Inventory Optimization of Fresh Agricultural Products Supply Chain Based on Agricultural Superdocking

Shen, Lianfeng; Li, Fucheng; Li, Cong-Cong; Wang, Yumin; Qian, Xueqi; Feng, Tao

doi:10.1155/2020/2724164

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…Bakker et al [4] reviewed the inventory control issues of the fresh agricultural product supply chain. Shen et al [5] studied the best supply and replenishment strategy under the "agricultural super docking" model. Su and Ma [6] studied the pricing and inventory decision-making of retailers with the assumption that stock-outs are allowed and partially delayed.…”

Section: Operational and Management Issues Of Fresh Product Supply Chainmentioning

confidence: 99%

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Research on Coordination of Fresh Agricultural Product Supply Chain considering Fresh-Keeping Effort Level under Retailer Risk Avoidance

Yan-gang

2021

Discrete Dynamics in Nature and Society

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Considering that the demand for fresh agricultural products is affected by product freshness and price, a two-level fresh agricultural product supply chain decision model consisting of a risk-neutral supplier and a risk-averse retailer is constructed. In order to increase consumer demand for fresh agricultural products, the supplier will make appropriate efforts to preserve the freshness of agricultural products. The optimal fresh-keeping effort level of the supplier and the optimal pricing decision of the retailer under the centralized decision-making and decentralized decision-making modes were studied, respectively; through the design of traditional cost-sharing contracts, traditional cost and revenue-sharing contracts, and cost-sharing and compensation strategies, the supplier was encouraged to improve their fresh-keeping effort. The research shows that the traditional cost-sharing contract and the traditional cost-benefit sharing contract cannot coordinate the supply chain. Under the strategy of cost sharing and compensation, when the amount of compensation meets certain conditions, the coordination of supply chain can be realized. Finally, the important parameters of the model are analyzed by numerical simulation.

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Section: Operational and Management Issues Of Fresh Product Supply Chainmentioning

confidence: 99%

“…Calculate the partial derivatives of the product wholesale price and the fresh-keeping effort level of equation (5), respectively, to obtain the second-order Hessian matrix:…”

Section: B Proof Of Propositionmentioning

confidence: 99%

Research on Coordination of Fresh Agricultural Product Supply Chain considering Fresh-Keeping Effort Level under Retailer Risk Avoidance

Yan-gang

2021

Discrete Dynamics in Nature and Society

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“…Consumer demand, perception, and buying behavior can be approached with a probabilistic system [ 140 , 141 , 142 ], for instance, uncertainty concerning what food products are expected to be bought. Meanwhile, daily demand prediction and inventory management can be addressed with an optimization paradigm [ 143 , 144 ]. For this case, the aim is to optimize stock levels in such a way that food waste can be decreased or even to avoiding over-stocking issues completely.…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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“…Por último, y no menos importante se presentan aportes de los AG en el área de almacenamiento y gestión de inventarios [45][46][47][48][49][50][51] y en el área de selección, evaluación de proveedores respecto a una demanda variable [52][53][54][55][56][57].…”

Section: Algoritmos Genéticosunclassified

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Palacio

2021

Rev. politec.

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La inteligencia artificial ofrece grandes oportunidades para la cadena de suministro, siendo esto una ventaja competitiva para el mercado cambiante de hoy en día. Este artículo tiene como objetivo identificar los impactos y oportunidades que puede ofrecer el software con inteligencia artificial para facilitar la operación y mejorar el desempeño de la cadena de suministro en el sector bananero de Colombia. La metodología de trabajo consta de seis pasos en donde se obtuvo un total de 72 investigaciones. Las fuentes de información fueron cuatro bases de datos. Como conclusión principal, la cadena de suministro del sector bananero tiene todo lo necesario para que se implementen soluciones basadas en software inteligente con el fin de lograr una adaptación, flexibilidad y sensibilidad al contexto y dominio de ejecución. Artificial intelligence offers great opportunities for the supply chain, making it a competitive advantage for today's changing market. This paper aims to identify the impacts and opportunities that artificial intelligence software can offer to supply chain in the Colombian banana sector to facilitate the operation and improve the performance. The searching method consists of six steps getting 72 investigations finally. The sources of information were four databases. The main conclusion is the supply chain of the banana sector has everything for implementation of solutions based on intelligent software in order to achieve adaptation, flexibility and context awarenes and execution domain.

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Inventory Optimization of Fresh Agricultural Products Supply Chain Based on Agricultural Superdocking

Cited by 18 publications

References 14 publications

Research on Coordination of Fresh Agricultural Product Supply Chain considering Fresh-Keeping Effort Level under Retailer Risk Avoidance

Research on Coordination of Fresh Agricultural Product Supply Chain considering Fresh-Keeping Effort Level under Retailer Risk Avoidance

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

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