求AHP 层次分析法 英文文献（有中文译文更好）

Question

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Answer 1

管理风险
管理风险是普遍存在于民营企业中的。这是由于企业治理结构的不健全、管理的漏洞和失误所带来的。主要表现在：
（1）家长制作风。民营企业一般产权结构单一，管理中以个人忠诚度为管理纽带的家长制作风盛行，组织机构频繁变动，用人带有感情色彩，决策者决策只凭经验。
（2）内部控制制度不完善。由于产权关系的约束，民营企业难以建立规范科学的激励和约束机制。不愿用股权补偿经验者、奖惩没有统一标准等其他直接或间接原因都妨碍建立有效的激励和约束机制。
（3）目光短浅。多数民营企业都很重视有形资产和短期利益，而对一些无形资产考虑较少，例如品牌、企业文化等，这样不利于企业长期发展。
（4）资源配置不佳。由于企业内部家长制作风的盛行，有些决策者在用人上过于相信亲朋，造成权利盲目而无控制的下放，其业务运作、财务管理、资源分配各自独立，缺乏优化资源配置的观念，难以进行过程控制的评价和考核，资源浪费大。
2．2 经营风险
民营企业的经营风险是因为生产经营方面的各种原因给企业的盈利能力带来的不确定性。“民转军”过程中的民营企业面临的经营风险尤其特殊，主要表现在：
（1）规模不经济。如果军品一次性订货批量过小，会造成企业生产和经营规模上的不经济，甚至可能陷入亏损；军品订货间隔期长，企业不可避免地存在专用设备、人才等闲置浪费现象，影响企业的盈利能力。军品生产成本高，如果企业付出的高昂生产成本不能得到合理补偿，企业的盈利水平必然会受到严重影响。
（2）专用性强。包括专用性投资风险和军品技术开发的风险。由于用于军品生产的投资，通常被“锁定”在军品生产上，这样军品生产波动性较大的风险将转移为企业投资风险。同时，在军品的技术开发中，不仅需要投入巨额资金和大量的人力、物力，而且面临失败率极高的风险。
（3）进入和退出门槛高。军品生产是高技术、高知识、高资本密集性生产，民营企业相对比较缺乏对军品生产的技术和知识积累，且国防产业的实际成本和机会成本都比较高；民营企业长期按照商业规范和标准运作，一时难以适应特殊的军用标准；军品安全保密要求高，如进入军工产业，要进行很多专用固定资产投资，导致退出门槛高。
（4）不平等。经济地位不平等：民企可能不能优先获得政府的军品订货，在获得专项资金和技术方面可能也不如国有军工企业那样及时；税收方面也存在不合理制度。信息占有不平等：由于国防科技工业的特殊性，很多需求信息不能让民企占有太多。而国有军工企业可以凭借其与政府之间的特殊关系获得较多的信息。
（5）技术不完善。军品生产是高技术、高知识、高资本密集性生产，民营企业相对比较缺乏对军品生产的技术和知识积累，从而导致了技术风险。
2．3 财务风险
财务风险是指企业在生产经营活动中，由于企业内部和外部各种不确定性因素的影响，企业的投资活动和筹资活动使企业财务状况、经营成果和现金流量具有不确定性，从而使企业有遭受损失的可能性。对于民营企业来说，财务风险主要表现在以下几个方面：
（1）筹资形式不确定。我国目前很多民营企业零负债经营，一方面是因为银行对民营企业放贷过于苛刻，另一方面是我国的民营企业家缺乏企业财务风险管理理念。企业在达到最优负债规模时，财务风险是最小的，同时零负债也意味着高风险。偏离最优负债规模的程度越大，财务风险也随之增加。民营企业中不确定的筹资形式，使筹资的实际结果与其目标之间存在较大偏差。投融资矛盾突出，过度的负债筹资面临到期不能还本付息的风险
Risk management
Management of risk is commonly found in private enterprise. This is because the corporate governance structure is inadequate, the management of the loopholes and errors arising. Mainly reflected in:
(1) paternalistic. Property rights of private enterprises in general structure, the management of personal ties of loyalty to parents to manage the production of wind prevailed, frequent changes in organization, people with emotional, just the experience of policy makers in decision-making.
(2) inadequate internal control system. Bound as a result of the relationship between property rights, private enterprises it is difficult to establish norms of scientific incentive and restraint mechanisms. Do not want to experience with stock-based compensation, there is no uniform standard incentive and other reasons, directly or indirectly, impede the establishment of an effective incentive and restraint mechanisms.
(3) short-sighted. Most private enterprises attach great importance to the tangible assets and short-term interests, and less on some intangible assets to consider, such as brand, corporate culture, this is not conducive to long-term development of enterprises.
(4) the poor allocation of resources. Parents as a result of internal production prevailing wind, and some policy makers believe in the people's friends and relatives too, resulting in no control of the blind the right to decentralize their operations, financial management, resource allocation separate, the lack of the concept of optimizing the allocation of resources, it is difficult to carry out process control evaluation and assessment, major waste of resources.
2.2 operational risks
Operational risks of private enterprises because of production and management aspects of a variety of reasons for the profitability of enterprises brought about by uncertainty. "The people to the military" in the course of the operation of private enterprises face special risks in particular, mainly reflected in:
(1) scale. If the military one-time order quantity is too small, this will result in production and operation of non-economic scale, and may even fall into a loss; military long lead times, inevitably there is a dedicated business equipment, idle waste of human resources, impact on the profitability of enterprises . Military production costs are high, if the enterprises to pay the high cost of production can not be reasonable compensation, corporate profits will inevitably be seriously affected.
(2) a dedicated and strong. Including investment-specific risks and the risk of military technology development. For military production as a result of investment, are often "locked" in military production, military production so the risk of greater volatility will be transferred investment risk for enterprises. At the same time, military technology development, not only need to input huge amounts of money and a lot of manpower, material, and face the risk of very high failure rate.
(3) high threshold of entry and exit. Military production is the high-tech, high knowledge, high capital-intensive production, and private enterprises are relatively lack of military technology and the accumulation of knowledge, and the defense industry and the opportunity cost of the actual cost higher; long-term private sector norms and standards in accordance with the commercial operation, making it difficult to adapt to specific military standards; military security requirements, such as access to military industry, would involve a lot of dedicated investment in fixed assets, resulting from the high threshold.
(4) inequality. The unequal economic status: private enterprises may not be able to give priority to the Government's military orders, access to special funds and technology may not be as timely as the state-owned military enterprises; tax system there are also unreasonable. Unequal possession of information: as a result of the special nature of national defense science and technology industry, a lot of demand for private information should not be allowed to occupy too much. The state-owned military industrial enterprises and the Government by virtue of their special relationship between access to more information.
(5) technical imperfections. Military production is the high-tech, high knowledge, high capital-intensive production, and private enterprises are relatively lack of military technology and the accumulation of knowledge, resulting in a technical risk.
2.3 Financial Risk
Financial risk refers to enterprises in the production and operation activities, as a result of a variety of internal and external uncertainties, corporate investment activities and fund-raising activities so that the financial situation of enterprises, operating results and cash flow uncertainty, so that enterprises there is the possibility of loss. For private enterprises, the financial risk mainly in the following areas:
(1) the form of uncertain funding. At present, many private enterprises of China's zero-liability management, on the one hand, because the lending banks to private enterprises are too harsh, on the other hand, China's private entrepreneurs lack the concept of enterprise financial risk management. Liabilities of enterprises in the optimal size of the financial risk is the lowest, zero at the same time also means that high-risk liabilities. Deviation from the optimal size of the extent of liability the greater the financial risk increases. Private enterprises in the form of uncertain funding, so that the actual results of financing its large deviation between the target. The obvious contradiction between the investment and financing, over-funding of liabilities faced by the risk of debt service due can not be

Answer 2

Improving AHP for construction with an adaptive AHP approach (A3)

Chun-Chang Lina, , Wei-Chih Wanga, , and Wen-Der Yub,

Department of Civil Engineering, National Chiao Tung University, 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan

Abstract
The Analytic Hierarchy Process (AHP) approach is widely used for multiple criteria decision-making in construction management. However, the traditional AHP requires that decision makers remain consistent in making pairwise comparisons among numerous decision criteria. Accurate expression of relative preferences on the criteria is difficult for decision makers due to the limitations of the 9-value scale of Saaty. Although Saaty proposed a method to assess the consistency of pairwise comparisons, no automatic mechanism exists for improving the consistency for AHP. This work proposes an adaptive AHP approach (A3) that uses a soft computing scheme, Genetic Algorithms, to recover the real number weightings of the various criteria in AHP and provides a function for automatically improving the consistency ratio of pairwise comparisons. A real world construction management example for determining the weightings of the multiple criteria for a best-value bid is chosen as a case study to demonstrate the applicability of the proposed A3. The application results show that the proposed A3 is superior to the traditional AHP in terms of cost effectiveness, timeliness, and improved decision quality.

Keywords: Multiple criteria analysis; Analytic hierarchy process; Soft computing; Genetic Algorithms; Construction

Article Outline
1. Introduction
2. The traditional AHP approach for MCDM problems
3. Proposed adaptive AHP approach (A3)
3.1. Selection of soft computing scheme
3.2. Definition of objective functions
3.3. Determination of coding scheme
3.4. Formulation of GA for A3
3.5. GA operations in A3
4. Demonstrated case study
4.1. Description of case background
4.2. Weighting approaches
4.2.1. Traditional AHP weightings (AHP weightings)
4.2.2. Proposed A3 weightings (A3 weightings)
4.3. Evaluation results
5. Benefits and limitations
5.1. Benefits
5.1.1. Cost effectiveness
5.1.2. Timeliness
5.1.3. Improved decision quality
5.2. Limitations
6. Conclusion
Acknowledgements
References

1. Introduction
Construction management involves numerous multi-criteria decision-making (MCDM) problems. Correctly weighting various criteria is the key issue in a MCDM problem. The use of the Analytic Hierarchy Process (AHP) approach [1] and [2] to assess the criterion weightings in MCDM recently has become popular in different areas of construction management, such as project management [3] and [4], contractor selection [5], [6], [7] and [8], procurement [9], facility location determination [10], construction safety management [11], project/proposal evaluation [12] and [13], green building evaluation [14], and technology/equipment/material selection [15], [16] and [17].

The AHP method can be used to construct the additive value functions for preferentially independent MCDM problems [18] and [19] and to determine the membership values of the elements in a fuzzy set [20]. However, several researchers, including Triantaphyllou and Mann [21] and Lakoff [22], have pointed out the weakness of AHP in assessing the relative importance weights of various criteria. This weakness results primarily from two limitations: (1) the difficulty of using Saaty's discrete 9-value scale to reflect the belief of decision makers in the relative importance relationship among the various criteria; (2) the difficulty of identifying the in-between numbers of fuzzy sets. Saaty's discrete 9-value scale method forces decision makers (DMs) to select numbers from the finite set {1/9, 1/8, 1/7, …, 1, 2, 3, …, 7, 8, 9}, contradicting the real world fuzzy memberships of elements in a fuzzy set. In most real world problems, the membership values in a fuzzy set take on continuous values (namely real numbers) rather than discrete numbers [21]. Triantaphyllou and Mann [21] found that this limitation can cause extremely high failure rates for AHP. Furthermore, the ability of humans to accurately express their knowledge decreases with increasing problem complexity. Thus, as the number of criteria in AHP increases, DMs are likely to make inconsistent judgments during pairwise comparison. The above two limitations are sources of the high Consistency Ratio (CR), that is the high inconsistency, when adopting the AHP method.

Saaty devised a method of measuring CR (see Saaty [2]). If CR exceeds 0.10, the pairwise comparison needs to be reassessed. The reassessment process is tedious and does not guarantee the consistency of pairwise comparisons. Thus, another reassessment is necessary if the resulting CR remains unsatisfactory. The reassessment process is impractical in situations where time is crucial for DMs who are top managers of a company or for urgent MCDM problems that must be solved rapidly. Reassessment is simply too expensive for sorting out inconsistencies [23]. Tam et al. [23] proposed a tool that aids AHP decision-making that changes the consistency checking from a 1–9 value scale to a 1–3 value scale, thereby reducing the time required to handle inconsistency in decision-making for construction problems. Alternatively, the development of a method that automatically improves the CR of pairwise comparisons and recovers the continuous relative importance weights of various criteria is extremely attractive.

This investigation develops an Adaptive AHP Approach (in brief A3) using Genetic Algorithm (GA) to recover the continuous relative importance weights of the various criteria based on two objective values: (1) CR, and (2) the difference of the derived pairwise weighting matrix (PWM) from the initial PWM. Since the search process of GA is guided by minimizing CR, it results in an adapted PWM with lower CR, which is acceptable in terms of the consistency requirements of AHP. The search process is also guided by minimizing the difference from the initial PWM. Thus, the resulting PWM reserves the original beliefs of the DM regarding the relative importance relationship among the criteria. The proposed A3 also provides an automatic mechanism for improving CR, and thus eliminates the reassessment process of AHP.

The remainder of this paper is organized as follows: Section 2 reviews the traditional AHP approach and its application in determining the fuzzy weightings of the various criteria in the MCDM; Section 3 then presents the proposed A3; next, Section 4 analyzes the proposed A3 for a real world best-value-bid MCDM problem to demonstrate the applicability of the proposed A3; finally, advantages, limitations, and future research directions are discussed and conclusions presented.

参考资料：http://www.sciencedirect.com/