A Multivariate Approach to Construction Contract Bidding Mark-up Strategies

A multivariate approach to contract bidding strategies in the construction industry is presented. This represents a radical departure from previous work in the field by using all available data on competing bidders. ‘Optimal’, ‘no loss’ and ‘break even’ mark-up strategies are derived and methods of parameter estimation proposed. A case study shows how the three strategic mark-up values are calculated against known competitors.     

求极限中的解题策略

通过实例介绍求极限的解题策略,旨在帮助学生在解题过程中效仿与拓展,从而强化其创新意识,使其灵活掌握所学基础知识.     

Students' Verification Strategies for Combinatorial Problems

We focus on a major difficulty in solving combinatorial problems, namely, on the verification of a solution. Our study aimed at identifying undergraduate students' tendencies to verify their solutions, and the verification strategies that they employ when solving these problems. In addition, an attempt was made to evaluate the level of efficiency of the students' various verification strategies in terms of their contribution to reaching a correct solution. 14 undergraduate students, who had taken at least 1 course in combinatorics, participated in the study. None of the students had prior direct learning experience with combinatorial verification strategies. Data were collected through interviews with individual or pairs of participants as they solved, 1 by 1, 10 combinatorial problems. 5 types of verification strategies were identified, 2 of which were more frequent and more helpful than others. Students' verifications proved most efficient in terms of reaching a correct solution when they were informed that their solution was incorrect. Implications for teaching and learning combinatorics are discussed.     

Students' Verification Strategies for Combinatorial Problems

We focus on a major difficulty in solving combinatorial problems, namely, on the verification of a solution. Our study aimed at identifying undergraduate students' tendencies to verify their solutions, and the verification strategies that they employ when solving these problems. In addition, an attempt was made to evaluate the level of efficiency of the students' various verification strategies in terms of their contribution to reaching a correct solution. 14 undergraduate students, who had taken at least 1 course in combinatorics, participated in the study. None of the students had prior direct learning experience with combinatorial verification strategies. Data were collected through interviews with individual or pairs of participants as they solved, 1 by 1, 10 combinatorial problems. 5 types of verification strategies were identified, 2 of which were more frequent and more helpful than others. Students' verifications proved most efficient in terms of reaching a correct solution when they were informed that their solution was incorrect. Implications for teaching and learning combinatorics are discussed.     

动态自融资与消费策略的多目标控制模型

在不考虑交易费用的前提下,本文研究了投资者最优动态自融资与消费策略,建立了两种多目标控制模型,分别考虑了使投资者的累积消费效用与／或拥有的财富的效用较大风险较小两个目标。对传统的侧重于单纯追求消费或财富效用达到极大的方法进行了改进,给出了相关分析结果。     

Distributions in Competitive Bidding

Analyses of historical tendering data for a company selling concrete farm buildings have called into question the usual assumption of normality or lognormality for the probability distribution. The desirability of modelling individual competitors' strategies is reinforced. Complex problems of parameter estimation and testing have been overcome by adopting a likelihood approach, with values examined for plausibility.     

Competitive Bidding

This paper considers the problem of making simultaneous bids for a number of objects against competition by an unknown number of competitors. A method of solution is given which has been successfully applied in practice.     

Parallel Strategies Applied to FE Simulations of Multiphasic Problems

While the theoretical background of various porous media models is well understood, it is still a demanding task to deal with these models numerically. In this contribution, a triphasic model is presented, which is capable of describing partially saturated soils. In quasi‐static conditions, this model results in the primary variables solid displacement, pore‐liquid pressure and pore‐gas pressure. For a stable numerical implementation, Taylor‐Hood elements are required, which need quadratic ansatz functions for the displacement and linear ansatz functions for the pressure terms. Looking at numerical simulations in 2‐d, challenging finite element calculations have already been realized in combination with adaptivity in time and space [1]. Nevertheless, new strategies have to be considered for a realization of applications of the model in 3‐d in order to handle the huge amount of unknowns arising from the discretization with Taylor‐Hood elements. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Recurrent Competitive Bidding

A competitive bidding policy can be formulated if prior probability distributions of competitors' bids for future contracts have been determined. A method for determining these prior probability distributions is described for markets where there are a large number of customers. The identity of each winning company and its bid are assumed to be available to competitors and other customers so that the market reacts quickly to changes. A real example of the use of the method is given. The suppliers' general levels of bidding and their variation with individual types of customer are evaluated quantitatively. The results are consistent with qualitative knowledge of the market.     

Some Optimal Strategies for Bandit Problems with Beta Prior Distributions

A bandit problem with infinitely many Bernoulli arms is considered. The parameters of Bernoulli arms are independent and identically distributed random variables from a common distribution with beta(a, b). We investigate the k-failure strategy which is a modification of Robbins's stay-with-a-winner/switch-on-a-loser strategy and three other strategies proposed recently by Berry et al. (1997, Ann. Statist., 25, 2103–2116). We show that the k-failure strategy performs poorly when b is greater than 1, and the best strategy among the k-failure strategies is the 1-failure strategy when b is less than or equal to 1. Utilizing the formulas derived by Berry et al. (1997), we obtain the asymptotic expected failure rates of these three strategies for beta prior distributions. Numerical estimations and simulations for a variety of beta prior distributions are presented to illustrate the performances of these strategies.     

Cost Uncertainty in Competitive Bidding Models

In this paper a number of issues will be examined regarding uncertainty about cost estimates in competitive bidding models. First, the value of information is derived when the relevant parameters are known. Next, it is shown how the usual procedure for finding the optimal price can be improved by taking into account information regarding the relation between the ratio of estimated cost to real cost for winning bids, and markup. Finally, a trial-and-error procedure is presented for determining the parameters needed to estimate the value of information.     

Implementing a Bidding Model

A major British construction company spent 4 years developing, experimenting, evolving and implementing a probabilistic bidding model. This paper chronicles the progress and describes the results achieved. Over the course of the project the model evolved from a simple algorithm into a sophisticated tool for corporate planning.     

Managerial Judgement in a Competitive Bidding Model

The basic Friedman model is extended to allow for bias in cost estimates and the use of management judgement on market trends. The estimated mean bid is used as a parameter. Simulation, using data from four construction companies, is used to evaluate the performance of the resulting model. A sensitivity analysis is used to determine the required accuracy for the management estimate. A brief account is given of the introduction of the model as a practical tool of top management in a major construction firm.     

多风险因素的投标报价决策方法

本文讨论工程项目投标报价的多风险因素层次模型，系统地介绍了90年代以来发展出的多风险因素条件下投标报价的5种主要决策方法，即层次分析法（AHP）、人工神经网络（ANN）、模糊评价法（Fuzzy）、专家系统（ES）和基于事例推理（CBR）。本文也对中国在该领域的研究现状作一个简单的评述。     

A Stochastic Model for Bidding

Competitive bidding situations involve considering a multiplicity of factors. Organizations must be able to weigh the relative probability of potential projects based on resource usage, project duration and competitor actions to decide which of many possible bids to submit. A bidding strategy designed to maximize expected long run return is crucial, since an organization can usually submit only one bid per project.This paper presents a family of stochastic dynamic programming models considering different bidding situations. Several projects, each with several potential bids, are available for each situation. The objective is to determine what bidding strategy will maximize expected returns. Models are developed for two principle bidding situations: sequential, where projects are bid individually; and simultaneous, where several projects are bid at one time. Next, the effects of over- or under-commitment of resources are incorporated into the models. Finally, changes in project timing and the resultant effects on bidding strategy are included.A numerical example traces the changes in bidding strategy which occur as the models are expanded. The general formulation of bidding problems is also discussed, including changing the bid success probabilities due to competitor actions, the possibility of crashing projects and alternate methods of performing projects.     

组合优化算法中摆脱局部极小值的几种策略

局部搜索算法是一种非常有效的求解组合优化问题的算法 ,它具有通用、灵活等特点 .但是 ,由于搜索空间和目标函数的复杂性 ,目标函数在搜索空间中有许多局部极小值点 ,使算法在这些局部极小值点处被“卡住”,大大影响算法的效果 .对于此问题 ,笔者查阅了大量文献资料 ,结合自己的研究实践 ,总结出几种跳出局部极小“陷井”的策略 .使用这些策略 ,有望使算法更加完善 ,在求解组合优化问题过程中更能发挥其作用 .     

Generation Supply Bidding in Perfectly Competitive Electricity Markets

This paper reports on the development of a comprehensive framework for the analysis and formulation of bids in competitive electricity markets. Competing entities submit offers of power and energy to meet the next day's load. We use the England and Wales Power Pool as the basis for the development of a very general competitive power pool (CPP) framework. The framework provides the basis for solving the CPP dispatcher problem and for specifying the optimal bidding strategies. The CPP dispatcher selects the winning bids for the right to serve load each period of the scheduling horizon. The dispatcher must commit sufficient generation to meet the forecasted load and reserve requirements throughout the scheduling horizon. All the unique constraints under which electrical generators operate including start-up and shut-down time restrictions, reserve requirements and unit output limits must be taken into account. We develop an analytical formulation of the problem faced by a bidder in the CPP by specifying a strategy that maximizes his profits. The optimal bidding strategy is solved analytically for the case of perfect competition. The study in this work takes into account the principal sources of uncertainty—the load forecast and the actions of the other competitors. The formulation and solution methodology effectively exploit a Lagrangian relaxation based approach. We have conducted a wide range of numerical studies; a sample of numerical results are presented to illustrate the robustness and superiority of the analytically developed bidding strategies.