scholarly journals The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes Under Uncertain Demand, Yields, and Prices

2021 ◽  
Author(s):  
Sheng-I Chen ◽  
Wei-Fu Chen
Keyword(s):  
Programming Model ◽  
Sample Average Approximation ◽  
Stochastic Program ◽  
Uncertain Demand ◽  
Storage Space ◽  
Sample Average ◽  
Optimal Harvest ◽  
Stochastic Programming Model ◽  
Harvest Decisions ◽  
Average Approximation

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango type is suggested based on the stochastic program solution.

scholarly journals The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices

2021 ◽  
Vol 13 (17) ◽  
pp. 9660
Author(s):  
Sheng-I Chen ◽  
Wei-Fu Chen
Keyword(s):  
Programming Model ◽  
Sample Average Approximation ◽  
Stochastic Program ◽  
Uncertain Demand ◽  
Storage Space ◽  
Sample Average ◽  
Optimal Harvest ◽  
Stochastic Programming Model ◽  
Harvest Decisions ◽  
Average Approximation

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango variety is suggested based on the stochastic program solution.

scholarly journals Distribution-Free Stochastic Closed-Loop Supply Chain Design Problem with Financial Management

2019 ◽  
Vol 11 (5) ◽  
pp. 1236 ◽  
Author(s):  
Dapeng Yang ◽  
Daqing Wu ◽  
Luyan Shi
Keyword(s):  
Supply Chain ◽  
Financial Management ◽  
Closed Loop ◽  
Programming Model ◽  
Sample Average Approximation ◽  
Supply Chain Design ◽  
Closed Loop Supply Chain ◽  
Sample Average ◽  
Financial Flow ◽  
Average Approximation

Financial flow is an important part of supply chain management (SCM) and increasingly playing a crucial role as the amount of global trade increases. Reasonable and scientific financial operation is necessary in closed-loop supply chain management, especially when customer demand is uncertain. However, financial flow, which may lead to an increase in effectiveness, has rarely been considered in the literature. In this paper, we present a closed-loop supply chain design with financial management problem, which is tackled as a stochastic programming model with ambiguity demand set. The main contributions of this work include: (i) A joint chance constrained programming model is proposed to maximize the total profit, and (ii) financial flow and uncertain demand are both taken into consideration. According to the characteristic of the problem, we chose four approaches, namely sample average approximation (SAA), enhanced sample average approximation (ESAA), Markov approximation (MA), and mixed integer second-order conic program (MI-SOCP). Computational experiments were conducted to compare the adopted methods, and 10,000 scenarios were generated to examine the reliability of the methods. Numerical results revealed that the Markov approximation approach can achieve more reliable solutions.

Sample Average Approximation

2017 ◽  
pp. 75-84
Author(s):  
Tingsong Wang ◽  
Shuaian Wang ◽  
Qiang Meng
Keyword(s):  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation

SAMPLE AVERAGE APPROXIMATION METHOD FOR SOLVING A DETERMINISTIC FORMULATION FOR BOX CONSTRAINED STOCHASTIC VARIATIONAL INEQUALITY PROBLEMS

2012 ◽  
Vol 29 (02) ◽  
pp. 1250014
Author(s):  
MEI-JU LUO ◽  
GUI-HUA LIN
Keyword(s):  
Variational Inequality ◽  
Approximation Method ◽  
Deterministic Model ◽  
Random Parameter ◽  
Sample Average Approximation ◽  
Variational Inequality Problems ◽  
Stochastic Variational Inequality ◽  
Sample Average ◽  
Sample Average Approximation Method ◽  
Average Approximation

In this paper, we discuss the Expected Residual Minimization (ERM) method, which is to minimize the expected residue of some merit function for box constrained stochastic variational inequality problems (BSVIPs). This method provides a deterministic model, which formulates BSVIPs as an optimization problem. We first study the conditions under which the level sets of the ERM problem are bounded. Then, we show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in BSVIPs. Since the integrality involved in the ERM problem is difficult to compute generally, we then employ sample average approximation method to solve it. Finally, we show that the global optimal solutions and generalized KKT points of the approximate problems converge to their counterparts of the ERM problem. On the other hand, as an application, we consider the model of European natural gas market under price uncertainty. Preliminary numerical experiments indicate that the proposed approach is applicable.

scholarly journals Sample average approximation of stochastic dominance constrained programs

2010 ◽  
Vol 133 (1-2) ◽  
pp. 171-201 ◽  
Author(s):  
Jian Hu ◽  
Tito Homem-de-Mello ◽  
Sanjay Mehrotra
Keyword(s):  
Stochastic Dominance ◽  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation
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