Multi-Objective Mathematical Model for the Optimal Time to Harvest Sugarcane

Sungnul, Surattana; Pornprakun, Wisanlaya; Prasattong, Santipong; Baitiang, Chanasak

doi:10.4236/am.2017.83028

Cited by 4 publications

(3 citation statements)

References 3 publications

(2 reference statements)

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“…Sungnul et al [8] used the ε-constraints method to solve the mathematical model by choosing the revenue as the objective function and the costs as constraints. In 2017, Sungnul et al [9] extended the work in [7] to find the optimal harvesting times for all of the four regions of Thailand.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Sungnul et al used the ε-constraints method [4] to find the optimal cutting pattern by using the revenue as the objective function and the costs as constraints. In 2017, Sungnul et al [21] extended the work in [20] to find the optimal harvesting times for all of the four regions of Thailand. 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]).…”

Section: Introductionmentioning

confidence: 99%

An Application of Optimal Control to Sugarcane Harvesting in Thailand

et al. 2021

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The sugar industry is of great importance to the Thai economy. In general, the government sets sugarcane prices at the beginning of each harvesting season based on type (fresh or fired), sweetness (sugar content) and gross weight. The main aim of the present research is to use optimal control to find optimal sugarcane harvesting policies for fresh and fired sugarcane for the four sugarcane producing regions of Thailand, namely North, Central, East and North-east, for harvesting seasons 2012/13, 2013/14, 2014/15, 2017/18 and 2018/19. The optimality problem 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 each day, where a harvesting policy is defined as the amount of each type of sugarcane harvested and delivered to the sugar factories during each day of a harvesting season. The results from the optimal control methods are also compared with results from three optimization methods, namely bi-objective, linear programming and quasi-Newton. The results suggest that discrete optimal control is the most effective of the five methods considered. The data used in this paper were obtained from the Ministry of Industry and the Ministry of Agriculture and Co-operatives of the Royal Thai government.

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“…The latter is applied to forecast the production indicators of sugar industry enterprises, biofuel (ethanol), etc. [2][3][4][5][6]. In Brazil, 60% of the sugarcane fields are located in the state of São Paulo [7].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

Kavats¹,

Khramov²,

Sergieieva³

et al. 2020

Remote Sensing

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The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.

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Multi-Objective Mathematical Model for the Optimal Time to Harvest Sugarcane

Cited by 4 publications

References 3 publications

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

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

An Application of Optimal Control to Sugarcane Harvesting in Thailand

Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

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