z-logo
open-access-imgOpen Access
An optimization model for the combined planning and harvesting of sugarcane with maturity considerations
Author(s) -
Rômulo Pimentel Ramos,
Roberto Isler Paulo,
Helenice de Oliveira Florentino,
Dylan Jones,
Jecks Nervis Jonis
Publication year - 2016
Publication title -
african journal of agricultural research
Language(s) - English
Resource type - Journals
ISSN - 1991-637X
DOI - 10.5897/ajar2016.11441
Subject(s) - agricultural engineering , production (economics) , genetic algorithm , factory (object oriented programming) , mill , computer science , sowing , selection (genetic algorithm) , cane , operations research , engineering , agronomy , economics , mechanical engineering , biochemistry , chemistry , sugar , machine learning , artificial intelligence , biology , macroeconomics , programming language
Planting and harvesting are important stages in the sugarcane crop cycle, because well planned planting and harvesting phases promote a series of benefits throughout the cultivation cycle and in the subsequent industrial use of the products. These benefits are operational, economic and environmental such as: improved utilization of the land area and transport logistics; an increase in sugarcane output; better cane reception in the factory; in administrative simplification of the industrial activities; in enhanced response to the demands of the industry; in cost planning; and in the control of pests and weeds. In this work a methodology of optimal cultivation planning to sugarcane planting and harvesting is proposed. The cultivation plan is for 5 years; and key decisions to be made in this period are to determine the planting date, the variety selection and the harvesting date corresponding for each plot such that the global production is optimized. We propose a mathematical model for this optimization task. The model uses computational and mathematical strategies to ensure that date of harvesting is always in period of the maximum maturation of the sugarcane and considers all demand and other operational constraints of the processing mill. The binary nonlinear optimization model was solved by a proposed genetic algorithm, giving an optimum plan with a potential sugarcane production 17.8% above production obtained by conventional means in the mill. Key words: Genetic algorithm, integer nonlinear optimization model, optimal planning, Saccharum spp., sugarcane planting and harvesting.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom