Under the impact of the government’s policies to expand domestic demand and maintain economic growth, the western area acquired a large amount of funding for infrastructure construction. The high-grade highways became the key project attracting investment because of its great development potential and strong transportation adaptability. However, the special geographical conditions in the western area created numerous barriers for the construction of high-grade highways, including many investment influencing factors, great investment risks and uncertainties, and high difficulty in defining the investment effect. In view of the goals in technical advancement and economic rationality for the investment scheme of high-grade highways, the possible influencing factors of the investment scheme decision-making of the high-grade highways in western China were first given comprehensive analysis. Through literature review and field investigation, 67 influencing factors of investment scheme decision-making were determined by the cost decomposition method and expert investigation method. Then, the influence degree of each factor was analyzed by using the Delphi method and entropy method. According to the sorting results, 49 important factors were reserved as the detailed index for investment scheme decision-making. Afterwards, the index system for investment scheme decision-making consisting of 2 target factors, 5 first-level indexes, 13 second-level indexes, and 49 third-level indexes was constructed. Based on this, the decision-making model of investment scheme for high-grade highways was established by combining Analytic Hierarchy Process (AHP) and grey theory. Specially, the standardized index matrix of investment scheme was determined by AHP, and the relation degree of each scheme was calculated by grey correlation degree, and then the optimal scheme was shaped by the size of comprehensive relation degree. Finally, the grey correlation degree decision-making model of the investment scheme was applied to a highway project located in Gansu province, China. The results showed that the optimal investment scheme determined by the decision model was consistent with the scheme actually adopted, indicating that the model has good operability and practicability. In this paper, a grey correlation degree decision-making model of investment scheme for high-grade highways in western China was proposed, providing an effective theoretical basis and valuable practical experience for the investment scheme decision-making of transportation infrastructure under special environments.
Due to the fact that the gap between the eastern and western areas has been further widened with the rapid development of science and technology and economic level in China, the state implemented develop-the-west strategy and adopted a series of preferential policies, so as to accelerate the economic growth of the western areas and to solve the problem of unbalanced regional economic development in China. In this context, the western areas vigorously carry out the transportation infrastructure construction, including the high-grade highways and other projects, to improve the regional development environment, enhance regional accessibility, promote economic integration, and optimize the allocation of resources, thus to propel the rapid economic development in the western areas. During the period of “The 13th Five-year Plan,” the expected total investment for a highway project in western China reaches RMB 4.9 trillion or more. The provincial secondary highways and above accounts for more than 70%, which means that each city and each county has the secondary highways. Meanwhile, the newly added mileage of high-grade highways is expected to reach 25000 km or more, almost doubling the length in “The 12nd Five-year Plan.” Compared with other projects, the difficulty of investment scheme decision-making for high-grade highways in western China is greatly reinforced due to existing characteristics including fixed financing channels, complicated investment structure, slow turnover of construction funds, and special regional environment. In order to avoid the phenomena that investment cannot reach expected effect because of decision-making errors and that it will result in unnecessary waste, it is urgent to conduct overall planning and integration of high-grade highways in western China, which is beneficial for the benign development of the highway construction in western China, and to conduct comprehensive studies on the investment scheme decision-making when facing the government’s abundant investment.
The investment scheme decision-making refers to the process of selecting and optimizing investment scheme from both technical and economic aspects [
In the practical application, Narula et al. [
Grey correlation degree is a model to quantitatively analyze the integration degree between subsystems and systems [
Overall, there have been many scholars over the years conducting research on the investment scheme decision-making content and scope from the perspectives of project risks, costs, and benefits of the highway project. However, the study on investment scheme decision-making for highways in western China is relatively few in terms of economic rationality and technical advancement, especially the lack of targeted study on investment decision-making index factor and on evaluation model. Compared with general highway engineering, the investment scheme of high-grade highway in western China is affected by a variety of decision factors with great fuzziness. Given this, it is necessary using the grey correlation degree method to quantize various fuzziness and subjective factors, which will make the decision to be more scientific, reasonable, and reliable. Therefore, it is imperative to construct the index system of the investment scheme decision-making suitable for the high-grade highways project in western China and to establish the corresponding decision-making model, so as to ensure the expected effect and target of the investment scheme.
Four specific steps are as follows: Determine the influencing factors of investment scheme decision-making. In this paper, the cost decomposition method and expert investigation method were used for preliminarily determining various factors that may affect the investment scheme decision-making of the high-grade highways in western China. Construct the index system of investment scheme decision-making. In this step, the degree analysis of influencing factors was conducted and the key decision factors were screened out by using the Delphi method and entropy method to carry out and to screen out. Establish the investment scheme decision-making model. According to the established index system, the investment scheme decision-making model of high-grade highways was constructed by combining the analytic hierarchy process (AHP) and the grey correlation degree theory. The verification of model effect: through an example of the investment scheme decision-making of the high-grade highway in western China, the application effect of the established investment scheme decision-making model was verified.
For the specific research framework procedure, see Figure
Schematic diagram of research framework.
In order to ensure the technical advancement and economic rationality of the investment scheme of the high-grade highways in western China, the relevant influencing factors can be considered from the following five aspects: construction condition, technical ability, economic benefit, social benefit, and environmental benefit. According to field investigation, with reference to the
Factor graph of decision-making influence for investment plan for high-grade highway in the western region.
In Figure Adaptability to natural conditions A1: location A11; geological factor A12; landform A13; water regime A14; climate A15; and topography A16 Adaptability to social conditions A2: government funding A21; policy A22; talents A23; support A24; tax incentives A25; and market conditions A26 Survey and design capability B1: building scale B11; technical standard B12; route trend B13; major control points B14; and traffic forecast quantity B15 Construction technology capability B2: construction technology of roadbed, road surface, water proof, and drainage system B21; construction technology of highway appurtenance B22; construction technology of bridge and culvert B23; application ability of new construction technology B24; safety of technical solutions B25; construction technology and operation B26; and comprehensive management capability of site construction B27 Total investment estimation C1: estimation of fixed assets C11; estimation of liquidity C12; and estimation of other costs C13 Financial evaluation conclusion C2: financial internal rate of return C21; capital gain rate C22; gain rate of all investment parties C23; financial net present value C24; investment payoff period C25; return on investment C26; payment date of loan C27; debt service coverage ratio C28; and debt-to-asset ratio C29 National economic evaluation conclusion C3: economic internal rate of return C31; economic net present value C32; economic benefit cost ratio C33; and economic Net Present Value Rate C34 Social effects D1: influence rate of local employment D11; influence rate of local customs D12; influence rate of industrial structure D13; and influence rate of traffic distribution D14 Land using effect D2: immigrant resettlement rate D21; land acquisition rate D22; pipeline mobility D23; and traffic diversion rate D24 Development effect among regions D3: resource development utilization D31; percentage of improvement in living standards D32; urbanization rate D33; and land structure optimization rate D34 Environmental pollution control effect E1: ratio of water pollution control E11; ratio of air pollution control E12; ratio of noise pollution control E13; ratio of solid waste control E14; and ratio of liquid waste control E15 Recovery effect of ecological damage E2: soil erosion rate E21; reclamation rate of temporary ground E22; implantation rate of green space E23; return rate of cultivated land E24; return rate of land fertility E25; and repair rate of Earth excavation E26 Biological conservation effect E3: survival rate E31; vegetation coverage E32; species protection rate E33; and crop growth rate E34
In the diagram about influencing factors, some factors, the key factors of the decision-making, may have a significant impact on the investment scheme decision-making of high-grade highways, which could cause results unacceptable to the builder. Project builders should devote limited energy and resources to the analysis and research of key decision factors, so it is significant to screen out them from numerous influencing factors.
The entropy method is an objective evaluation method that can reduce subjective interference. Specially, the greater the changes of the information are, the more information will be presented; the greater the role in the comprehensive evaluation is, the more weight it is given to [
Based on this, due to the existence of a large number of influencing factors, it is necessary to fully consider different expert opinions. In order to avoid subjective influence, in this paper, the entropy method was used to screen out the key decision factors on the basis of considering the investment scheme target of high-grade highways and its characteristics and referring to opinions from technical and management experts.
According to the scale of the influence degree of the investment scheme decision, each factor in the diagram about influencing factors was scored. The scoring criteria are shown in Table
Scoring criteria about influence degree on decision-making.
Influence degree on decision-making | Score |
---|---|
High | 5 |
Upper intermediate | 4 |
Intermediate | 3 |
Medium-low | 2 |
Low | 1 |
Between the adjacent influence degree | The average of adjacent numbers |
The selection of experts and the reliability test of the questionnaire before the evaluation of the impact factors are indispensable and necessary: Selection of experts: as high-level highway projects featuring high investment amounts, complex technical programs, and significant social and ecological impacts, their construction requires the cooperation of various departments involving experts in many fields including technology, management, and environment. In order to avoid scoring by experts in a single field or department and to ensure reasonable groups of experts, it is planned to select 30 experts to score from 6 related departments including government administration department, project out-contracting units, survey and design units, general construction contractors, project supervisors, and equipment purchasers. The structure of experts and their engaged fields is shown in Table Questionnaire reliability: the reliability of the questionnaire was analyzed using Cronbach’s
Expert structure table.
Main departments | Number of experts | Title structure | Education structure | Seniority structure of the major | Age structure |
---|---|---|---|---|---|
Government of traffic management | 3 | Senior engineer: 1 | Doctor: 1 | ≥20 years: 2 | ≥50 years: 1 |
Department of highway engineering construction | 6 | Senior engineer: 1 | Doctor: 1 | ≥20 years: 2 | ≥50 year-old: 1 |
Department of highway engineering supervision | 4 | Chief management engineer: 1 | Doctor: 1 | ≥20 years: 1 | ≥50 year-old: 1 |
Department of highway engineering survey and design | 6 | Structural designer: 1 | Doctor: 1 | ≥20 years: 2 | ≥50 year-old: 1 |
General contractor department of highway engineering construction | 7 | Senior engineer: 2 | Doctor: 2 | ≥20 years: 2 | ≥50 year-old: 2 |
Department of highway engineering equipment procurement | 4 | Senior engineer: 1 | Doctor: 1 | ≥20 years: 1 | ≥50 year-old: 1 |
Value range of Cronbach’s
The evaluation results | Unacceptable | Need to revise | Acceptable | High reliability |
---|---|---|---|---|
Value range | 0.7 ≤ | 0.8 ≤ |
Expert domain structure.
After expert selection and reliability analysis, first, according to Table
For example, there are five third-level factors in survey and design capability
After the original data matrix formula (
For example, each third-level factor in
The calculation formula of the entropy value of each decision influence factor is as follows:
Uncertainty matrix After
Summarized results of entropy value and entropy weight of each third-level factor.
Third-level factor | Entropy value | Entropy weight |
---|---|---|
Building scale B11 | 0.5803 | 0.076 |
Technical standard B12 | 0.968 | 0.0058 |
Route trend B13 | 0.384 | 0.112 |
Major control point B14 | 0.613 | 0.071 |
Traffic forecast quantity B15 | 0.967 | 0.00601 |
According to results in Table
Decision-making evaluation index system of the investment scheme for high-grade highways in western China.
Target level | First-level index level | Second-level index level | Third-level index level |
---|---|---|---|
Technical advancement | Construction condition A | Adaptability of natural conditions A1 | Location A11; geological factor A12; landform A13; |
Adaptability of social conditions A2 | Policy A22; talents A23; support A24; tax incentives A25; | ||
Technical capability B | Survey and design capability B1 | Building scale B11; route trend B13; major control points B14; | |
Construction technology capability B2 | Construction technology of roadbed, road surface, water proof, and drainage system B21; | ||
Economic rationality | Economic effect C | Total investment estimation C1 | Estimation of fixed assets C11; estimation of liquidity C12 |
Financial evaluation conclusion C2 | Financial internal rate of return C21; capital gains rate C22; | ||
National economic evaluation conclusion C3 | Economic internal rate of return C31; | ||
Social effect D | Social effect D1 | Influence rate of local employment D11; | |
Land using effect D2 | Immigrant resettlement rate D21; land acquisition rate D22 | ||
Development effect among regions D3 | Percentage of improvement in living standards D32; | ||
Environment effect E | Environmental pollution control effect E1 | Ratio of water pollution control E11; | |
Recovery effect of ecological damage E2 | Soil erosion rate E21; reclamation rate of temporary ground E22; implantation rate of green space E23; | ||
Biological conservation effect E3 | Survival rateE31; vegetation coverage E32; |
Because of the predictive, comprehensive, and complex characteristics existed in investment decision-making process of high-grade highways in western China, as well as the uncertain and unquantifiable features for most indexes, it is essential to make scientific decision by utilizing the limited information and analyze its uncertainty.
At present, there are three common methods used for decision analysis and uncertainty system research, which are probability statistics method, fuzzy mathematics method, and grey correlation degree method. Comparison of the three methods is shown in Table
Applicability comparison of decision-making methods.
Applicability | Probability statistics | Fuzzy mathematics | Grey correlation degree |
---|---|---|---|
Decision-making scheme | |||
Research object | Randomness | Uncertain perception | Poor information |
Basic set | Cantor set | Fuzzy set | Grey obscure set |
Method | Frequency statistics | Cut set | Grey sequence operator |
Data demand | Canonical distribution | Available membership | Random distribution |
Focus on results | Connotation | Denotation | Connotation |
Research characteristics | Large sample | By experience | Small sample |
It can be learned from Table
Based on this, the process to calculate with Grey Correction Degree and AHP for the purpose of building the decision-making model of the investment plan for high-grade highway in the western region is shown in Figure
Process to calculate the decision-making model of the investment plan for high-class highway in the western region.
As Analytic Hierarchy Process (AHP) can simplify decision-making problems with multiple objectives, multiple factors, and indexes that can be hardly quantified and needs less quantitative information, it matches with the grey characteristics of the decision-making process of the investment plan for high-grade highway in the western region. Therefore, Analytic Hierarchy Process (AHP) is used to calculate index weights when synergy evaluation indexes are determined. Firstly, recursion order hierarchy of indexes should be constructed; secondly, the pair judgment matrix should be constructed. Then, the index weight of all factors constituting the judgment matrix should be obtained through consistency inspection, and level-3 indexes that have the lowest weight within the group among level-2 indexes should be eliminated according to weight result. At last, the synergy evaluation indexes of the alternative plan can be obtained.
Assume there are
Select the maximum value
Difference sequences with minimum and maximum poles are
In order to take the importance of indexes into full consideration and make the results more practical, the paper combines subjective and objective weights and determines weight through two-way combined weighting with the entropy weight method (objective weighting method) and AHP (subjective weighting method), making up for the deficiency in lateral focus on subjective or objective weight and making the weighting of all indexes more reasonable. Then, correlation calculation results are optimized accordingly. To be more specific, the weight optimization calculation formula is as
Then, the correlation value calculated with different weighting methods is the product of correlation coefficient and the weight used by each weighting method. The calculation formula is as
At last, sequence sort plans in order according to value
As shown in Figure
Planning map of related regions.
In the project area, the geomorphic unit is Yellow River valley, and Yellow River inside the area flows from east to west. The existing topography is formed due to long-term erosion accumulation and alluvial of river. As a whole, the topographic relief is high. Within the project area, the special rock and soil are mainly collapsible loess, which is widely distributed in the project area. According to data of adjacent venues, the inner layer of silt inside the venue is not self-collapsible and the collapsible foundation Earth has Grade II collapsibility. Besides, the thickness of collapsible loess is generally larger than 30 m, and the self-collapsibility is Grade IZV. Unfavorable geology that may affect the highway to be built within the project area is mainly landslides, unsteady slopes, and debris flows. Local sliding has occurred on the slope surface, and landslides are not steady enough due to the erosion and scouring in the front edge within the toe channels resulting from seasonal drainage. Once a rainstorm or an earthquake happens, it is highly possible that this landslide becomes unsteady and slides again.
Through specific feasibility analysis, direction of possible routes within the project area is studied in detail, and then 4 alternative plans, A, B, C, and D are determined. Comparison of the technical and economic indexes of all alternative plans is as shown in Table
Comparison table of the technical and economic indexes of all alternative plans.
No. | Item | Unit | Plan A | Plan B | Plan C | Plan D | |
---|---|---|---|---|---|---|---|
1 | Highway class | — | Arterial highway | Arterial highway | Arterial highway | Arterial highway | |
2 | Design life | Year | 15 | 20 | 20 | 15 | |
3 | Route length | km | 1.275 | 1.1 | 1.132 | 1.018 | |
4 | Number of lanes | pcs | Two-way 4 lanes | Two-way 6 lanes | Two-way 6 lanes | Two-way 4 lanes | |
5 | Number of horizontal curves | pcs | 4 | 3 | 3 | 5 | |
6 | Minimum radius of horizontal curve | m/pcs | 350/1 | 400/1 | 350/2 | 380/2 | |
7 | Maximum longitudinal grade | %/pcs | 1.2/1 | 1.0/1 | 1.1/1 | 1.1/1 | |
8 | Earthwork | Fill | m3 | 87,662 | 74,763 | 89,100 | 90,140 |
Excavation | m3 | 18,676 | 11,833 | 20,064 | 22,774 | ||
9 | Masonry of protection engineering | m3 | 1,589 | 1,549 | 1,455 | 1,546 | |
10 | Treatment of special subgrade | m | 597 | 482.3 | 610 | 633.5 | |
11 | Subgrade width | m | 24.5 | 29 | 29 | 32 | |
12 | Pavement | Asphalt concrete | m2 | 28,270 | 27,500 | 28,020 | — |
Cement concrete | m2 | — | — | — | 29,300 | ||
13 | Peak acceleration of Earth | g | 0.2 | 0.2 | 0.2 | 0.2 | |
14 | Bridge | m/building | 1 | 1 | 1 | 1 | |
15 | At-grade intersection | pcs | 3 | 2 | 6 | 5 | |
16 | Culvert and channel | pcs | 6 | 6 | 6 | 6 | |
17 | Demolished buildings | m2 | 36,270 | 28,400 | 44,451 | 49,127 | |
18 | Occupied land | mu | 94 | 86 | 89.4 | 91.3 | |
19 | Construction costs | RMB 10,000 | 6,544.8 | 5,365.81 | 6,975.1 | 7,013.9 |
According to the particularities of the project, the expert survey method was used, inviting 30 experts in all relevant fields (as shown in Table
For example, the judgment matrix of 5 indexes, including construction condition A and technical capability B, is established on Class-1 index layer, as shown in Table
Pair judgment matrix of the criterion layer.
A-B | A | B | C | D | E | Weight |
---|---|---|---|---|---|---|
Construction condition A | 1 | 1/2 | 1/3 | 3 | 1/3 | 0.1181 |
Technical capability B | 2 | 1 | 1/2 | 4 | 1/2 | 0.1916 |
Economic effect C | 3 | 2 | 1 | 4 | 2 | 0.3625 |
Social effect D | 1/3 | 1/4 | 1/4 | 1 | 1/3 | 0.0634 |
Environmental effect E | 3 | 2 | 1/2 | 3 | 1 | 0.2644 |
It is obtained from consistency inspection:
Similarly, specialists score Class-2 indexes and Class-3 indexes in Class-2 indexes group; the pair judgment matrix is established and MATLAB software is used to analyze and calculate to obtain the weight of all indexes in the evaluation index system, as shown in Table
Weight table of decision-making evaluation indexes of the investment plan of the project.
Object layer | Class-1 index layer | Class-2 index layer | Class-3 index layer |
---|---|---|---|
Technical advancement | Construction condition A, 0.1181 | Adaptability of natural conditions A1, 0.1227 | Geographical position A11, 0.1672 |
Geologic factor A12, 0.3508 | |||
Topography A13, 0.1058 | |||
Hydrological regime A14, 0.3045 | |||
Climate condition A15, 0.0717 | |||
Technical capability B, 0.1916 | Adaptability of social conditions A2, 0.0222 | Policies and regulationsA22, 0.2165 | |
Talent pool A23, 0.3835 | |||
Supporting strength A24, 0.1102 | |||
Tax preference A25, 0.0839 | |||
Market condition A26, 0.2059 | |||
Survey and design capability B1, 0.0776 | Construction scale B11, 0.3107 | ||
Direction of route B13, 0.1464 | |||
Critical control point B14, 0.1036 | |||
Estimated traffic B15, 0.4393 | |||
Construction technology capability B2, 0.0942 | Roadbed, pavement, and water-proofing drainage construction technology B21, 0.3462 | ||
Bridge and culvert construction technology B23, 0.3462 | |||
Safety of technical plan B25, 0.2094 | |||
Comprehensive management capability of filed construction B27, 0.0982 | |||
Economical rationality | Economic effect C, 0.3625 | Estimated total investment C1, 0.1167 | Estimated fixed assets C11, 0.3333 |
Estimated circulating funds C12, 0.6666 | |||
Financial evaluation conclusion C2, 0.0440 | FIRR C21, 0.2759 | ||
ROE C22,0.1360 | |||
Earning ratio of all investing parties C23, 0.1956 | |||
FNPVZ C24, 0.0727 | |||
Payback period C25, 0.0687 | |||
ROI C26, 0.1138 | |||
Repayment period of loan C27, 0.0687 | |||
DSCR C28, 0.0687 | |||
National economic evaluation conclusion C3, 0.1375 | EIRR C31, 0.5936 | ||
ENPV C32, 0.2493 | |||
Economic effect-expense ratio C33, 0.1571 | |||
Social effect D, 0.0634 | Social effect D1, 0.0927 | Rate of influence on local employment D11, 0.74998 | |
Rate of influence on local customs D12, 0.25002 | |||
Land use effect D2, 0.0541 | Resettlement rate D21, 0.25002 | ||
Land acquisition rate D22, 0.74998 | |||
Regional development effect D3, 0.0948 | Rate of improvement of people’s living standard D32, 0.3333 | ||
Rate of process of urbanization D33, 0.6666 | |||
Environmental effect E, 0.2644 | Environmental pollution treatment effect E1, 0.0811 | Water pollution treatment rate E11, 0.4827 | |
Atmospheric pollution treatment rate E12, 0.2756 | |||
Noise treatment rate E13, 0.1006 | |||
Solid waste treatment rate E14, 0.1412 | |||
Ecological recovery effect E2, 0.0335 | Soil erosion rate E21, 0.3462 | ||
Provisional land reclamation rate E22, 0.0802 | |||
Planting rate of greening area E23, 0.2517 | |||
Return rate of cultivated land E24, 0.2049 | |||
Land fertility recovery rate E25, 0.1169 | |||
Biological conservation effect E3, 0.0289 | Biological survival rate E31, 0.3108 | ||
VCR (vegetation coverage rate) E32, 0.4934 | |||
Species protection rate E33, 0.1958 |
According to the calculation rate in Table
30 experts were invited to score the system, of which the number of rows is the number of programs and the number of columns is the number of indicators. After passing the reliability test of the questionnaire, a grey correlation initial data matrix composed of 4 programs and 34 indicators was made. And the following initial standardized index matrix was made by the dimensionless method:
According to the initial standard index matrix, evaluation standard sequence is formed by the maximum values of all indexes of each plan, and the result is as below:
Determination of difference sequence: Determination of maximum difference sequence and minimum difference sequence:
where
where the maximum value
Calculate
On the basis of considering the situation of the project itself, 30 experts were invited to score the 34 selected comprehensive benefit evaluation indicators. After passing the reliability test of the questionnaire, the out-of-group weights for each indicator were calculated by the analytic hierarchy process (AHP) and entropy method of MATLAB, and the optimal weights were calculated by formula (
Summary table of the weights for all weighting methods.
A11 | A12 | A13 | A14 | A22 | A23 | |
AHP | 0.0274 | 0.0423 | 0.0214 | 0.0401 | 0.0147 | 0.032 |
Entropy weight method | 0.0481 | 0.0417 | 0.0165 | 0.0165 | 0.0118 | 0.0417 |
Combination weight process | 0.0484 | 0.0649 | 0.01299 | 0.0243 | 0.0064 | 0.049 |
A24 | A26 | B11 | B13 | B15 | B21 | |
AHP | 0.0094 | 0.0141 | 0.0264 | 0.0205 | 0.0262 | 0.0560 |
Entropy weight method | 0.0084 | 0.0171 | 0.0165 | 0.0165 | 0.0084 | 0.0699 |
Combination weight process | 0.0029 | 0.0089 | 0.0160 | 0.0124 | 0.0081 | 0.144 |
B23 | B25 | C12 | C21 | C22 | C23 | |
AHP | 0.0603 | 0.0365 | 0.0121 | 0.0152 | 0.0105 | 0.0113 |
Entropy weight method | 0.01598 | 0.0237 | 0.0282 | 0.029 | 0.0724 | 0.0453 |
Combination weight process | 0.0354 | 0.0318 | 0.0126 | 0.0162 | 0.0280 | 0.0188 |
C24 | C26 | C31 | C32 | D11 | D22 | |
AHP | 0.0094 | 0.0106 | 0.0128 | 0.0120 | 0.0133 | 0.0192 |
Entropy weight method | 0.0527 | 0.0593 | 0.0443 | 0.0455 | 0.0216 | 0.0247 |
Combination weight process | 0.0182 | 0.0231 | 0.0209 | 0.0201 | 0.0106 | 0.0174 |
D33 | E11 | E12 | E14 | E21 | E23 | |
AHP | 0.0188 | 0.0534 | 0.0497 | 0.04 | 0.044 | 0.0405 |
Entropy weight method | 0.0472 | 0.0284 | 0.0247 | 0.0284 | 0.0162 | 0.0075 |
Combination weight process | 0.0326 | 0.0558 | 0.0452 | 0.0418 | 0.0262 | 0.0112 |
E24 | E25 | E31 | E32 | |||
AHP | 0.0370 | 0.0361 | 0.0658 | 0.0611 | ||
Entropy weight method | 0.0237 | 0.0094 | 0.0232 | 0.0154 | ||
Combination weight process | 0.0323 | 0.0125 | 0.0562 | 0.0346 |
After the correlation coefficient is obtained, Grey Correlation Degree values corresponding to the weights for all weighting methods can be obtained according to the results calculated with formula (
Grey correlation values obtained with different weighting methods in the alternative plans for the project.
Calculation method | AHP grey correlation degree value | Entropy weight method grey correlation degree value | Combination weight process grey correlation degree value | Grey correlation degree sequencing result |
---|---|---|---|---|
Alternative plan | ||||
Plan A | 0.5515 | 0.5804 | 0.5482 | 4 |
Plan B | 0.8919 | 0.8769 | 0.8809 | 2 |
Plan C | 0.9230 | 0.8951 | 0.9053 | 1 |
Plan D | 0.6992 | 0.7115 | 0.7025 | 3 |
Calculation results obtained with 3 weight methods in Table
Weight distribution chart.
Weights calculated with the combination weight process, AHP, and entropy weight method are shown in Figure
Table of critical decision indexes determined with 3 weighting methods.
Weighting method | Measurement object | Critical decision index | |
---|---|---|---|
Class-2 indexes | Class-3 indexes | ||
Combination weight process | Technical advancement | Adaptability of natural conditions A1 | Geologic factor A12 |
Adaptability of social conditions A2 | Talent pool A23 | ||
Construction technology capability B2 | Technical capability of roadbed, pavement, and water-proofing drainage construction B21 | ||
Economical rationality | Environmental treatment effect E1 | Water pollution treatment rate E11 | |
Ecological protection effect E3 | Biological survival rate E31 | ||
AHP | Technical advancement | Construction technology capability B2 | Technical capability of bridge and culvert construction B23 |
Economical rationality | Environmental treatment effect E1 | Water pollution treatment rate E11 | |
Ecological protection effect E3 | Biological survival rate E31 | ||
Entropy weight method | Technical advancement | Construction technology capability B2 | Technical capability of roadbed, pavement, and water-proofing drainage construction B21 |
Economical rationality | Financial evaluation conclusion C2 | ROE C22, | |
FNPV C24 | |||
ROI C26 |
In sum up, in order to ensure plan investment effect, the critical decision indexes of the project should be Critical indexes that decide technical advancement: geologic factor A12, talent pool A23, technical capability of roadbed, pavement, and water-proofing drainage construction B21, and technical capability of bridge and culvert construction B23 Critical indexes that decide economical rationality: ROE C22, FNPV C24, ROI C26, water pollution treatment rate E11, and biological survival rate E31
Based on correlation values and sequencing of all plans obtained in Table
Grey correlation degree chart of alternative plans.
According to the sequencing and distribution of correlations in Figure Determination of main plan: from Table Determination of alternative plans: as the project is located in western geological environment, alternative plans should be determined as well, considering risks and uncertainties. Based on this, Plan B can be selected as the alternative plan to cope with the unforeseeable risks that might be met in the project construction process, as the correlation of Plan B is very close to that of Plan C (as shown in Figure Optimization plan: although the correlation coefficient of Plan A and Plan D is small and there is a big gap between them and the correlation of Plan C (as shown in Figure
In summary, Plan C is taken as the main plan and Plan B is considered to be used as the alternative plan, and relevant content in Plan A and Plan D will be used as optimization ideas for Plan C.
With constant implementation of China Western Development, high speed economic development, and constant improvement of traffic infrastructures, it is of great significance to explore scientific, reasonable, and effective investment decision-making methods for high-grade highway projects. In this paper, factors that influence the investment plans for high-grade highway investment plans in western region are determined with the method of specialist investigation and cost composition method, and critical decision factors are filtered with the Delphi method and entropy weight method, in order to solve decision-making problems about high-grade highway investment plans in the western region. Besides, the decision-making index system for high-grade highway investment plans in the western region is established according to the above. Based on the decision-making index system, AHP and Grey Correction Degree are combined to establish the correlation model of investment plan decision. Through instance analysis, the result of this model decision matches with the actually adopted optimal plan the best, showing this model has better adaptability and operability.
Leveraging the objective weighting characteristics of the entropy weight method and taking the opinions of different specialists into consideration, objective influences are reduced and the decision-making factors of investment plans are filtered in this study to establish a decision-making index system for high-grade highway investment plans applicable to the western region. Taking advantages of Grey Correction Degree, i.e., less information and simplified decision-making problem, the Grey Correction Degree is used together with AHP to gather objective and subjective weights again, decision results are optimized with grey correlation, and a new decision-making correlation model of investment plans that meet the characteristics of high-grade highways in the western region is brought up, providing theoretical basis for the decision-making of high-grade highway investment plans under the same environmental background.
The data used to support the findings of this study are included within the article.
The authors declare no conflicts of interest.
This research was funded by the Scientific Research Project of the State Administration of Work Safety (Sichuan-0005-2017AQ), Key Project of the Education Department of Sichuan Province (14ZA0048), and School-level Education Reform Project of Southwest Petroleum University (18YJYB25).