招投标 [复制链接]

道。现有龙华港泵闸在建造时，综合考虑了航运、用地等各方面的因素，为地区的发展发挥了应有的作用。然而近年来随着城市建设的迅速推进和城市功能的不断提升，区域河网萎缩且受开发影响破坏性较严重，目前整个淀北片内现状河面率不足3%，龙华港泵闸现状也已不能适应经济社会发展的需求，迫切需要对龙华港泵闸进行外移和扩建。 5_XV%-wM  
2005年3月，上海市水务规划设计院完成了《龙华港泵站移址及扩建工程规划报告》，并已于2005年4月通过专家评审和市水务局的行业审查，原则同意报告中推荐的扩建及移址规划方案。 5_XV%-wM  
龙华港泵闸作为“淀北片”外围防洪除涝的主要骨干工程和最大的排涝口门，泵闸外移扩建后可为减低淀北片的除涝最高水位，加强退水置换能力、改善水环境质量等创造有利条件。 5_XV%-wM  
7.2.2 工程内容 5_XV%-wM  
(1)按规划要求外移并扩建一座净宽为12米的节制闸和一座设计流量为90 m3/s的泵站； 5_XV%-wM  
(2)拆除老泵闸，并依据河道规划断面，根据现状条件进行改建； 5_XV%-wM  
(3)本工程为ⅰ等工程。泵闸主体建筑物—泵站（闸）、进水池（消力池）、外河防汛墙为1级建筑物，其他永久建筑物级别为3级，临时建筑物级别为4级，本工程抗震烈度为7o设防。 5_XV%-wM  
7.2.3 工程建设必要性 5_XV%-wM  
(1)保障地区防汛除涝安全的需要； 5_XV%-wM  
近几年城市建设发展迅猛，雨水强排系统强度集中，对河网的蓄水、排水造成了较大压力。另一方面，龙华港泵站也是淀北片除涝排水的主力泵站。 5_XV%-wM  
(2)苏州河环境综合整治的需要； 5_XV%-wM  
龙华港泵站的扩建，将增加“淀北片”东向排水能力，减少排入苏州河的流量，减轻其防汛压力，为苏州河环境综合整治创造有利条件。同时，泵闸移址扩建后，可从分利用工程设施加强退水置换能力，也有利于“淀北片”水环境的改善。 5_XV%-wM  
(3)减轻龙华港防洪（潮）压力的需要； 5_XV%-wM  
(4)实现淀北片除涝总体规划安排的需要； 5_XV%-wM  
(5)完善地区交通网络的需要； 5_XV%-wM  
(6)按照徐汇区城市总体规划，服务世博会，促进地区发展的需要。 5_XV%-wM  
7.2.4 工程预算 5_XV%-wM  
通过工程量清单和设计单位的相关图纸，经过预算，该工程的预算为5580万元，其中用于土建的为5400万元。 5_XV%-wM  
7.3 投标报价方案比较 5_XV%-wM  
该工程机关工程预算低，投资不大，但社会影响较大，参加工程施工的单位较多，当时估计在8家以上，如果我们公司中标，主要从事施工。我们对招标单位基本情况进行了反复研究，同时对可能参与投标的单位也作了分析，尤其是投标人可能的报价从概率方面进行比较，列出了几种投标报价方案的报价值与估算成本比值的百分数、期望利润等值。在投标报价策略方面考虑采用典型竞争对手法进行分析，以确定最合理的报价，争取中标。下面是我们的具体分析过程。 5_XV%-wM  
招标单位的招标书规定，投标单位必须2007年12月31日前投送标书。我们分析，一旦中标，中标概率大小除取决己方报价与项目成本外，还要受到竞争对手的数量及对方报价大小的影响。一般来说，竞争对手越多，对手报价低于投标者报价值的可能性越多，则投标者中标概率越小，在众多对手竞争的条件下，我们从较多竞争对手中选取最具有竞争力的对手作为典型“假想敌”，尽可能获取对方的有关情报，并针对该对手的可能报价进行“模拟报价”。自己报价越合理地低于典型对手报价，击败对手的可能性也就越大。因此，击败所有对手的可能性也就越大，这就是典型对手法的基本概念，方法步骤如下： 5_XV%-wM  
(1)选中典型对手。我们分析A公司是典型对手，因此该公司过去投标而命中该项目类似工程，人才济济、公司规模档次高;弱点是接手市政方面的工程较少，但与其他对手相比，的确是劲敌。手机该典型对手历史资料及其报价策略是我们的关键任务。 5_XV%-wM  
(2)根据对方的历史资料，模拟分析这次投标的情况，估计这次典型对手各种报价可能数值及对应的报价出现次数，次数越多，其报价值可能性越大。 5_XV%-wM  
(3)自己制定多种报价方案，测算各种报价方案时己中标概率，并计算不同报价时己方盈利的可能幅度。 5_XV%-wM  
(4)根据不同中标概率和盈亏幅度作出权衡判断，选定最佳报价方案。 5_XV%-wM  
最具体运作时，根据自己的估价G、典型对手的各种可能报价Bi以及Bi出现的频率f，分析某一Bi小于己方的报价频率不应多，当然对手Bi远远大于自己的频率也不会多，也就是说对方的报价略低于己方的估价的频率可能较多，这时可计算出对手的报价B1与G的比值的概率P1。例如P1=1.18时，其概率等于其出现的频率/合计频率，即16/50=0.32，其他类推，如表7-1所示。 5_XV%-wM  
表7-1 不同Bi/G值出现的概率 5_XV%-wM  
Bi/G    0.88    0.93    0.98    1.03    1.08    1.18    1.25    1.3    1.35    合计 5_XV%-wM  
频数f    1    2    3    5    8    16    9    5    1    50 5_XV%-wM  
概率p1    0.02    0.04    0.06    0.1    0.16    0.32    0.18    0.1    0.02    1 5_XV%-wM  
求出P1值，就可以计算出机房报价C低于对手报价的概率，为此，采用表7-2中小于P1值的己方报价与成本估价的比值C/G，并求出相应的概率P。 5_XV%-wM  
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表7-2 不同B/G值时的P的值 5_XV%-wM  
我方报价/我方估价 C/G    0.85    0.9    0.95    1    1.05    1.15    1.2    1.25    1.3 5_XV%-wM  
报价低于对手报价的概率P    1    0.98    0.94    0.88    0.78    0.62    0.3    0.12    0.02 5_XV%-wM  
表7-2中P为某一C/G值能成为最低报价的概率，故P等于所有高于C/G的Bi/G对应概率之和。则己方拟采用C/G值为1.15，则由表7-3可知，高于1.15的Bi/G值为1.18、1.25、1.30、1.35，其对应的概率值分别为0.32、0.18、0.1、0.02，故C/A=1.15的概率： 5_XV%-wM  
P=0.32+0.18+0.1+0.02=0.62 5_XV%-wM  
同理，表格7-2中其他与C/G相应的各P值都可以这样求出。 5_XV%-wM  
有了以上数据，就可以分析确定己方的投标策略。此时取自己的估价G作为工程项目的报价基础，则计算出其每一报价的预期利润。计算依据为直接利润等于投标报价减估算成本G，因此在确定投标策略时，要以预期利润作为比较的依据，则预期利润E(A)等于相应的概率乘以相应的直接利润。如表7-3所示。 5_XV%-wM  
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表7-3 投标报价预期利润（G为工程估价） 5_XV%-wM  
投标报价    0.85G    0.9G    0.95G    1G    1.05G    1.15G    1.20G    1.25G    1.30G 5_XV%-wM  
直接利润    -0.15G    -0.10G    -0.05G    0    0.05G    0.15G    0.20G    0.25G    0.30G 5_XV%-wM  
概率    1    0.98    0.94    0.88    0.78    0.62    0.3    0.12    0.02 5_XV%-wM  
预期利润    -0.15G    -0.10G    -0.05G    0    0.039G    0.093G    0.06G    0.03G    0.006G 5_XV%-wM  
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由于5-3的结果分析可知，投标者按1.15G报价是最有利的。工程项目估价为5580万元，则应报价6417万元。考虑到落标的可能，投标者的预期利润为0.093G，即519万元。 5_XV%-wM  
经过运用典型竞争对手的分析计算，一旦认定这种分析的可靠性，那么就应该当机立断，而不能瞻前顾后。报出的投标价格就只有一次，只要击败了典型对手，其他竞争对手便不难战胜，后来，开标的实际情况表明，我们以6420万元的价格中标，优势明显。 5_XV%-wM  
8 致谢 5_XV%-wM  
感谢胡良明老师在我学习期间，对我在学习上的悉心教导与帮助，以及在我作毕业论文时给我提出的宝贵意见。胡老师以其严谨求实的治学态度、高度的敬业精神、兢兢业业、孜孜以求的工作作风和大胆创新的进取精神对我产生重要影响。您渊博的知识、开阔的视野和敏锐的思维给了我深深的启迪。同时，在此次毕业论文过程中，从论文的选题，结构安排，到论点的推敲和文字的修改，直到最后的定稿，得到了我的老师的悉心教导和帮助。我的思想更加开阔，受益匪浅，在此表示我对老师您衷心的感谢！ 5_XV%-wM  
感谢在我三年学习过程中，认真培育和教导我的水利与环境学院的老师们，是你们给了我分析问题和解决问题的工具，给我打开了未来之门的钥匙。同时还要感谢所有帮助和关心过的同学。在与你们一起学习和讨论问题的过程中，使我受到了很多启发，学到了很多东西。 5_XV%-wM  
感谢院领导对我的关怀与教导，我将不断的鞭策自己，无论将来遇到什么困难，我都会勇敢的前进 5_XV%-wM  
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9 结束语 5_XV%-wM  
笔者通过工程投标过程主要环节和关键影响因素的认真分析，尤其通过亲身参与几个工程标书编制的实践，深深的感受到报价的编制是有技巧的，但任何一个专业编制技巧都无法完全脱离工程自身的特点和企业自身的投标策略，只有善于根据工程特点性质和当地施工条件进行深入分析，结合企业优势，才能在初步报价基础上做出合理调整，在保证实现企业投标目的的基础上，编制出一套具有竞争性的最终报价。 5_XV%-wM  
10 参考文献： 5_XV%-wM  
[1] 宋彩萍.工程施工项目投标报价实战策略与技巧.北京：科技出版社，2004年2月.100～104 5_XV%-wM  
[2]李坚，董志英.技术标书实录.北京：知识产权出版社，2005年9月.1～6 D]hwG0Chd  
[3]龚维丽.工程造价的确定与控制[M].北京：中国计划出版社，2000年 D]hwG0Chd  
[4]彭思志，孙勇毅，谈投标报价策略和作价技巧「J].建筑经济，2000年5月.20～100 D]hwG0Chd  
[5]何增勤.工程项目投标策略「M].天津:天津大学出版社.2004.15～30 D]hwG0Chd  
[6]李洁.建筑工程承包商的投标策略[M].北京:中国物价出版社，2000.2～12 D]hwG0Chd  
[7]薛锐，王树林.浅谈投标策略及其应用 黑龙江交通科技，2004(19).35～50 D]hwG0Chd  
[8]谭德精，杜晓玲，吴宇红，工程造价确定与控制[M].重庆:重庆大学出版社，2001.80～120 D]hwG0Chd  
[9]王卓甫工程项目风险管理~- T论、方法与应用[M].北京:中国水利水电出版社，2002.56～70 D]hwG0Chd  
[10] R. E.麦格尔著，孙济元，杨少俊译.风险分析概论[M].北京:石油工业出版社，1995.70～86 D]hwG0Chd  
[11l郭钟伟.风险分析与决策「m].北京:机械工业出版社，1994.23～28 D]hwG0Chd  
[12]宋雪荃，建设工程投标报价趋势探讨[J ].新疆石油教育学院学报，2004 (2).107～123 D]hwG0Chd  
[13]尹贻林等.工程造价计价与控制[M].北京:中国计划出版社，2000.67～73 D]hwG0Chd  
[14]刘伊生等.工程造价管理基础理论与相关法规[M].北京:中国计划出版社，2003.15～20 D]hwG0Chd  
[15]陈文珍.工程投标项目风险及对策[J].建筑经济，1999 (5).17～30 D]hwG0Chd  
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附件 D]hwG0Chd  
Analysis of equilibrium about bidding strategy ofsuppliers D]hwG0Chd  
with future contracts D]hwG0Chd  
Zhiqiang Yuan *, Dong Liu, Chuanwen Jiang D]hwG0Chd  
Electric Power School, Shanghai Jiao Tong University, Shanghai 200030, PR China D]hwG0Chd  
Received 6 May 2005; accepted 9 August 2006 D]hwG0Chd  
Available online 2 October 2006 D]hwG0Chd  
Abstract D]hwG0Chd  
In this paper, the supply function model is employed to simulate the bidding strategy of suppliers in the power pool, and models of the supply function with future contracts are presented. It is proved that only one of the parameters between slope and intercept of the bidding curve is an independent variable in order to achieve definite equilibrium. In the meantime, the equilibria of the bidding strategy about suppliers are studied when different intercepts of the bidding curve are chosen. Some examples are employed to study the Nash equilibrium strategies of suppliers with different future contracts in various bidding strategy models. The results show that the equilibria are different in different bidding strategy models, but the future contracts can effectively make spot prices decrease in all the models. D]hwG0Chd  
1. Introduction D]hwG0Chd  
The electric power industry worldwide is experiencing unprecedented restructuring from the traditional integrated regulation monopoly to a competitive power market. The object of deregulation is to introduce a competition mechanism into the power market and provide incentives for efficient operation of the power industry, eventually reducing the market price. The ideal market is a perfect competitive market in which participants bid their marginal cost into the market. Consequently, the market price is low in this kind of market. However, the electricity market is different from other merchandise markets. In the power market, only a few suppliers can provide power services in some geographic region. This is due to the restriction of some factors, such as large investment scale, small demand elasticity, transmission constraints and no largely storable electricity. Consequently, the actual power market is more close to an oligopolistic market in which the suppliers can achieve maximum profit through strategic bidding. That means that the generation companies possess market power, which is harmful to the operation of the power system D]hwG0Chd  
and will make electricity price far higher than the marginal cost of the power market. One famous example is the electricity crisis of California of America in the summer 2000, which made the electricity price far above competitive levels. So, it is meaningful to study the bidding strategy of suppliers. D]hwG0Chd  
Generally speaking, there are basically three ways for a supplier to develop an optimal bidding strategy [1]. The first one relies on estimation of the market clearing price (MCP) in the next trading period, the second one is based on estimation of the bidding behavior of rival participants and the third one is game theory based. The models of bidding strategy of suppliers based on game theory used in the power market are mainly Cournot, Bertrand and the supply function model [2]. Among these models, both the Cournot and supply function models are widely studied. D]hwG0Chd  
Probability theory is employed to study the bidding strategy of suppliers based on the supply function model in Refs. [3,4], where the bidding coefficients of rival participants are assumed to obey a joint normal distribution. In Ref. [5], a conjectured supply function is utilized to simulate the bidding strategy of suppliers. In Ref. [6], a supply function

model whose slope and intercept of the bidding curve vary with the same scale is employed to analyze the bidding strategy of suppliers, where the influence of contracts for difference CFDs [7] (CFDs means that if the spot price rises above the strike price, which is the price level at D]hwG0Chd  
which the contract can be called, suppliers compensate retailers for the difference, but if the spot price falls below the strike price, then the retailers compensate suppliers for the difference) on market equilibrium is considered. In Ref. [8], the concept of virtual rival and the method of parameter estimation are introduced to study the bidding strategy of suppliers based on game theory, where the slope and intercept of the bidding curve is also assumed to vary with the same scale. D]hwG0Chd  
Forward contracts are one of the efficient means for risk management, and it is applied in the power market of many countries. The power pool will plan the electricity of suppliers according to the market rules, the bidding price of suppliers and forward contracts while there are forward D]hwG0Chd  
contracts between suppliers and buyers. The bidding strategy of suppliers will vary with the forward contracts to achieve maximum profit. In this paper, a supply function model is used to simulate the bidding strategy of suppliers in a power pool. Firstly, a supply function model with forward D]hwG0Chd  
contracts is presented. Accordingly, it is proved that only one of the parameters between the slope and intercept of the bidding curve can be strategy variable in order to achieve definite equilibrium (the Nash equilibrium is a strategy profile in which each player’s part is as good a response to what the others are meant to do as any other strategy available to that player [9]). Secondly, the equilibria of the market are studied when different intercepts of the bidding curve are chosen. Finally, the effect of different future contracts on the equilibrium strategies of suppliers in various bidding strategy models is analyzed. Besides, the bidding strategies are also studied while generation constraints are active. D]hwG0Chd  
2. Supply function equilibrium model with forward contracts D]hwG0Chd  
It is supposed that the inverse demand function is a linear function, that is D]hwG0Chd  
                                       （1） D]hwG0Chd  
where p is market price, r and s are the intercept and slope of the inverse demand function, respectively,   is the generation of the supplier i and N is the number of suppliers. Eq. (1) can be transformed into the electricity demand function, D]hwG0Chd  
                            （2） D]hwG0Chd  
  ，       D]hwG0Chd  
The bidding curve of the supplier is assumed as a linear function, D]hwG0Chd  
                                 （3） D]hwG0Chd  
where   and   are the intercept and slope of the bidding curve, respectively, both of which are larger than zero, and i = 1,. . . ,N. D]hwG0Chd  
The electricity price is assumed to clear at the uniform price in the power pool, and then, the generations of the suppliers are calculated according to Eqs. (2) and (3). D]hwG0Chd  
If the forward contracts is  while the dealing price is , the equilibrium state of the suppliers can be obtained by maximizing an individual profit function of each supplier. D]hwG0Chd  
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     （i=1……N）                    （4） D]hwG0Chd  
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Without loss of generality, the cost function of a supplier is assumed to be a quadratic function of active power generation. That is: D]hwG0Chd  
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Then, the generation of supplier i is D]hwG0Chd  
D]hwG0Chd  
Correspondingly, the system marginal price is D]hwG0Chd  

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If both the intercept and slope of the bidding curve are chosen to be independent strategy variables by the supplier, the maximum profit of supplier i can be achieved when the following differential equation is satisfied D]hwG0Chd  
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Eqs. (8) and (9) show that the intercept and slope of the bidding curve are not independent. The 2N variables, xi and bi, need to be calculated by means of N equations, so there exists an infinite number of equilibrium states. That means only one of the parameters between the intercept and slope of the bidding curve is independent in order to achieve a definite equilibrium. D]hwG0Chd  
If the slope of the bidding curve is chosen to be the strategy D]hwG0Chd  
variable by supplier i, Eqs. (6)–(8) yield the following optimal reaction function of supplier i   D]hwG0Chd  
is the point that the bidding curve passes through. D]hwG0Chd  
If the intercept of the bidding curve is chosen to be the strategy variable by supplier i, Eqs. (6), (7) and (9) yield the following optimal reaction function of supplier i D]hwG0Chd  
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The equilibrium bidding strategy can be obtained by solving Eqs. (10) or (11). D]hwG0Chd  
When the slope of the bidding curve is chosen to be the strategy variable by supplier i, if the bidding curve passes through the point D]hwG0Chd  
, which means the supplier I is bidding its marginal cost at generation of forward contracts, the bidding curve is D]hwG0Chd  
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Then, the generation of supplier i is D]hwG0Chd  
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Correspondingly, the system marginal price is   D]hwG0Chd  
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According to differential equations akin to Eq. (8), the following optimal reaction function of supplier i exists, D]hwG0Chd  
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Eq. (15) shows that the strategy variable  is independent of the forward contracts if the bidding curve passes through point  . D]hwG0Chd  
3  The equilibrium of supply function model with generation constraints D]hwG0Chd  
Generation constraints have not been considered in the optimal reaction function of the models mentioned above. When generation constraints are considered, if the generation of supplier i calculated from Eqs. (10), (11) or (15) is above the upper limit of generation of supplier i, his generation will be set to the limited value. If the market price calculated from the optimal reaction function of the suppliers is low and the calculated generation is below the minimum generation of supplier i, it is possible that the profit of supplier i is negative. As a result, supplier i will buy electricity from the spot market to meet the forward contracts and maximize its own profit. The necessary condition of supplier  to generate electricity is D]hwG0Chd  
D]hwG0Chd  
where  is the market price without suppliers If inequality (16) is met, let  =  , otherwise, let   = 0. D]hwG0Chd  
If there exist suppliers whose generation constraints are active, then suppliers whose generation constraints are not active will be faced with the following residual demand function: D]hwG0Chd  
D]hwG0Chd  
where M–L and N–M are the numbers of suppliers whose generation is over maximum and under minimum generation, respectively.  and  are the maximum and minimum generations of supplier i, respectively. D]hwG0Chd  
The generations of suppliers whose generation constraints are not active are calculated by using Eqs. (17),(10), (11) or (15) again. If there still exist suppliers whose generation exceeds their generation limit, the residual demand function will be calculated again. The calculation processes are repeated until there no longer exist suppliers whose generation exceeds their generation limit, and then, the threshold value of the bidding strategy of supplier i whose generation constraints are active is D]hwG0Chd  
D]hwG0Chd  
where   is the maximum and minimum generation of supplier i. D]hwG0Chd  
The threshold value of the bidding strategy for supplier indicates that the supplier will choose any strategy that is less than the threshold value if the maximum generation is active, while the supplier will choose the threshold value if the minimum generation is active. D]hwG0Chd  
4. Numerical examples D]hwG0Chd  
An example with six generators is employed to calculate the market equilibrium state of the different bidding strategy models. Firstly, with or without forward contracts, the variety of equilibrium states is analyzed when either a different intercept is chosen or the bidding curve passes through point   if the slope of the bidding curve D]hwG0Chd  
is used as the strategy variable. The market equilibrium states in the various bidding strategy models of the suppliers who chose the slope or the intercept of the bidding curve as the strategy variable are compared when the slope is selected as strategy variable by some suppliers while D]hwG0Chd  
other suppliers choose the intercept as strategy variable. D]hwG0Chd  
In the paper, the inverse demand curve is assumed as Accordingly, the demand curve is The cost coefficients of the suppliers are listed in Table 1.