Food products pricing theory with application of machine learning and game theory approach

Mamoudan, Mobina Mousapour; Mohammadnazari, Zahra; Ostadi, Ali; Esfahbodi, Ali

doi:10.1080/00207543.2022.2128921

Cited by 17 publications

(7 citation statements)

References 66 publications

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“…Additionally, machine learning and fuzzy logic have been employed for quality monitoring and traceability in the food supply chain. (Min et al, 2023) (Mamoudan et al, 2022).Decision Support Systems: The development of decision support systems powered by machine learning techniques, including model-based reinforcement learning, has shown promising results in optimizing inventory management and reducing food waste in the retail sector. (Kuleshov et al, 2019.…”

Section: Discussionmentioning

confidence: 99%

Development of a Decision Support System for Perishable Food Management in Culinary Education Institutions Using Machine Learning with Linear Regression

Makin,

Abdullah,

Abbas Helmi

2024

JMS

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This study presents the development of a decision support system tailored for educational institutions with culinary programs, aimed at optimizing the management of perishable food items. The system employs a machine learning approach, specifically linear regression, to predict and enhance decision-making regarding the handling, storage, and utilization of perishable ingredients. The foundation of the system lies in the collection and analysis of historical data pertaining to various perishable food items commonly utilized in culinary education. Key variables, including temperature, humidity, storage duration, and other relevant factors, are identified to build a robust linear regression model. This model serves to predict the remaining shelf life of perishable goods, offering valuable insights into optimal storage conditions. Instructors benefit from a tool that enhances the practical learning experience, fostering a deeper understanding of food preservation and waste reduction. Continuous feedback mechanisms are integrated to improve the system's accuracy and relevance over time. Culinary educators can contribute insights, ensuring that the system remains aligned with the dynamic needs of culinary education.By incorporating this decision support system into culinary programs, educational institutions can not only empower students with practical skills but also in still a culture of sustainability and resource efficiency. The linear regression model, as a part of the system, aids in making informed decisions that align with industry best practices, contributing to the development of responsible and skilled culinary professionals. This research represents a significant step towards leveraging machine learning techniques to enhance culinary education, offering a practical and innovative solution for the management of perishable food items within educational settings.

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Section: Discussionmentioning

confidence: 99%

Development of a Decision Support System for Perishable Food Management in Culinary Education Institutions Using Machine Learning with Linear Regression

Makin,

Abdullah,

Abbas Helmi

2024

JMS

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“…Cai et al [18] demonstrated the relationship between the optimal selling price and optimal inventory level for deteriorating products. Many scholars have researched the issues relating to the ordering or pricing of perishable goods from different perspectives [19][20][21][22]. The abovementioned scholars only examined the ordering or pricing decisions, while other scholars have investigated joint ordering and pricing decisions.…”

Section: Management Of Supply Chains For Deteriorating Productsmentioning

confidence: 99%

Fresh Produce Ordering, Pricing and Freshness-Keeping Decisions with Call Option Contracts and Spot Markets

Jia,

Chen,

Wang

2024

Systems

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Considering the characteristics of both quality and quantity losses in fresh produce as well as the existence of spot markets, optimal retailer ordering, pricing, and freshness-keeping decisions through the single ordering policy (firm ordering only or option ordering only) and the mixed ordering policy (firm ordering and option ordering simultaneously) are constructed based on option contracts and analyzed for the retailer under different ordering policies. The results show that there is a unique optimal pricing, ordering, and freshness-keeping decision under all three ordering policies, but there is no joint decision. The optimal freshness-keeping and retail price under the mixed ordering policy are lower than those under the option ordering only but higher than those under the firm ordering only. When only a single order can be placed, the retailer’s optimal ordering policy is determined by demand risk. When all three ordering policies are available, the optimal ordering policy for the retailer is the mixed ordering policy. A spot market will weaken the role of option contracts in mitigating supply chain risks, and the larger the risk, the more significant the role of the spot market.

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“…He et al [15] examined how suppliers of complementary products on digital platforms sustain cooperation via repeated interactions in a supply chain. Mamoudan et al [16] analyzed the demand and pricing of perishable food and its complementary products and found that independent procurement provides retailers with higher profit. The competition and supply-chain strategies between electric and gasolinebased vehicles were addressed in Rasti-Barzoki and Moon's study [17].…”

Section: Access Strategy For Complementary Productsmentioning

confidence: 99%

Competitive Firms’ Optimal Decisions on Entering Accessory Market

Wang

Hung

2023

Sustainability

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The smartphone is now a widespread trend that causes phone accessory markets to rise. The high penetration rate of smartphones has led to a saturated market. The accessory market not only has tremendous business opportunities but also high potential. In practice, the decision of whether to join the accessory market differentiates by each smartphone firm. This study analyzed and discussed the main product firms’ optimal decisions of whether to enter the accessory market by using game theory as a construct model. Considering the different competition situations, the decision of whether the main product firm invests in the accessory market affected the market demand, pricing, and profit of all firms. Furthermore, we analyzed the differences in the external environment to determine what the best decision for the main product firm was. The results of the research show that complementary effects do not always have a positive influence on firms’ willingness to join the accessory market. When weak main product firms have different market forces in the main product market, the dominant main product firms are not necessarily more willing to join the accessory market than weak firms.

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Food products pricing theory with application of machine learning and game theory approach

Cited by 17 publications

References 66 publications

Development of a Decision Support System for Perishable Food Management in Culinary Education Institutions Using Machine Learning with Linear Regression

Development of a Decision Support System for Perishable Food Management in Culinary Education Institutions Using Machine Learning with Linear Regression

Fresh Produce Ordering, Pricing and Freshness-Keeping Decisions with Call Option Contracts and Spot Markets

Competitive Firms’ Optimal Decisions on Entering Accessory Market

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