Determining optimal policies for sugarcane harvesting in Thailand using bi-objective and quasi-Newton optimization methods

Abstract

In Thailand, the harvesting season for sugarcane usually begins in November and ends the following May. At the beginning of each harvesting season, the Royal Thai government sets the price of two types of sugarcane, namely fresh and fired, based on sweetness (sugar content) and gross weight of sugarcane delivered to the sugar factories. The aim of the present research is to determine optimal harvesting policies for the two types of sugarcane in sugarcane producing regions of Thailand in order to maximize revenue and minimize harvesting cost. In this paper, a harvesting policy is defined as the amount of each type of sugarcane harvested and delivered to the sugar factories during each 15-day period of a harvesting season. Two optimization methods have been used to solve this optimization problem, namely the ε-constraints method and a quasi-Newton optimization method. In the ε-constraints method, the problem is considered as a bi-objective optimization problem with the main objective being to determine harvesting policies that maximize the total revenue subject to upper bounds on the harvesting cost. In the quasi-Newton method, the aim is to determine the harvesting policy which gives maximum profit to the farmers subject to constraints on the maximum amount that can be cut in a 15-day period. The methods are used to determine optimal harvesting policies for the north, central, east, and northeast regions of Thailand for harvesting seasons 2012/13, 2013/14, and 2014/15 based on the data obtained from the Ministry of Industry and the Ministry of Agriculture and Cooperatives of the Royal Thai government.

An Application of Optimal Control to Sugarcane Harvesting in Thailand

“…For the bi-objective optimization problem, we separated the profit P(t k , x k , u k ) per 15-day period into a revenue term R(t k , x k , u k ) and a cost term C(t k , x k , u k ) and assumed that r = 0, that is, no growth during the harvesting season. We then used the ε-constraints method [4,14] to solve the bi-objective optimization problem…”

“…As in the previous paper [14], we used the constrained optimization function fmincon in Matlab with the "active-set" algorithm based on quasi-Newton method [5][6][7] to find the optimal cutting patterns to maximize the profit in (2) for 15-day periods and we assumed that there was no growth during the harvesting season.…”

“…We use data obtained from the Ministry of Industry and the Ministry of Agriculture and Co-operatives of the Royal Thai government [9][10][11][12][13]. The present paper is an extension of a previous paper [14] in which bi-objective and quasi-Newton methods were used to find optimal harvesting policies for the crop years 2012/13, 2013/14 and 2014/15. For these two optimization methods, it was necessary to divide a harvesting season into 15-day periods and to assume that there was zero growth of sugarcane during a season.…”

“…Quasi-Newton optimization methods are well-known methods of optimization that have been used for many years to find optimal solutions for problems in many areas of science, finance and industry (see, e.g., [5][6][7]). In 2019, Pornprakun et al [14] used the bi-objective and quasi-Newton optimization methods (see, e.g., [5][6][7]) to determine optimal harvesting policies for sugarcane in Thailand in order to maximize the total revenue and minimize harvesting cost for crop years 2012/13, 2013/14 and 2014/15.…”

Contributions to Case-Based Reasoning Enabled Decision Support System for Smart Agriculture

“…Manimuthu et al [67] presented the application of Newton-Raphson algorithm in combination with the pulse width modulation bridge to characterize the energy cost associated with locomotion gaits, providing robots with the optimal area coverage strategy for agriculture harvesting. Pornprakun et al [68] explored the optimal harvesting policies for sugarcane by using an epsilon-constraints method and a quasi-Newton optimization method. The generated result was able to assist farmers in determining the optimal harvesting time with the maximum profits and the minimum harvesting cost.…”