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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.

We compare allocation rules in uniform price divisible-good auctions. Theoretically, a “standard allocation rule (STANDARD)” and a “uniform allocation rule (UNIFORM)” admit different types of low-price equilibria, which are eliminated by a “hybrid allocation rule (HYBRID).” We use a controlled laboratory experiment to compare the empirical performances of these allocation rules.

We conduct three-bidder uniform price divisible-good auctions varying the different allocation rules (standard, uniform, or hybrid) and whether or not explicit communication between bidders is allowed. For the case where explicit communication is allowed we also study six-bidder auctions.

We find that prices are similar across allocation rules. Under all three allocation rules, prices are competitive when bidders cannot explicitly communicate. With explicit communication, prices are collusive, and we observe collusive prices even when collusive agreements are broken. Collusive agreements are particularly fragile when the gain from a unilateral deviation is larger, and an implication of this is that collusive agreements are more robust under STANDARD.

We do not find conclusive evidence of differences in performance among allocation rules. However, there is suggestive evidence that STANDARD may be more vulnerable to collusion.

Divisible-good uniform price auctions are used in financial markets, but it is not possible to use naturally occurring data to test how alternatives to the standard format would perform. Using laboratory methods we provide an initial test of alternative allocation rules.

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 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.

This paper presents a procurement auction format structured so as to reconcile supply-side costs with demand-side value through the alignment of bidder and bid-taker interests. The existence of a dominant bidding strategy ensures this alignment and, at the same time, discourages strategic bidding. The format also involves minimal revelation of bidder information, making it attractive to bidders who expect continued participation in the same market. Another major goal of this paper is to engage policymakers through a discussion of important issues, which were brought forth in the course of designing and analyzing the aforementioned auction, that relate generally to the successful execution of procurement auctions.

The purpose of this paper is to address the inefficiency in resource allocation for disaster relief procurement operations. It presents a holistic and reconfigurable procurement auctions‐based framework which includes the announcement construction, bid construction and bid evaluation phases.

The holistic framework is developed in a way that auctioneers and bidders compete amongst each other in multiple rounds of the procurement auction. Humanitarian organization in disaster locations are considered as auctioneers (buyers) and suppliers are considered as bidders.

Unique system parameters (e.g. announcement options, priority of items, bidder strategies, etc.) are introduced to represent the disaster relief environment in a practical way. The framework is verified by simulation and optimization techniques using the system characteristics of the disaster relief environment as an input. Based on the parameters and their values, behavioural changes of auctioneers and suppliers are observed.

Combining the three phases of procurement auctions is unique both in the auction literature and in the disaster relief research, and it helps the humanitarian organizations supply the immediate and long‐term requirements in the disaster location more efficiently.