenders from subcontractors of plumbing services. In the scenarios the subcontractors differ on their reputation and price variables, while the contractors differ in their quality sensitiveness. Multiple regressions analyzes the contingent price effects. Findings – Although low price is generally important for subcontractor selection, quality-sensitive buyers are willing to pay subcontractors a price premium to prevent quality debasement. On the other hand, despite the combined significance of supplier reputation on choice, buyers are not willing to pay price premiums to suppliers with a quality reputation. Research limitations/implications – Conjoint studies produce multiple cases but the underlying sample is limited. Therefore, this study should be regarded as preliminary and a basis for further validation on larger samples. Practical implications – In competitive bidding situations, suppliers with strong quality reputations may benefit most by low price offers. Thus, suppliers with a strong reputation should achieve profitability through a volume premium rather than a price premium effect. Suppliers opting for price premiums should target the quality sensitive segment of the market. Originality/value – In contrast to previous findings in B2B brand equity studies, but in line with findings in information economics, this study suggests that suppliers with a reputation for quality will not receive price premiums. The results indicate that in bidding contexts in B2B markets, the reputation variables may enhance rather than reduce buyers' price sensitivity, because supplier reputation increases low price credibility.

Uncertainty in competitive bidding – a framework for product–service systems

Owing to servitisation, manufacturing companies are increasingly required to compete through the provision of services around their products. The contracts for these services are often allocated through competitive bidding where the potential suppliers submit a price bid to the customer. The pricing decision is influenced by various uncertainties. This article proposes a conceptual framework depicting these influencing uncertainties on the bidding strategy. This framework is based on three empirical studies with industry investigating different viewpoints on the decision-making process. The intention is to support the pricing decision when competitively bidding for a service contract. The framework can be applied to specific competitive bidding situations to identify the influencing uncertainties, model them and depict their influences on the pricing decision.

Bayesian method for the correlated competitive bidding model

A multivariate competitive bidding model takes into account the correlation among competitors in determination of markup size. However, parameter estimation for the multivariate model is a challenging issue. A simplified, piecemeal style statistical method was proposed for low-dimension problems. However, this method may cause significant estimation errors when applied to complex bidding situations. A refined Bayesian statistical method based on Markov chain Monte Carlo (MCMC) simulation is developed that can be employed in practical bidding problems. To deal with missing values in bid data, a data augmentation technique is integrated in the MCMC process. The proposed Bayesian method is shown through case studies to be robust for complex bidding situations and also insensitive to the selection of the prior models of the correlation matrix. An important feature of the proposed Bayesian method is that it allows a project manager to quantify statistical uncertainties of parameter estimation and their effects on markup decisions. The optimal markup is represented by a posterior distribution which paints a complete picture of the uncertainties involved in the markup size decision.

ACO trained ANN-based bid/no-bid decision-making

One of the most important decisions that has to be made by contractor firms is whether to bid or not to bid for a project, when an invitation has been received. For any construction company, being able to deal successfully with various bidding situations is of crucial importance, especially in today’s highly competitive construction market. This paper presents an application of ant colony optimisation (ACO) algorithm and artificial neural network (ANN) to bid/no-bid decision making. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. Neural network is used to express the non-linear function between the input and output of the bid/no-bid decision making of project. ACO algorithm is used to learn ANN. The new algorithm has the merits of both ACO algorithm and neural network. The proposed decision support system framework is of good value to contracting organisations in different construction markets.

Analysis of Improved Bids in the Scope of a Risk Arbitrage Strategy

This document is a study of improved bids observed during announced mergers & acquisitions of listed companies. The scope of the analysis is the North American market (US and Canada) over the 1998-2011 period. An improved bid is defined as an improvement on the terms of the initial bid for a target either by the buyer or by a third party.In the scope of a risk arbitrage strategy, improved bids are important events, since they offer particularly interesting risk/return, similar to buying a call option. This document therefore aims to analyse improved bids from the point of view of an investor setting up an arbitrage strategy.The main conclusions are as follows: i) improved bids account for about 12% of finalised deals, ii)prevailing market conditions on equity and bond markets have an impact on the volume of improved bids as well as the observed yields. For example, towards the top of equity market cycles, the number of improved bids is particularly high. Conversely, the 2008 liquidity crisis led to a dramatic fall in yields on potential improved bid situations, iii) the market is efficient when it forecasts the outcome of certain improved bids. The higher the target’s shares trade above the offer price in the days following the announcement, the likelier it is that the deal will have a favourable outcome for the investor, iv) even though improved bids are most commonly seen in hostile bid situations, it is hard to establish the typical profile for an improved bid: neither the payment type nor the sector of the target, nor the nationality of the buyer are deciding factors. Overall, the phenomenon is therefore difficult to forecast.

Generation companies' adaptive bidding strategies using finite‐state automata in a single‐sided electricity market

SUMMARYThis paper explores the use of genetic algorithms (GAs) in the development of the bidding strategies used by generation companies under two different market clearing mechanisms, uniform pricing and pay‐as‐bid pricing. The bidding strategies are represented by two modifications of a classical data processing structure known as finite‐state automata. Semi‐fixed fitness function and co‐evolutionary fitness function were incorporated in our GA. A third simple representation to obtain a comparison baseline for the other two representations, showing how their behaviors compare with a “standard” solution, was also incorporated. The strategies developed by our method were adaptive, and all GA types were based on maximizing profit in a competitive bidding situation. Copyright © 2011 John Wiley & Sons, Ltd.

Decision to bid or not to bid: a data envelopment analysis approach

One of the most crucial decisions that is regularly exercised by construction contractors is to determine whether to bid or not to bid on a certain project. The purpose of this paper is to propose a data envelopment analysis (DEA) approach for the bid–no-bid decision. DEA is a robust non-parametric linear programming approach that is used for benchmarking performance and for making selection decisions. Based on a contractor's database of previous considerations of bidding opportunities, DEA creates a “favorable frontier” that consists of favorable bidding opportunities. New bidding opportunities are evaluated in reference to this “favorable frontier” and the bid–no-bid decision is consequently made. The proposed approach incorporates subjective management expertise and deals systematically with bidding situations to guide contractors in their bid–no-bid determination. A major strength of the proposed DEA approach is that it is deployable by organizations facing the bid–no-bid problem regardless of size, country of operation, number and type of factors considered in bidding, or even industry.

SCBMD: A knowledge-based system software for strategically correct bid/no bid and mark-up size decisions

This paper presents the development of a practical knowledge-based system software called SCBMD, which deals systematically with different bidding situations and assists the contractors in reaching ‘strategically correct’ bid/no bid and mark-up size decisions. The proposed framework differentiates among different sizes of contractors and uses a reasoning model that goes deeply into the heart of the decision making process. Hence, it helps to clarify the complex picture regarding the two sequential decisions and allows the users to follow the methodology used and the steps followed in reaching the final decisions. In order to see the degree to which SCBMD mirrors actual industry practices, it was implemented to 100 different real bidding situations. An accuracy of 86% was reached in simulating actual bid/no bid decisions of the contractors while the average difference in markup percentages proposed by the software and the contractors was found as 1.75%. Moreover, the software was evaluated for its timeliness under varying bidding conditions and found very effective especially for the cases where the contractor would like to reach decision alternatives for different possible scenarios of the same bidding situation.

PKO Resources, Inc.

Pete Carvey, CFO for the PKO Resources of McKinney, TX sat at his desk puzzling over the analysis just presented to him regarding an acquisition his firm was considering. Although this project seemed like hundreds of others that he had analyzed, Pete was bothered by three things: First, the purchase price that had been quoted to PKO seemed very high in light of prices that PKO had observed in recent months. Second, the typical reserve mix for properties that PKO considered was weighted very heavily on proven reserve categories (i.e., proved developed producing, proved developed non-producing, and proved undeveloped). The engineers had told Pete that the Salinas field had substantial prospects for unproved reserves (i.e., typically referred to as probable reserves). In the past PKO had simply ignored the unproved category when evaluating an acquisition but in this instance it appeared the firm would have to place some value on the unproved reserves if it was to have any chance of winning the bid. Finally, Pete worried that his approach to bidding was no longer appropriate as the firm had lost out in all its recent competitive bidding situations. This caused Pete to question the firm's standard approach to arriving at its bid prices. PKO's standard approach to evaluating acquisitions of oil and gas properties is largely static but the firm engages in a number of what if or scenario analyses before arriving at a final number.

Lower cost smart ID cards

Smart ID card designers often lead the pack when it comes to incorporating state-of-art security features into card designs. Because the costs of compromised identities are high, there is a greater willingness to include tamper-proof laminates and other security add-ons to information-rich chips, even if these additional features cost more. There is a limit, however, to how much extra cost the market can bear. It's largely been up to smart ID card manufacturers who are obliged to operate in competitive bidding situations to look for ways to keep smart ID card costs in check. In turn, suppliers to ID card manufacturers have created more intelligent manufacturing systems that can help defray the ultimate card costs.

Bounded awareness: what you fail to see can hurt you

We argue that people often fail to perceive and process stimuli easily available to them. In other words, we challenge the tacit assumption that awareness is unbounded and provide evidence that humans regularly fail to see and use stimuli and information easily available to them. We call this phenomenon “bounded awareness” (Bazerman and Chugh in Frontiers of social psychology: negotiations, Psychology Press: College Park 2005). We begin by first describing perceptual mental processes in which obvious information is missed—that is, simply not seen—by the visual perceiver. Inattentional blindness and change blindness are examples. We then extend this phenomenon to decision making and forecasting, using evidence about focalism to illustrate how people over focus on some information and fail to use other easily available information. We next examine how these processes of bounded awareness may extend to other important domains and across levels of analysis, such as information-sharing in groups, decision making in negotiators, and in competitive bidding situations such as auctions. Bounded awareness is a phenomenon that encompasses a variety of psychological processes, all of which lead to the same error: a failure to see, seek, use, or share important and relevant information that is easily seen, sought, used, or shared.

Exploring the Bidding Situation for Economically Most Advantageous Tender Projects Using a Bidding Game

Most open tendering procedures in the real world are highly complex, uncertain, and costly. With an increasing emphasis on the quality and value of procurement, economically most advantageous tender (EMAT) has been widely adopted as an alternative contract-awarding criteria, which has changed competitive strategies in the construction industry. A conceptual model of competitive bidding in EMAT is first established to reflect the credibility of the bidding situation. A bidding game for EMAT projects is performed by 24 participants to partially test the conceptual model. The result reveals that the game has the potential to reveal important factors in the bidding situation, simulate competitive bidding behaviors, and explore competitive advantages in the EMAT bidding process. The learning effect from the game should be useful for contractors who are preparing to deliver optimal tenders in EMAT projects.

Maximum Entropy Utility

This paper presents a method to assign utility values when only partial information is available about the decision maker’s preferences. We introduce the notion of a utility density function and a maximum entropy principle for utility assignment. The maximum entropy utility solution embeds a large family of utility functions that includes the most commonly used functional forms. We discuss the implications of maximum entropy utility on the preference behavior of the decision maker and present an application to competitive bidding situations where only previous decisions are observed by each party. We also present minimum cross entropy utility, which incorporates additional knowledge about the shape of the utility function into the maximum entropy formulation, and work through several examples to illustrate the approach.