A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture

Abstract

A two-stage stochastic programming with recourse model for the problem of determining optimal planting plans for a vegetable crop is presented in this paper.Uncertainty caused by factors such as weather on yields is a major influence on many systems arising in horticulture. Traditional linear programming models are generally unsatisfactory in dealing with the uncertainty and produce solutions that are considered to involve an unacceptable level of risk. The first stage of the model relates to finding a planting plan which is common to all scenarios and the second stage is concerned with deriving a harvesting schedule for each scenario. Solutions are obtained for a range of risk aversion factors that not only result in greater expected profit compared to the corresponding deterministic model but also are more robust.

A chance-constrained multi-period model for a special multi-reservoir system

“…Applications of stochastic optimization and chance-constrained programming techniques in agriculture can be found in a number of studies [9,[22][23][24][25].…”

Planning Agricultural Water Resources System Associated With Fuzzy and Random Features1

“…As a result, many inexact optimization methods, based on fuzzy set theory and stochastic analysis, were advanced for addressing uncertainties of fuzziness and randomness in water resources management systems. Stochastic programming is effective for dealing with decision problems whose coefficients (input data) are not certainly known but could be represented as chances or probabilities (Dupačová et al, 1991;Rangarajan and Simonovic, 1999;Darby-Dowman et al, 2000;Li et al, 2006;Ganji et al, 2007;Harrison, 2007;Guha, 2008;Kumar and Merwade, 2009;Qin and Huang, 2009;Cui et al, 2010). For example, Abrishamchi et al (1991) studied reservoir systems planning for irrigation districts through a chance-constrained programming (CCP) model; Huang (1998) developed an inexact CCP method for examining risk of violating system constraints and for dealing with uncertainties expressed as probabilities and intervals.…”

An Inexact Energy System Optimization Model including Environmental Constraints for Beijing, China

“…SLP and FLP have been used extensively by energy planners [6][7][8]. SLP and FLP can effectively express the stochastic aspects of a model's right-hand-sides, but encounters difficulties when the left-hand-sides are highly uncertain.…”