Research on Food Safety Control Based on Evolutionary Game Method from the Perspective of the Food Supply Chain
Abstract
:1. Introduction
- What’s the law of interaction between the strategies of the food raw material supplier, the food manufacturer, and consumers;
- What are the factors that affect the decision-making of each subject in the food supply chain;
- How to employ these influencing factors to guide supply chain member to make the right decisions that can help improve the food quality.
2. Research Methods
3. Assumptions and Model Construction
3.1. Conditional Assumptions of the Game Model
- The weight of the food raw materials provided by the food raw material supplier to the food manufacturer is Q. The unit cost of high-quality raw materials is Cg, and the unit cost of poor-quality raw materials is Cb, (Cg > Cb). Regardless of whether the food raw materials are of high quality, the food raw material supplier will sell them to the food manufacturer at the same unit price P1. Therefore, the profit obtained by the food raw material supplier is (P1 − Cg)Q when it provides high-quality raw materials, and the profit is (P1 − Cb)Q when it provides poor-quality raw materials;
- After processing food raw materials into food products, the food manufacturer will sell them to consumers at a unit price of P2, (P2 > P1 > Cg > Cb). The unit cost of the food manufacturer to produce and sell food products is expressed as Cp, which includes the cost of processing food raw materials, the cost of adding other auxiliary materials during the food production process, and the expenses incurred in food sales activities. Therefore, the profit earned by the food manufacturer from selling food is (P2 − P1 − Cp)Q;
- The food manufacturer should be responsible for the safety of food raw materials, and it needs to inspect the quality of food raw materials. If the food manufacturer detects that the food raw material doesn’t meet the safety standards, it will refuse to accept the food raw materials provided by the food raw material supplier so the food raw material supplier cannot obtain income. At the same time, there is a certain probability that the government will investigate and punish the food raw material supplier for providing poor-quality food raw materials, and the amount of the fine is represented as Fa;
- The food manufacturer may choose to inspect the quality of food raw materials to prevent the food raw material supplier from replacing high-quality food raw materials with poor-quality food raw materials, or it may choose not to inspect to save inspecting costs. If the food manufacturer chooses to inspect, the inspection cost is Cr. When the food raw material supplier provides poor-quality food raw materials, the probability of the food manufacturer detecting the food raw material has quality problems is α, and the probability of detection failure is (1 − α), i.e., the value of α can reflect the detection ability of the food manufacturer; the higher the value of α, the stronger the detection ability of the food manufacturer.Even if the food manufacturer doesn’t discover the food raw materials are of poor quality, the food produced by it may still cause food safety incidents. If the food raw materials are of poor quality, and the food manufacturer chooses the “inspect” strategy at the same time, the expected income of the food raw material supplier is expressed as (1 − α)P1Q; if the food manufacturer discovers the food raw materials are of poor quality, it will choose to return the goods and purchase high-quality food raw materials from other food raw material suppliers then. After repurchasing, the food manufacturer needs to spend Cr to detect the quality of the new food raw materials. If the food manufacturer directly processes the purchased food raw materials into food and sells it without detecting its quality, the safety of the food cannot be guaranteed, which is a violation of the regulations. There is a certain probability that the government will investigate and punish the food manufacturer for such transgressions, and the amount of the fine is expressed as Fb;
- Suppose the weight of the food purchased by the consumer from the food manufacturer is ∆Q, then the consumer will spend P2∆Q on buying the food. Consumers can purchase food offline or through online ordering. Consumers will complain if they feel unwell after eating food or find other abnormalities during the food eating process. However, sometimes even if there is no problem with the food raw materials and the detecting process, consumers may feel that the food doesn’t meet their expectations and therefore have a skeptical attitude towards food quality. In this case, they may also choose to complain. The probability that the government spontaneously takes regulatory actions against the food raw material supplier and the food manufacturer without customer complaint is β; when consumers complain to the government, the probability of the government taking regulatory actions is 1. The complaint cost of consumers is Ct. Since the food manufacturer is the subject of direct transactions with consumers, consumers often require the food manufacturer to compensate for their economic losses.If the food manufacturer is indeed negligent in the detecting process, the consumer’s complaint will be successful, and the food manufacturer will compensate the consumer for an amount of B. After the food manufacturer reimburses the consumer, it will seek compensation from the food raw material supplier. If the food raw materials are indeed inferior, the food manufacturer will receive compensation γB from the food raw material supplier, where γ is the compensation factor. If neither the food raw material supplier nor the food manufacturer violates the regulations, the consumer’s complaint will be invalid.
- The probability of the food raw material supplier providing high-quality food raw materials is x, and the probability of providing poor-quality food raw materials is 1 − x; the probability of the food manufacturer inspecting food raw materials is y, and the probability of not inspecting food raw materials is 1 − y; the probability of the customers not complaining is z, and the probability of complaining is 1 − z. x, y, and z are variables, whose values change with the evolution process. Their value range is [0, 1].
3.2. Model Construction
4. Evolutionary Game Analysis of the Food Raw Material Supplier, the Food Manufacturer, and Customers
4.1. The Replicator Dynamics Equation and Equilibrium Points of the Food Raw Material Supplier
4.2. The Replicator Dynamics Equation and Equilibrium Points of the Food Manufacturer
4.3. The Replicator Dynamics Equation and Equilibrium Points of Consumers
5. Evolutionary Stability Analysis of the Equilibrium Points
6. Model Application and Simulation Analysis
6.1. Case Background
6.2. Case Analysis and Parameter Assignment
6.3. Simulation Analysis
6.3.1. Influence Analysis of Factors Related to the Intensity of Government Supervision
- Numerical simulation analysis of the probability β of the government spontaneously taking supervisory actions
- 2.
- Numerical simulation analysis of the fine Fa imposed on the food raw material supplier
- 3.
- Numerical simulation analysis of the fine Fb imposed on the food manufacturer
6.3.2. Influence Analysis of Factors Related to the Interests of the Food Raw Material Supplier
- Numerical simulation analysis of the cost Cg of the food raw material supplier to provide high-quality food raw materials
6.3.3. Influence Analysis of Factors Related to the Interests of the Food Manufacturer
- Numerical simulation analysis of the inspection cost Cr of the food manufacturer
- 2.
- Numerical simulation analysis of the inspection ability α of the food manufacturer
- 3.
- Numerical simulation analysis of the compensation coefficient γ
6.3.4. Influence Analysis of Factors Related to the Interests of Consumers
- Numerical simulation analysis of the complaint cost Ct of consumers
- 2.
- Numerical simulation analysis of the compensation B received by consumers
7. Conclusions and Suggestions
7.1. Conclusions
- Increasing the supervision intensity of the government, increasing the amount of fines for illegal practices, narrowing the cost gap between high-quality food raw materials and poor-quality food raw materials, reducing the inspection cost, improving the inspection ability of food manufacturers, reducing the complaint cost of consumers and encouraging consumers to report the misconduct of enterprises, and optimizing the compensation mechanism can all contribute to the healthy and stable development of the food supply chain.
- The strategies of game participants influence and restrict each other. According to the results of the research, it can be concluded that the higher the probability that the food manufacturer chooses the “inspect” strategy, the food raw material supplier will be more inclined to choose the “provide high-quality food raw materials” strategy; the higher the willingness of consumers to complain, the stronger the restraint on the misconduct of the food raw material supplier and the food manufacturer. This indicates that the government has many ways to intervene in the operation of the food supply chain. It shouldn’t only directly intervene in food raw material suppliers by the amount of the fine and the frequency of supervision, but also indirectly intervene by guiding food manufacturers and consumers, thereby improving the safety of food quality.
- The strategy sets {1,1,1} and {1,0,1} (i.e., {provide high-quality food raw materials, inspect, not complain} and {provide high-quality food raw materials, not inspect, not complain}) are two kinds of optimal strategy combinations of three game participants, but the strategy set {1,1,1} is the better one. After optimizing the value of each influencing factor, only two results of the final evolutionary equilibrium point remained, i.e., (1,1,1) and (1,0,1). Through the results of the numerical simulation experiments, it can be concluded that the food raw material supplier will be more inclined to select the “provide high-quality food raw materials” strategy when the food manufacturer chooses the “inspect” strategy. If the food manufacturer is in a state of relaxation for a long time, it may spur the food raw material supplier to engage in misconduct again. Therefore, the strategy combination {1,1,1} of the three game participants is the ideal equilibrium we pursue.
7.2. Suggestions
- Node enterprises in the food supply chain should cooperate in good faith and enhance their sense of social responsibility. In the complex market environment, enterprises can be more competitive in the fierce market competition only when they jointly build a stable supply chain system. This requires enterprises to treat each other sincerely instead of deceiving each other for their own sake. Food raw material suppliers should provide high-quality raw materials, and food manufacturers should strictly control the food quality, so that the supply chain system can better meet the market demand and enterprises themselves can achieve long-term and stable profits.
- Food production enterprises should seek to improve their inspection capabilities, and they should strive to carry out technological transformations to achieve the goal of reducing costs. For the food manufacturer, if their inspection ability is not excellent enough, it is easy to make the food that doesn’t meet the safety standards flow into the market. The quality of food is directly related to people’s health, and it is extremely important to carry out strict inspections at all stages of food production. When hiring inspection personnel, enterprises should strictly select professionals and carefully evaluate their inspection capabilities. At the same time, they should repair and maintain inspection equipment in time and strive to develop more efficient inspection technologies to improve their inspection ability.
- Consumers should consciously fulfill their feedback obligations, enhance their awareness of rights protection, and become the right-hand men assisting the government in supervising the illegal behavior of enterprises. With limited resources of government supervision departments, there will inevitably be omissions in the supervision work. Consumers are the recipients of the products at the end of the supply chain and critics of product quality. If all consumers can actively report to the government when they buy inferior products, they will not only safeguard their rights and interests but also help the government supervision department to investigate and punish the illegal behaviors of enterprises in time. Therefore, we recommend consumers actively provide feedback product quality information to relevant departments and bravely defend their rights when food safety incidents happen.
- Government regulatory departments should strive to improve the regulatory mechanism, strengthen legal binding, carry out educational activities on food safety topics for enterprises and consumers, and build an official online platform for consumers to report anonymously. The government should increase the frequency of unannounced inspections on enterprises and increase punishment intensity for some illegal traders. In addition, the government can carry out food safety educational activities for enterprises and consumers through the government’s official website, Weibo, WeChat, and other online media platforms, which can not only encourage food enterprises to increase their awareness of producing high-quality food but also make consumers raise their awareness of rights protection. The government can also build an official online platform for consumers to report anonymously. Consumers can directly upload photos of evidence to the platform, which can significantly save time and transportation costs for complaints.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Devadoss, S.; Holland, D.W.; Stodick, L.; Ghosh, J. A general equilibrium analysis of foreign and domestic demand shocks arising from mad cow disease in the United States. J. Agric. Resour. Econ. 2006, 31, 441–453. [Google Scholar]
- Calvin, L. Outbreak linked to spinach forces reassessment of food safety practices. Amber Waves 2007, 5, 24–31. [Google Scholar]
- Yang, J.L.; Shan, H.Y. The willingness of submitting waste cooking oil (WCO) to biofuel companies in China: An evolutionary analysis in catering networks. J. Clean. Prod. 2021, 282, 125331. [Google Scholar] [CrossRef]
- Lachenmeier, D.W.; Humpfer, E.; Fang, F.; Schutz, B.; Dvortsak, P.; Sproll, C.; Spraul, M. NMR-spectroscopy for nontargeted screening and simultaneous quantification of health-relevant compounds in foods: The example of melamine. J. Agric. Food Chem. 2009, 57, 7194–7199. [Google Scholar] [CrossRef] [Green Version]
- Yang, R.J.; Huang, W.; Zhang, L.S.; Thomas, M.; Pei, X.F. Milk adulteration with melamine in China: Crisis and response. Qual. Assur. Saf. Crop. Foods 2009, 1, 111–116. [Google Scholar] [CrossRef]
- Ministry of Public Security. Uncovering the Truth of Waste Cooking Oil Returning Back to the Table. Available online: http://www.gov.cn/gzdt/2011-09/13/content_1946112.htm (accessed on 28 June 2022).
- Govindan, K.; Kadzinski, M.; Sivakumar, R. Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega-Int. J. Manage. Sci. 2017, 71, 129–145. [Google Scholar] [CrossRef]
- Song, C.; Zhuang, J. Modeling a government-manufacturer-farmer game for food supply chain risk management. Food Control 2017, 78, 443–455. [Google Scholar] [CrossRef] [Green Version]
- Granja, C.R.; Wollni, M. Opportunistic behaviour and trust: Experimental results from broccoli farmers in ecuador. J. Agric. Econ. 2019, 70, 62–80. [Google Scholar] [CrossRef] [Green Version]
- Yue, L.Q.; Liu, Y.M. Research into the quality of agricultural products in “Company + Farmer” mode based on signaling game model. Oper. Res. Manag. Sci. 2015, 24, 263–269. [Google Scholar]
- Jiang, Q.J. The analysis of the management of corporate social responsibility in food supply chain. J. Shanghai Ocean. Univ. 2012, 21, 1087–1092. [Google Scholar]
- Beske, P.; Land, A.; Seuring, S. Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. Int. J. Prod. Econ. 2014, 152, 131–143. [Google Scholar] [CrossRef]
- de Jonge, J.; van Trijp, H.; Renes, R.J.; Frewer, L. Understanding consumer confidence in the safety of food: Its two-dimensional structure and determinants. Risk Anal. 2007, 27, 729–740. [Google Scholar] [CrossRef] [PubMed]
- Grunert, K.G. Food quality and safety: Consumer perception and demand. Eur. Rev. Agric. Econ. 2005, 32, 369–391. [Google Scholar] [CrossRef]
- Mangla, S.K.; Luthra, S.; Rich, N.; Kumar, D.; Rana, N.P.; Dwivedi, Y.K. Enablers to implement sustainable initiatives in agri-food supply chains. Int. J. Prod. Econ. 2018, 203, 379–393. [Google Scholar] [CrossRef] [Green Version]
- Xing, X.H.; Hu, Z.H.; Wang, S.W.; Luo, W.P. An evolutionary game model to study manufacturers and logistics companies’ behavior strategies for information transparency in cold chains. Math. Probl. Eng. 2020, 2020, 7989386. [Google Scholar] [CrossRef] [Green Version]
- Abd, M.A.; Al Rubeaai, S.F.; Salimpour, S.; Azab, A. Evolutionary game theoretical approach for equilibrium of cross-border traffic. Transp. B-Transp. Dyn. 2019, 7, 1611–1626. [Google Scholar] [CrossRef]
- Boctor, L. Active-learning strategies: The use of a game to reinforce learning in nursing education. A case study. Nurse Educ. Pract. 2013, 13, 96–100. [Google Scholar] [CrossRef]
- Jiang, K.; Merrill, R.; You, D.; Pan, P.; Li, Z. Optimal control for transboundary pollution under ecological compensation: A stochastic differential game approach. J. Clean Prod. 2019, 241, 118391. [Google Scholar] [CrossRef]
- Reluga, T.C.; Smith, R.A.; Hughes, D.P. Dynamic and game theory of infectious disease stigmas. J. Theor. Biol. 2019, 476, 95–107. [Google Scholar] [CrossRef]
- Shi, Q.; Zhu, J.; Li, Q. Cooperative evolutionary game and applications in construction supplier tendency. Complexity 2018, 2018, 8401813. [Google Scholar] [CrossRef]
- Li, K.Q.; Chen, Y.; Liu, J.C.; Zhang, L.; Mu, X.W. Online food delivery platforms and restaurants’ interactions in the context of the ban on using single-use plastics. IEEE Access 2021, 9, 96210–96220. [Google Scholar] [CrossRef]
- Zhu, Y.; Chu, M.; Wen, X.W.; Wang, Y.Q. Food safety risk communication between the food regulator and consumer in China: An evolutionary game perspective. Complexity 2021, 2021, 9933796. [Google Scholar] [CrossRef]
- Liu, P.; Wang, S. Evolutionary game analysis of cold chain logistics outsourcing of fresh food enterprises with operating risks. IEEE Access 2020, 8, 127094–127103. [Google Scholar] [CrossRef]
- Yang, S.; Zhuang, J.C.; Wang, A.F.; Zhang, Y.C. Evolutionary game analysis of Chinese food quality considering effort levels. Complexity 2020, 2020, 4549629. [Google Scholar] [CrossRef]
- Chen, T.Q.; Zhang, J.; Luo, J. Differential game evolution of food quality safety based on market supply and demand. Food Sci. Nutr. 2021, 9, 2414–2435. [Google Scholar] [CrossRef]
- Wang, J.; Yao, S.; Lu, X.M.; Li, Y. Organic food labeling and advertising: A tripartite game model between one supplier and two heterogeneous manufacturers. Complexity 2019, 2019, 3143416. [Google Scholar] [CrossRef]
- Wang, J.L.; Peng, X.; Du, Y.N.; Wang, F.L. A tripartite evolutionary game research on information sharing of the subjects of agricultural product supply chain with a farmer cooperative as the core enterprise. Manag. Decis. Econ. 2021, 43, 159–177. [Google Scholar] [CrossRef]
- Tang, L.N.; Yang, T.O.; Tu, Y.L.; Ma, Y.Z. Supply chain information sharing under consideration of bullwhip effect and system robustness. Flex. Serv. Manuf. J. 2021, 33, 337–380. [Google Scholar] [CrossRef]
- Friedman, D. Evolutionary game in economics. Econometrica 1991, 59, 637–666. [Google Scholar] [CrossRef] [Green Version]
- Liu, L.X.; Zhu, Y.C.; Guo, S.B. The evolutionary game analysis of multiple stakeholders in the low-carbon agricultural innovation diffusion. Complexity 2020, 2020, 6309545. [Google Scholar] [CrossRef]
- Lu, Z.; Huang, P.; Liu, Z. Predictive approach for sensorless bimanual teleoperation under random time delays with adaptive fuzzy control. IEEE Trans. Ind. Electron. 2018, 65, 2439–2448. [Google Scholar] [CrossRef]
- Zhao, D.; Hao, J.; Cao, C.; Han, H. Evolutionary game analysis of three-player for low-carbon production capacity sharing. Sustainability 2019, 11, 2996. [Google Scholar] [CrossRef] [Green Version]
- Zhao, D.; Hao, J.; Yang, J.; Han, H. Evolutionary game analysis of three parties in sharing economy considering network externality of platform. J. Control Decis. 2020, 35, 1741–1750. [Google Scholar]
- Leeks Worth 2CNY Need to Cost 5000CNY for Testing. Available online: https://www.antpedia.com/news/41/n-221641.html (accessed on 28 June 2022).
- CCTV 315 Program Exposed: Guangqi Trading Company Sells Expired Food Raw Materials in Hangzhou. Available online: https://www.tech-food.com/news/detail/n1081914.htm (accessed on 28 June 2022).
- Ministry of Commerce of the People’s Republic of China. Available online: https://cif.mofcom.gov.cn/cif/html/dataCenter/index.html?jgnfcprd (accessed on 28 June 2022).
- Analysis of Market Supply and Demand and Price Trend of China’s Flour Industry in 2020. Available online: https://www.chyxx.com/industry/202103/941279.html (accessed on 28 June 2022).
Number | Authors | Game Participants | Is There a Case Analysis |
---|---|---|---|
1 | Li et al. [22] | online food delivery platforms and restaurants | No |
2 | Zhu et al. [23] | the government and consumers | No |
3 | Yang and Shan [3] | the government, restaurants, and consumers | Yes |
4 | Xing et al. [16] | manufacturers and logistics service providers | No |
5 | Liu and Wang [24] | the fresh food enterprise and the third-party logistics enterprise | Yes |
6 | Yang et al. [25] | food suppliers and food producers | No |
7 | Chen et al. [26] | food suppliers and food retailers | No |
Food Raw Material Supplier | Customers | Food Manufacturer | |
---|---|---|---|
Inspect (y) | Not inspect (1 − y) | ||
Provide high-quality food raw materials (x) | Not complain (z) | P1Q − CgQ | P1Q − CgQ |
(P2 − P1 − Cp)Q − Cr | (P2 − P1 − Cp)Q − βFb | ||
Q | Q | ||
Complain (1 − z) | P1Q − CgQ | P1Q − CgQ | |
(P2 − P1 − Cp)Q − Cr | (P2 − P1 − Cp)Q − Fb − B | ||
Q − Ct | Q − Ct + B | ||
Provide poor-quality food raw materials (1 − x) | Not complain (z) | (1 − α)P1Q − CbQ − βFa | P1Q − CbQ − βFa |
(P2 − P1 − Cp)Q − (1 + α)Cr − (1 − α)βFb | (P2 − P1 − Cp)Q − βFb | ||
Q | Q | ||
Complain (1 − z) | (1 − α)P1Q − CbQ − Fa − γB(1 − α) | P1Q − CbQ − Fa − γB | |
(P2 − P1 − Cp)Q − (1 + α)Cr − (1 − α)(Fb + B − γB) | (P2 − P1 − Cp)Q − Fb − B + γB | ||
Q − Ct + (1 − α)B | Q − Ct + B |
Equilibrium Point | Eigenvalue 1 | Eigenvalue 2 | Eigenvalue 3 |
---|---|---|---|
E1(0,0,0) | −(Cg − Cb)Q + Fa + γB | −(1 + α)Cr + αFb + α(1 − γ)B | Ct − B |
E2(0,0,1) | −(Cg − Cb)Q + βFa | − (1 + α)Cr + αβFb | B − Ct |
E3(0,1,0) | αP1Q − (Cg − Cb)Q + Fa + (1 − α)γB | (1 + α)Cr − αFb − α(1 − γ)B | Ct − B + αB |
E4(1,0,0) | (Cg − Cb)Q − Fa − γB | −Cr + Fb + B | Ct − B |
E5(0,1,1) | αP1Q − (Cg − Cb)Q + βFa | (1 + α)Cr − αβFb | (1 − α)B − Ct |
E6(1,0,1) | (Cg − Cb)Q − βFa | − Cr + βFb | B − Ct |
E7(1,1,0) | −αP1Q + (Cg − Cb)Q − Fa − (1 − α)γB | Cr − Fb − B | Ct |
E8(1,1,1) | −αP1Q + (Cg − Cb)Q − βFa | Cr − βFb | −Ct |
Equilibrium Point | Eigenvalue Symbol | Stability |
---|---|---|
E1(0,0,0) | Ct − B > 0, so this equilibrium point is unstable | Unstable |
E2(0,0,1) | When −(Cg − Cb)Q + βFa < 0 and − (1 + α)Cr + αβFb < 0, all eigenvalues are negative | ESS |
E3(0,1,0) | Ct − B + αB > 0, so this equilibrium point is unstable | Unstable |
E4(1,0,0) | Ct − B > 0, so this equilibrium point is unstable | Unstable |
E5(0,1,1) | When αP1Q − (Cg − Cb)Q + βFa < 0 and (1 + α)Cr − αβFb < 0, all eigenvalues are negative | ESS |
E6(1,0,1) | When (Cg − Cb)Q − βFa < 0 and − Cr + βFb < 0, all eigenvalues are negative | ESS |
E7(1,1,0) | Ct > 0, so this equilibrium point is unstable | Unstable |
E8(1,1,1) | When −αP1Q + (Cg − Cb)Q − βFa < 0 and Cr − βFb < 0, all eigenvalues are negative | ESS |
Parameters | Value | Unit | Parameters | Value | Unit |
---|---|---|---|---|---|
P1 | 5.6 | CNY/kg | Fa | 2000 | CNY |
P2 | 20 | CNY/kg | Fb | 5000 | CNY |
Cg | 3.5 | CNY/kg | Q | 500 | kg |
Cb | 1.5 | CNY/kg | ΔQ | 1 | kg |
Cp | 4.4 | CNY/kg | α | 0.5 | / |
Cr | 1000 | CNY | β | 0.2 | / |
Ct | 2000 | CNY | γ | 1 | / |
B | 200 | CNY |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Peng, X.; Wang, F.; Wang, J.; Qian, C. Research on Food Safety Control Based on Evolutionary Game Method from the Perspective of the Food Supply Chain. Sustainability 2022, 14, 8122. https://doi.org/10.3390/su14138122
Peng X, Wang F, Wang J, Qian C. Research on Food Safety Control Based on Evolutionary Game Method from the Perspective of the Food Supply Chain. Sustainability. 2022; 14(13):8122. https://doi.org/10.3390/su14138122
Chicago/Turabian StylePeng, Xue, Fulin Wang, Jiquan Wang, and Chang Qian. 2022. "Research on Food Safety Control Based on Evolutionary Game Method from the Perspective of the Food Supply Chain" Sustainability 14, no. 13: 8122. https://doi.org/10.3390/su14138122