Optimal Selling Policies for Farmer Cooperatives

Shi, Jim; Zhao, Yue; Kiwanuka, Rose; Chang, Jasmine

doi:10.1111/poms.13091

Cited by 29 publications

(17 citation statements)

References 55 publications

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“…According to the work in [ 78 ], an optimal selling policy can increase the expected profit. Therefore, being able to combine and obtain real-time price information allows adjusting purchase prices in anticipation of the problem of making strategic sales decisions [ 80 ]. In the current case, a paradigm in which each stakeholder (i.e., direct FMCG chains, wholesalers, etc.)…”

Section: Discussionmentioning

confidence: 99%

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Pérez-Pons

Alonso

García

et al. 2021

Sensors

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Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

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Section: Discussionmentioning

confidence: 99%

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Pérez-Pons

Alonso

García

et al. 2021

Sensors

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show abstract

“…Function ( 6) indicates the probability that sales quantity for each subsequent period does not exceed the order quantity in the current period cannot be lower than α. Function (7) indicates the probability that the sales quantity does not exceed the order quantity in the current period cannot be lower than α. Obviously, function (6) contains the condition of function (7).…”

Section: Multiperiod Order and Sales Decision Modelmentioning

confidence: 99%

“…Shi et al established a random and dynamic inventory model of storable agricultural products with exogenous supply and prices. Farmers' cooperatives use this model to determine the best sales and inventory strategies and obtain the maximum expected profit [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Ordering and Selling Policy for Storable Agricultural Products in Contract Farming

Shui

Chen

2022

Scientific Programming

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With the farming mode widely promoted in many countries around the world, agricultural enterprises need to face risks from both supply and sales at the same time. Especially for storable agricultural products, it is also necessary to consider the potential profits and losses brought about by the sales of multi-period. This paper establishes a joint ordering and selling decision model under multiperiod random supply and demand. The model takes the maximum expected profit of agricultural enterprises as the goal, considers the constraints of production capacity and the risk preference coefficient of shortage, and makes decisions on the order and sales quantity in each cycle. The feasibility of the model was verified by taking China Yunnan Pu’er tea as a specific example. Finally, through numerical examples, the influence of important parameters on the joint ordering and selling strategy is as follows: order cost is the main factor affecting the order quantity. When the order cost increases to a certain level, the solving algorithm will make a decision that would rather be out of stock than to order. With the increase in selling price, inventory holding cost, and shortage cost, there will be an increasing trend of the sales quantity allocated to this period. Compared with other parameters, selling price and inventory holding cost have a more remarkable impact on sales quantity. In addition, enterprises can increase their expected profits of storable agricultural products by controlling the inventory holding cost, appropriately reducing the risk preference coefficient, and increasing the level of the selling price.

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“…These decision can vary from management practices (applying fungicide, nitrogen, fertilizer, etc.) to determining future holding costs with respect to yield futures from the Chicago Mercantile Exchange [12][13][14]. Due to the manual labor needed to count the number of kernels on an ear of corn, high-throughput phenotyping is not possible due to the necessary manual labor and the possibility of human error.…”

Section: Motivationmentioning

confidence: 99%

Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

Khaki

Pham

Han

et al. 2020

Sensors

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Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.

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Optimal Selling Policies for Farmer Cooperatives

Cited by 29 publications

References 55 publications

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Joint Ordering and Selling Policy for Storable Agricultural Products in Contract Farming

Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

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