A multiple objective methodology for sugarcane harvest management with varying maturation periods

Abstract

This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.

Bi-objective load balancing multiple allocation hub location: a compromise programming approach

“…Compromise programming is an approach parameterized by weight factors w i . In a transition from L 1 to L ∞ , the computational behavior of the problem changes dramatically from a linear combination of objective functions to a minimax objective function, which is known to be computationally challenging Florentino et al (2017). In addition, our initial computational experiments with different decomposition techniques revealed that different combinations of w i in both L 1 and L ∞ cases, lead to models with significantly different computational behavior.…”

A multi-objective approach to sugarcane harvest planning in Thailand: Balancing output maximization, grower equity, and supply chain efficiency

“…The traditional approach for estimating peak periods is based on the elapsed time since a predefined date, i.e. the date in which sugarcane is planted (Florentino et al, 2018;Jiao, Higgins, & Prestwidge, 2005;Pagani et al, 2017), although such a prediction may be imprecise due to several uncontrollable factors, including unexpected weather conditions, sugarcane types, and plant diseases (Florentino et al, 2018;Grunow, Gunther, & Westinner, 2007).…”

“…While the CCS value lies at the core of most sugarcane harvesting operations, it is normally impossible to harvest every field at its corresponding optimal time period, due to limited harvesting and milling resources (Florentino et al, 2018). As such, growers within the same group must negotiate a harvesting schedule that builds up transportation amounts satisfying production quotas and, at the same time, provides good expected yields for their fields.…”

Modelagem Matemática De Otimização Aplicada Ao Planejamento Do Plantio De Cana-De-Açúcar E Cana-Energia

“…A partir de 2014, pôde-se observar a realização de estudos utilizando o processo de Florentino et al (2018) também focaram sua pesquisa no processo de colheita e nos respectivos custos, e ainda, na maximização da sacarose, porém, realizaram a diversificação do processo de modelagem matemática de otimização ao empregar técnicas aproximativas, e ainda, por se preocuparem com variáveis relacionadas ao processo de cultivo da cana-de-açúcar propriamente dito. Ao testar computacionalmente um algoritmo genético, comparativamente a um método de otimização exato, Florentino et al (2018) concluíram que para a solução de problemas de fazendas de médio e grande porte, os métodos aproximativos baseados em heurística (técnicas aproximativas) são mais recomendados, ao passo que os métodos exatos se aplicam melhor a problemas de otimização de fazendas de pequeno porte.…”