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The purpose of this paper is to develop an optimal bidding strategy for a generation company (GenCo) in the network constrained electricity markets and to analyze the impact of network constraints and opponents bidding behavior on it.

A bi‐level programming (BLP) technique is formulated in which upper level problem represents an individual GenCo payoff maximization and the lower level represents the independent system operator's market clearing problem for minimizing customers' payments. The objective function of BLP problem used for bidding strategy by economic withholding is highly nonlinear, and there are complementarity terms to represent the market clearing. Fuzzy adaptive particle swarm optimization (FAPSO), which is a modern heuristic approach, is applied to obtain the global solution of the proposed BLP problem for single hourly and multi‐hourly market clearings. Opponents' bidding behavior is modeled with probabilistic estimation.

It is very difficult to obtain the global solution of this BLP problem using the deterministic approaches, even for a single hourly market clearing. However, the effectiveness of this new heuristic approach (FAPSO) has been established with four simulation cases on IEEE 30‐bus test system considering multi‐block bidding and multi‐hourly market clearings. The joint effect of network congestion and strategic bidding by opponents offer additional opportunities of increase in payoff of a GenCo.

FAPSO having dynamically adjusted particle swarm optimization inertia weight uses fuzzy evaluation to effectively follow the frequently changing conditions in the successive trading sessions of a real electricity market. This approach is applied to find the optimal bidding strategy of a GenCo competing with five GenCos in IEEE 30‐bus test system.

This paper is possibly the first attempt to evaluate an optimal bidding strategy for a GenCo through economic withholding in a network constrained electricity market using FAPSO.

Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.

In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots.

The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.

This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

The purpose of this study is to develop a conceptual framework of bidding model for maintenance contractors. Sustainability is closely linked with the maintenance of facilities or infrastructure. From maintenance contractor’s perspective, one of their key business processes is to bid projects under fierce competition. Their competitiveness is critically affected by their bidding behaviours and bid makeup selections.

This paper aims at identifying critical and important objectives of bidding and items affecting bidding strategies and developing a bidding model for maintenance contractors.

With the questionnaire surveys of 50 maintenance contractors, two critical objectives of bidding have been identified. Likewise, a total of eight critical and 12 important items affecting bidding strategy have also been identified. A bidding model for maintenance contractors is presented.

This model formulates the maintenance contractors’ bidding behaviors in a rational manner, and acts as a guideline to new and less experienced maintenance contractors to have a brief understanding about the bidding process and to assist them for establishing their own bidding strategies. With this knowledge, clients or maintenance contractors themselves would become easier to achieve their sustainability goals.

Many academic researches on contractor’s bidding strategy emphasized on the general building works, and less attention has been given to maintenance works. Because of the fact that the community is awash with sustainability, and sustainability development is closely related to maintenance work, the business operation of maintenance contractors should be understood. This shall contribute to the achievement of sustainability goals for the society.

The purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.

The problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.

The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.

Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.

To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.

This paper aims to study aggressive bidding strategies in real estate auctions – a structural equation modelling (SEM) approach.

The authors use two data sets to study aggressive bidding strategies. First, the results from a survey with 1,803 participants examining real estate auctions are used to identify bidding strategies and related motivations. Second, the authors apply SEM by using data from 1,078 exclusive auction journals from real estate sales in Norway to study both the direct and indirect price effects of the bidding strategies.

The authors define four aggressive bidding strategies: high opening bid, high bid increase (jump bids), short acceptance deadline and short response time. The authors find that all four strategies yield a higher sales price. Bidders can actively influence the behaviour of the other participants and cool the potential auction fever, thus reducing the final price premium.

This paper gives households, investors and policymakers a better understanding of how bidding strategies affect real estate auctions and the final price.

The purpose of this paper is to present implications of the seller's ability to bid in the four classical auction forms, with independent private values: English, Dutch, first‐ and second‐price auctions.

Under each auction form, the identity of the winning bidder and the expected winning bid are compared between the case when the seller may bid and when he cannot, using equilibrium bidder strategies. The seller's incentive to bid is evaluated.

The strategies and the welfare results differ with auction type and underlying information assumptions – bidders are either aware or unaware of the seller's ability to bid. In the Dutch and the first‐price auctions, seller‐bidding does not affect any classical results. In the English and the second‐price auctions, it leads to no lower expected prices than without it and higher prices with positive probability. In the English and the second‐price auctions, the seller bids above his reservation value and may unintentionally win the auction. These auctions result in inefficiency with positive probability.

The English and the second‐price auctions are the most common real‐world auctions. In these auctions, the seller's ability to bid – secretly or publicly – redistributes welfare among participants and introduces a possibility of inefficiency. Making this ability publicly known does not solve the latter problem. Auctioneers must prevent the seller from bidding, or must select a different auction form when seller‐bidding is anticipated.

The paper clarifies to regulators, auction designers, bidders, and other readers which auction forms are susceptible to subversion by seller‐bidding and what the potential damages are.

The purpose of this paper is to analyze and evaluate bidding competitiveness and success rate of contractors bidding for public construction projects (PCPs). Additionally, this research determines the effect of work sector, contractor’s classification category (experience), project size and number of bidders on contractors’ bidding competitiveness, and the influence of work sector and classification category on their success rate.

The data were collected through 2,296 bidding attempts for 289 tender projects that were announced by the Government Tenders Department in Jordan between 2013 and 2016. The research uses bid competitiveness percentage (BCP) to evaluate contractors’ bidding competitiveness. Pearson correlation is used to investigate the correlation among variables. Hypothesis testing using ANOVA was conducted to evaluate the effect of the abovementioned factors on contractors’ bidding competitiveness, and their success rate.

The results of the analysis indicate that contractors’ average BCP and success rate in Jordanian PCPs are 83.8% and 13.3%, respectively. The analysis also reveals that work sector, contractor’s classification category, project size and number of bidders significantly affect contractors’ bidding competitiveness, whereas classification category and work sector do not affect bidding success rate. Therefore, experience of contractors affects their bidding competitiveness, but does not affect their success rate.

The present research uses contractors’ bidding success rate as a measure to evaluate their bidding competitiveness for PCPs. The novel model of this research can be applied in any country, after considering local regulations, to measure and evaluate contractors’ bidding competitiveness, and success rate when bidding for PCPs. Also, contractors cannot depend on their experience (i.e. classification category) or increasing bidding attempts to win bids and improve bidding success rate, rather than enhance their bidding strategy.

The purpose of this paper is to explore the information that manufacturing companies have available when competitively bidding for service contracts.

A semi-structured interview study was undertaken with industrialists in various sectors, which are currently facing the issue of servitisation.

One of the main findings was that, despite the novelty of the process, the decision makers at the competitive bidding stage have an understanding of the involved uncertainties. In particular, the uncertainty arising from the customer as the user of the product and evaluator of the competitive bids in addition to the uncertainty connected to the competitors were identified as the main influences on the pricing decision.

The research implications show the influences and considerations during the decision-making process at the competitive bidding stage for service contracts. These include the customer and the competitors.

Shortcomings in the current industrial practice were identified such as the approaches used to communicate the cost estimate for the service contract. The approaches currently used contradict research findings in the area of communicating uncertainty information, which means that further research is to be done to identify optimal approaches to displaying the uncertainty connected to the communicated information.

This paper offers a basis for research to understand the challenges industry faces when competitively bidding for service contracts. This can be used to develop novel approaches in supporting the decision maker such as a model that presents the probability of winning in comparison to the probability of making a profit.

The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising.

The authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms.

Findings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors.

This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.