New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment
Abstract
:1. Introduction
2. Indicator Systems and Methods of GSC Performance Evaluation
3. Nature-Inspired Algorithms and Their Applications to GSC Performance Evaluation
3.1. Nature-Inspired Algorithms
3.2. Applications of Nature-Inspired Algorithms to GSC Performance Evaluation
4. Improved 5DBSC
5. Related Algorithms of the RS-GA-LMBP Neural Network Algorithm
5.1. RS Theory
5.2. GA
5.3. LMBP Neural Network Algorithm
6. Model of the RS-GA-LMBP Neural Network Algorithm
7. Case Study
7.1. Data Preparation
7.2. Selection Indicators
7.3. Chromosomal Gene Coding
7.4. Fitness Formula
7.5. Acquisition of the Hidden Layer Nodes
7.6. Application Process of the GA-LMBP Neural Network Algorithm
7.7. Results Analysis and Discussion
8. Conclusions
- (1)
- An improved 5DBSC for GSC performance evaluation was proposed. Based on the applications and shortcomings of the 5DBSC and the requirements of the IOS 14000 Environment Management Standard, the 5DBSC for GSC performance evaluation was improved. Its main contribution is that the measurement method of the “environmental pollution” indicator contained in the green performance was improved. The “environmental pollution” indicator was revised to measure the total emissions of “three wastes”. The modified indicator was more easily measured and more practical.
- (2)
- A new algorithm was proposed for GSC performance evaluation. According to the applications and shortcomings of RS theory, GA, the LMBP neural network algorithm, and the GA-LMBP neural network algorithm for evaluating supply chain performance, a RS-GA-LMBP neural network algorithm was proposed and developed. The algorithm of the proposed model was presented. Then, from the aspect of a theoretical analysis and a literature review, it was demonstrated that the proposed algorithm could be used in an uncertain environment and could help eliminate redundant indicators. This algorithm has a high convergence speed and a more accurate prediction ability.
- (3)
- From a practical perspective, the effectiveness of the proposed algorithm was confirmed. A case study was conducted, and the practical values of 18 indicators from 2014 to 2015 were collected from automotive company F. According to the analysis, the results show that the proposed model is effective, valid, and reliable. This model has a faster convergence speed than BP and LMBP neural network algorithms.
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
2014 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
C1 | 0.75 | 1 | 0.75 | 1 | 0.75 | 1 | 1 | 0.75 | 0.75 | 1 | 0.75 | 0.75 |
C2 | 0.82 | 0.7 | 0.81 | 0.71 | 0.6 | 0.8 | 0.76 | 0.83 | 0.81 | 0.65 | 0.76 | 0.72 |
F2 | 0.24 | 0.26 | 0.34 | 0.26 | 0.22 | 0.29 | 0.29 | 0.29 | 0.33 | 0.23 | 0.27 | 0.29 |
F3 | 0.8 | 0.8 | 0.6 | 0.6 | 0.8 | 0.6 | 0.8 | 0.6 | 0.6 | 0.6 | 0.4 | 0.6 |
P1 | 0.6 | 0.1 | 0.7 | 0.5 | 0.2 | 0.5 | 0.2 | 0.3 | 0 | 0.6 | 0.7 | 0.3 |
P2 | 0.25 | 0.27 | 0.31 | 0.25 | 0.14 | 0.32 | 0.23 | 0.31 | 0.27 | 0.13 | 0.32 | 0.23 |
P3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
P4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
D1 | 0.15 | 0.16 | 0.67 | 0.12 | −0.32 | 0.49 | 0.08 | 0.5 | 0.09 | −0.16 | 0.5 | 0.07 |
D2 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 1 | 0.75 | 1 | 1 | 0.5 | 1 | 0.75 |
D3 | 0.75 | 0.91 | 0.67 | 0.75 | 0.36 | 0.75 | 0.75 | 0.75 | 0.91 | 0.25 | 0.67 | 0.75 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 0.75 | 0.75 | 0.5 | 1 | 1 | 1 | 0.75 | 1 | 1 | 0.75 | 0.75 | 0.75 |
E1 | 0.7 | 0.76 | 0.84 | 0.85 | 0.6 | 0.9 | 0.86 | 0.91 | 0.9 | 0.54 | 0.9 | 0.81 |
E2 | 0.33 | 1 | 0.33 | 0.33 | 1 | 0 | 0.33 | 0 | 0 | 1 | 0 | 0.33 |
E3 | 0.79 | 0.79 | 0.93 | 0.77 | 0.81 | 0.84 | 0.91 | 0.87 | 0.8 | 0.43 | 0.85 | 0.84 |
E4 | 0.63 | 0.65 | 0.81 | 0.87 | 0.45 | 0.85 | 0.72 | 0.88 | 0.82 | 0.72 | 0.88 | 0.75 |
Performance | 0.5 | 0.5 | 0.75 | 0.75 | 0.25 | 1 | 0.75 | 1 | 1 | 0.25 | 1 | 0.75 |
2015 | ||||||||||||
C1 | 0.75 | 1 | 1 | 0.75 | 1 | 1 | 0.75 | 1 | 1 | 1 | 1 | 0.75 |
C2 | 0.8 | 0.6 | 0.77 | 0.59 | 0.65 | 0.82 | 0.7 | 0.7 | 0.82 | 0.54 | 0.81 | 0.72 |
F2 | 0.33 | 0.22 | 0.26 | 0.22 | 0.25 | 0.31 | 0.25 | 0.28 | 0.32 | 0.23 | 0.29 | 0.3 |
F3 | 0.4 | 0.6 | 0.6 | 0.6 | 0.6 | 0.6 | 0.8 | 0.8 | 0.6 | 0.4 | 0.6 | 0.8 |
P1 | 0.7 | 0.3 | 0.5 | 0.3 | 0.5 | 0.6 | 0.5 | 0.5 | 0.5 | 0.4 | 0.7 | 0.5 |
P2 | 0.3 | 0.15 | 0.25 | 0.1 | 0.15 | 0.3 | 0.25 | 0.25 | 0.25 | 0.1 | 0.3 | 0.2 |
P3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
P4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
D1 | 0.66 | −0.32 | 0 | −0.28 | −0.16 | −0.08 | 0.16 | 0.14 | 0.1 | −0.17 | 0.49 | 0.09 |
D2 | 0.75 | 1 | 0.75 | 1 | 1 | 1 | 0.75 | 0.75 | 1 | 0.75 | 1 | 1 |
D3 | 0.75 | 0.25 | 0.08 | 0.08 | 0.08 | 0.58 | 0.75 | 0.67 | 0.75 | 0.08 | 0.75 | 0.67 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 0.75 | 1 | 0.5 | 1 | 1 | 0.75 | 0.75 | 1 | 1 | 0.75 | 1 | 1 |
E1 | 0.85 | 0.53 | 0.72 | 0.53 | 0.7 | 0.84 | 0.74 | 0.86 | 0.93 | 0.57 | 0.93 | 0.83 |
E2 | 0.33 | 1 | 0.33 | 1 | 0.67 | 0.33 | 0.67 | 0.33 | 0 | 1 | 0 | 0.33 |
E3 | 0.95 | 0.83 | 0.52 | 0.63 | 0.67 | 0.34 | 0.84 | 0.76 | 0.83 | 0.47 | 0.83 | 0.93 |
E4 | 0.85 | 0.43 | 0.62 | 0.83 | 0.77 | 0.74 | 0.64 | 0.86 | 0.89 | 0.77 | 0.86 | 0.73 |
Performance | 0.75 | 0.25 | 0.5 | 0.25 | 0.5 | 0.75 | 0.5 | 0.75 | 1 | 0.25 | 1 | 0.75 |
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Evaluation Dimensions | KPIs | Index Description |
---|---|---|
Accounting | Profitability (F1) | Profit level of a SC |
Capital turnover rate (F2) | Management efficiency of the net capital of a SC | |
Cash turnover time (F3) | Cash flow payback period | |
Customer | Customer satisfaction (C1) | Customers’ awareness and acceptability |
Market share (C2) | Size of the customer community | |
Business processes | SCRT (Supply chain response time) (P1) | Required time from all enterprises on the chain finding the changes of the market requirements to absorbing these changes and adjusting their plans to meet these changes |
Stock turnover rate (P2) | Amount of cash in the stock account | |
Waste rate (P3) | The quality control and production technology | |
Capacity utilization (P4) | Facility application level | |
Innovation and development | Profit increment rate (D1) | Development capability of an enterprise |
Information sharing (D2) | Level of the information integration Dependent on the partners strategic relationships | |
Period of a new product R&D (D3) | How fast a chain responds to market changes Different for each product and enterprise, so it is difficult to determine its value | |
Suppliers | On-time delivery rate (S1) | Delivery capability of a supplier |
Flexibility (S2) | SC’s capability of dealing with the special business environment and meeting the customers’ special requirements or unexpected requirements | |
Green Performance | Energy usage rate (E1) | Rate of energy consumption |
Environment pollution (E2) | Total emissions of “three wastes” | |
Green procurement rate (E3) | The proportion of Green Materials contained in raw materials | |
Green recovery rate (E4) | The proportion of the Recyclable wastes Value accounting for the product total value | |
Evaluation dimensions | Measurement method | Property |
Accounting | Net profit/total income (%) | Quantitative |
Total sales/total value of net assets | Quantitative | |
Inventory days of supply + Receivables age − Payables age | Quantitative | |
Customer | Fuzzy evaluation | Quantitative |
Product sales/Total sales of industry | Quantitative | |
Business processes | The time required to meet the sudden demand | Quantitative |
Cost of sales/The average occupancy amount of inventory | Quantitative | |
Number of the defective products/Total production | Quantitative | |
Fuzzy evaluation | Quantitative | |
Innovation and development | This period/profit of Previous period | Quantitative |
Fuzzy evaluation | Quantitative | |
Statistical mean | Quantitative | |
Suppliers | Punctual delivery times/Total delivery times | Quantitative |
Fuzzy evaluation | Quantitative | |
Green Performance | The energy amount contained in the product/Total energy consumption | Quantitative |
Liquid wastes emissions + Gas wastes emissions + Solid wastes emissions | Quantitative | |
Number of green materials/total raw material | Quantitative | |
Recyclable wastes Value/product production gross | Quantitative |
2014 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
C1 | 3 | 4 | 3 | 4 | 3 | 4 | 4 | 3 | 3 | 4 | 3 | 3 |
C2 | 1.40% | 1.39% | 1.61% | 1.41% | 1.19% | 1.59% | 1.52% | 1.65% | 1.62% | 1.30% | 1.52% | 1.43% |
F1 | 0.425 | 0.437 | 0.53 | 0.461 | 0.42 | 0.51 | 0.457 | 0.53 | 0.51 | 0.41 | 0.54 | 0.47 |
F2 | 0.239 | 0.263 | 0.34 | 0.264 | 0.22 | 0.29 | 0.285 | 0.291 | 0.334 | 0.231 | 0.272 | 0.287 |
F3 | 110 | 110 | 120 | 120 | 110 | 120 | 110 | 120 | 130 | 120 | 130 | 120 |
P1 | 89 | 94 | 88 | 90 | 93 | 90 | 94 | 92 | 95 | 89 | 88 | 92 |
P2 | 0.25 | 0.27 | 0.31 | 0.25 | 0.14 | 0.32 | 0.23 | 0.31 | 0.27 | 0.13 | 0.32 | 0.23 |
P3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
D1 | 0.147 | 0.161 | 0.67 | 0.124 | −0.315 | 0.49 | 0.079 | 0.5 | 0.091 | −0.161 | 0.5 | 0.07 |
D2 | 3 | 3 | 3 | 3 | 3 | 4 | 3 | 4 | 4 | 2 | 4 | 3 |
D3 | 120 | 110 | 130 | 120 | 170 | 120 | 120 | 120 | 110 | 180 | 130 | 120 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 3 | 3 | 2 | 4 | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 3 |
E1 | 0.7 | 0.76 | 0.84 | 0.85 | 0.6 | 0.9 | 0.86 | 0.91 | 0.9 | 0.54 | 0.9 | 0.81 |
E2 | 3 | 1 | 3 | 3 | 1 | 4 | 3 | 4 | 4 | 1 | 4 | 3 |
E3 | 0.79 | 0.79 | 0.93 | 0.77 | 0.81 | 0.84 | 0.91 | 0.87 | 0.8 | 0.43 | 0.85 | 0.84 |
E4 | 0.63 | 0.65 | 0.81 | 0.87 | 0.45 | 0.85 | 0.72 | 0.88 | 0.82 | 0.72 | 0.88 | 0.75 |
Performance | M | M | G | G | P | E | G | E | E | P | E | G |
2015 | ||||||||||||
C1 | 3 | 4 | 4 | 3 | 4 | 4 | 3 | 4 | 4 | 4 | 4 | 3 |
C2 | 1.59% | 1.20% | 1.54% | 1.18% | 1.29% | 1.64% | 1.40% | 1.39% | 1.64% | 1.29% | 1.62% | 1.44% |
F1 | 0.52 | 0.419 | 0.444 | 0.414 | 0.561 | 0.507 | 0.426 | 0.459 | 0.509 | 0.426 | 0.5 | 0.468 |
F2 | 0.328 | 0.215 | 0.263 | 0.216 | 0.248 | 0.31 | 0.247 | 0.275 | 0.322 | 0.225 | 0.289 | 0.295 |
F3 | 130 | 120 | 120 | 120 | 120 | 120 | 110 | 110 | 120 | 130 | 120 | 110 |
P1 | 88 | 92 | 90 | 92 | 90 | 89 | 90 | 90 | 90 | 91 | 88 | 90 |
P2 | 0.3 | 0.15 | 0.25 | 0.1 | 0.15 | 0.3 | 0.25 | 0.25 | 0.25 | 0.1 | 0.3 | 0.2 |
P3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
P4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
D1 | 0.66 | −0.324 | 0 | −0.281 | −0.16 | −0.069 | 0.157 | 0.136 | 0.095 | −0.171 | 0.489 | 0.088 |
D2 | 3 | 4 | 3 | 4 | 4 | 4 | 3 | 3 | 4 | 3 | 4 | 4 |
D3 | 120 | 180 | 200 | 200 | 200 | 140 | 120 | 130 | 120 | 200 | 120 | 130 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 3 | 4 | 2 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 4 | 4 |
E1 | 0.85 | 0.53 | 0.72 | 0.53 | 0.7 | 0.84 | 0.74 | 0.86 | 0.93 | 0.57 | 0.93 | 0.83 |
E2 | 3 | 1 | 3 | 1 | 2 | 3 | 2 | 3 | 4 | 1 | 4 | 3 |
E3 | 0.95 | 0.83 | 0.52 | 0.63 | 0.67 | 0.34 | 0.84 | 0.76 | 0.83 | 0.47 | 0.83 | 0.93 |
E4 | 0.85 | 0.43 | 0.62 | 0.83 | 0.77 | 0.74 | 0.64 | 0.86 | 0.89 | 0.77 | 0.86 | 0.73 |
Performance | G | P | M | P | M | G | M | G | E | P | E | G |
C2 | F1 | F2 | F3 | P1 | P2 | P3 | D1 | D3 | S1 |
---|---|---|---|---|---|---|---|---|---|
(0, 2%) | (0, 1) | (0, 1) | (100, 150) | (85, 95) | (0, 1) | (0, 1) | (0, 1) | (100, 210) | (0, 1) |
2014 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
C1 | 0.75 | 1 | 0.75 | 1 | 0.75 | 1 | 1 | 0.75 | 0.75 | 1 | 0.75 | 0.75 |
C2 | 0.82 | 0.7 | 0.81 | 0.71 | 0.6 | 0.8 | 0.76 | 0.83 | 0.81 | 0.65 | 0.76 | 0.72 |
F1 | 0.43 | 0.44 | 0.53 | 0.46 | 0.42 | 0.51 | 0.46 | 0.53 | 0.51 | 0.41 | 0.54 | 0.47 |
F2 | 0.24 | 0.26 | 0.34 | 0.26 | 0.22 | 0.29 | 0.29 | 0.29 | 0.33 | 0.23 | 0.27 | 0.29 |
F3 | 0.8 | 0.8 | 0.6 | 0.6 | 0.8 | 0.6 | 0.8 | 0.6 | 0.6 | 0.6 | 0.4 | 0.6 |
P1 | 0.6 | 0.1 | 0.7 | 0.5 | 0.2 | 0.5 | 0.2 | 0.3 | 0 | 0.6 | 0.7 | 0.3 |
P2 | 0.25 | 0.27 | 0.31 | 0.25 | 0.14 | 0.32 | 0.23 | 0.31 | 0.27 | 0.13 | 0.32 | 0.23 |
P3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
P4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
D1 | 0.15 | 0.16 | 0.67 | 0.12 | −0.32 | 0.49 | 0.08 | 0.5 | 0.09 | −0.16 | 0.5 | 0.07 |
D2 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 1 | 0.75 | 1 | 1 | 0.5 | 1 | 0.75 |
D3 | 0.75 | 0.91 | 0.67 | 0.75 | 0.36 | 0.75 | 0.75 | 0.75 | 0.91 | 0.25 | 0.67 | 0.75 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 0.75 | 0.75 | 0.5 | 1 | 1 | 1 | 0.75 | 1 | 1 | 0.75 | 0.75 | 0.75 |
E1 | 0.7 | 0.76 | 0.84 | 0.85 | 0.6 | 0.9 | 0.86 | 0.91 | 0.9 | 0.54 | 0.9 | 0.81 |
E2 | 0.33 | 1 | 0.33 | 0.33 | 1 | 0 | 0.33 | 0 | 0 | 1 | 0 | 0.33 |
E3 | 0.79 | 0.79 | 0.93 | 0.77 | 0.81 | 0.84 | 0.91 | 0.87 | 0.8 | 0.43 | 0.85 | 0.84 |
E4 | 0.63 | 0.65 | 0.81 | 0.87 | 0.45 | 0.85 | 0.72 | 0.88 | 0.82 | 0.72 | 0.88 | 0.75 |
Performance | 0.5 | 0.5 | 0.75 | 0.75 | 0.25 | 1 | 0.75 | 1 | 1 | 0.25 | 1 | 0.75 |
2015 | ||||||||||||
C1 | 0.75 | 1 | 1 | 0.75 | 1 | 1 | 0.75 | 1 | 1 | 1 | 1 | 0.75 |
C2 | 0.8 | 0.6 | 0.77 | 0.59 | 0.65 | 0.82 | 0.7 | 0.7 | 0.82 | 0.54 | 0.81 | 0.72 |
F1 | 0.52 | 0.42 | 0.42 | 0.42 | 0.56 | 0.51 | 0.43 | 0.46 | 0.51 | 0.43 | 0.5 | 0.47 |
F2 | 0.33 | 0.22 | 0.26 | 0.22 | 0.25 | 0.31 | 0.25 | 0.28 | 0.32 | 0.23 | 0.29 | 0.3 |
F3 | 0.4 | 0.6 | 0.6 | 0.6 | 0.6 | 0.6 | 0.8 | 0.8 | 0.6 | 0.4 | 0.6 | 0.8 |
P1 | 0.7 | 0.3 | 0.5 | 0.3 | 0.5 | 0.6 | 0.5 | 0.5 | 0.5 | 0.4 | 0.7 | 0.5 |
P2 | 0.3 | 0.15 | 0.25 | 0.1 | 0.15 | 0.3 | 0.25 | 0.25 | 0.25 | 0.1 | 0.3 | 0.2 |
P3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
P4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
D1 | 0.66 | −0.32 | 0 | −0.28 | −0.16 | −0.08 | 0.16 | 0.14 | 0.1 | −0.17 | 0.49 | 0.09 |
D2 | 0.75 | 1 | 0.75 | 1 | 1 | 1 | 0.75 | 0.75 | 1 | 0.75 | 1 | 1 |
D3 | 0.75 | 0.25 | 0.08 | 0.08 | 0.08 | 0.58 | 0.75 | 0.67 | 0.75 | 0.08 | 0.75 | 0.67 |
S1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
S2 | 0.75 | 1 | 0.5 | 1 | 1 | 0.75 | 0.75 | 1 | 1 | 0.75 | 1 | 1 |
E1 | 0.85 | 0.53 | 0.72 | 0.53 | 0.7 | 0.84 | 0.74 | 0.86 | 0.93 | 0.57 | 0.93 | 0.83 |
E2 | 0.33 | 1 | 0.33 | 1 | 0.67 | 0.33 | 0.67 | 0.33 | 0 | 1 | 0 | 0.33 |
E3 | 0.95 | 0.83 | 0.52 | 0.63 | 0.67 | 0.34 | 0.84 | 0.76 | 0.83 | 0.47 | 0.83 | 0.93 |
E4 | 0.85 | 0.43 | 0.62 | 0.83 | 0.77 | 0.74 | 0.64 | 0.86 | 0.89 | 0.77 | 0.86 | 0.73 |
Performance | 0.75 | 0.25 | 0.5 | 0.25 | 0.5 | 0.75 | 0.5 | 0.75 | 1 | 0.25 | 1 | 0.75 |
Methods | BP Neural Network Algorithm | LMBP Neural Network Algorithm | RS-GA-LMBP Neural Network Algorithm |
---|---|---|---|
Standard MSE | 0.01 | 0.01 | 0.01 |
Iteration number | 20 | 9 | 4 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Liu, P.; Yi, S. New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment. Sustainability 2016, 8, 960. https://doi.org/10.3390/su8100960
Liu P, Yi S. New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment. Sustainability. 2016; 8(10):960. https://doi.org/10.3390/su8100960
Chicago/Turabian StyleLiu, Pan, and Shuping Yi. 2016. "New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment" Sustainability 8, no. 10: 960. https://doi.org/10.3390/su8100960