Competitiveness of Inexperienced and Experienced Contractors in Bidding

This paper tests the assertion that experienced contractors are more competitive than inexperienced contractors by measuring the effect of experience on bidding competitiveness for building contracts procured by a regular client. Contractors are grouped into experienced and inexperienced contractors, and two levels of contractors’ experience are identified: (1) bidding experience only and (2) bidding plus construction experience. Results of two-sample t tests show that experienced contractors are more competitive than inexperienced contractors. Contractors’ competitiveness in respect of school contracts and other contracts are further compared in evaluating the relationship between the project type and experience effect. Experienced contractors are, on average, more competitive in competing for school contracts. It emerges that the factor of project type familiarity amplifies the experience effect. A comparison of experienced contractors’ competitiveness in bidding for (1) new building works and (2) alteration and extension works shows that the effect of experience would be more significant when contract works packages are highly standardized.     

Available-to-promise based bidding decision by fuzzy mathematical programming and genetic algorithm

The present study attempts to integrate bidding decisions with order promising and production planning to enhance supplier profitability and service level. This study formulates the bid price and production plan as a mixed integer programming model with fuzzy constraints. The fuzzy constraints represent the decision-maker’s subjective judgment regarding the customer’s price tolerance. The proposed model combines the advanced available-to-promise (AATP) concept to find optimum resource allocation and enable accurate estimations of production costs and delivery dates. The proposed solution procedure determines the optimum bid price by striking a compromise between profitability and the possibility to win the contract. This study develops a genetic algorithm to solve this problem, and provides computer simulated experiments to evaluate the performance of the proposed approach.     

Bidding strategies for real-life small loan auctions

We define and identify bidding strategies in real-life small loan auctions (Prosper.com). In such auctions, lenders bid for borrowers' loan listings and the winners get to fund the loan at an interest rate determined by the auction. The exceptionally large empirical database provided by Prosper.com offers a unique opportunity to test and further develop the theory of online auctions. This study shows that bidding behavior is not homogeneous among bidders, as the traditional auction theory suggests. Instead, bidders use many different bidding strategies. Moreover, learning and bidders' consistency over time in different auctions is studied.     

东北区域电力市场发电报价网损修正算法研究

发电报价网损修正问题作为区域发电侧电力市场面临的主要问题之一，已受到各市场成员和许多学的普遍关注。作从公平性和经济性的角度明确了发电报价网损修正研究的必要性。指出发电报价网损修正问题的实质是输电损耗分摊问题。结合东北区域电力市场运营规则以及实用化算法的基本要求，对采用潮流摄动法和潮流追踪法的两种发电报价网损修正算法从网损修正系数、发电报价修正力度、网损优化方向及网损费用收支平衡四个角度进行了比较研究，指出了潮流摄动法存在的问题，明确了用潮流追踪法进行东北区域电力市场全电量竞价的发电报价网损修正是适宜的。以东北区域电力市场为例，经计算分析验证了本所提观点。     

基于SFE模型的区域电力市场模拟分析

在电力市场中，采用第一价格暗标拍卖机制的日前现货市场是一种主要的交易形式。为了研究现货市场中发电企业的竞标策略及其对市场结果的影响，作建立了分析现货市场的供给函数均衡模型，其基本假设是发电企业具有通常为线性的边际成本并提交线性供给函数进行市场竞价。由于采用零截距边际成本和供给函数的假设可以简化分析，中提出了线性供给函数模型和零截距线性供给函数模型之间的转化算法，即模型的正规化问题。利用该模型进行了算例分析，所考虑的影响市场结果的因素为需求、需求弹性，完全竞争企业的容量份额和策略竞标企业的规模，市场结果包括市场出清电价、消费剩余和生产剩余以及发电企业的利润。计算结果表明策略竞标企业的规模越大，抬升市场价格的能力和动机就越强。在大企业策略竞标的条件下，完全竞争企业会获得更大的超额利润。     

电厂机组优化出力竞价上网的自适应动态模型

通过引入机组自适应期望出力的概念，建立了电厂机组出力优化的自适应动态古诺模型，并由此导出了机组最佳出力竞价报价系数的动态模型，理论上这些动态模型总是稳定的。动态仿真结果表明，机组出力报价系数动态模型的稳定值总是趋近或等于机组出力报价系数的纳什均衡解。机组出力报价系数动态模型是电厂对其竞争者的最佳反应。该模型建立的目的是为电厂对机组优化出力报价提供理论指导。     

构造发电公司投标策略的一种新方法-微增响应猜测法初探

介绍了微增响应猜测法(Conjectural Variation，CV)的基本概念，提出了基于这种方法构造发电公司在不完全信息情况下的最优投标策略。通过理论分析和算例计算，结果表明基于经典博弈理论构造的发电公司的最优投标策略是该方法的一种特例。采用该方法进行市场模拟时，对各发电公司不同的微增响应猜测进行组合，可以得到不同的市场均衡点，且均为Nash均衡点。     

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.     

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

In an electricity market generation companies need suitable bidding models to maximize their profits. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper optimal bidding strategy problem is solved using a novel algorithm based on Shuffled Frog Leaping Algorithm (SFLA). It is memetic meta-heuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the Genetic-based Memetic Algorithm (MA) and the social behavior-based Particle Swarm Optimization (PSO). Due to this it has better precise search which avoids premature convergence and selection of operators. Therefore, the proposed method overcomes the short comings of selection of operators and premature convergence of Genetic Algorithm (GA) and PSO method. Important merit of the proposed SFALA is that faster convergence. The proposed method is numerically verified through computer simulations on IEEE 30-bus system consist of 6 suppliers and practical 75-bus Indian system consist of 15 suppliers. The result shows that SFLA takes less computational time and producing higher profits compared to Fuzzy Adaptive PSO (FAPSO), PSO and GA.     

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.