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.

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The problem of selecting sugarcane varieties has been widely discussed due to its computational complexity and its great impact for the sugar and ethanol industry. This paper proposes a new integrated mathematical programming model to deal with the selection of sugarcane varieties to be planted and the determination of the optimal period for planting and harvesting in order to increase production in the sugarcane industry. The proposed model optimizes the production of sugarcane and improves the quality of biomass whilst satisfying the main constraints imposed by sugarcane companies. The problem is modelled as an integer linear program (ILP) and solved using an exact method to generate optimal solutions for small and medium problems. For large problems, metaheuristic approaches based on Genetic Algorithm (GA) and Variable Neighbourhood Search (VNS) are proposed. According to the results, the proposed methodology provides sugarcane company managers with decision support in selecting the most suitable varieties and in determining the best period to plant and harvest their sugarcane.

This paper proposes a goal programming methodology to ensure that a mix of balance and optimisation is achieved across a hierarchical decision network. The extended goal programming principle is used for this purpose. A model is constructed that provides consideration of balance and efficiency of multiple objectives and stakeholders at each network node level. A goal programming formulation to provide the decision that best meets the goals of the network is given. The proposed model is controlled by three key parameters that represent the level of non-compensation between objectives, level of non-compensation between stakeholders, and level of centralisation in the network. The methodology is demonstrated on an example pertaining to regional renewable energy generation and the results are discussed. Conclusions are drawn as to the effect of different attitudes towards compensatory behaviour between objectives and stakeholders in the network.

Resumo.O Brasilé hoje o maior produtor mundial de cana-de-açúcar e de açúcar.É o segundo colocado na produção de etanol, e tem conquistado o mercado externo com o uso de biocombustíveis. Assim, este país possui grandes companhias sucroenergéticas e estas possuem complexos sistemas de gerenciamento, tanto na parte industrial como no campo. Tendo os gestores que recorrer a diversas ferramentas computacionais e matemáticas para auxílio nos planejamentos e tomadas de decisões. Neste contexto, o proposto trabalho apresenta uma metodologia para auxílio no planejamento otimizado do plantio e colheita da cana-de-açúcar nas unidades agrícolas que compõem as usinas. Para isto foi proposto um modelo de otimização linear inteira com a finalidade de determinar o períodoótimo de plantio e colheita da cana em cada talhão, de forma a maximizar a produção de cana-deaçúcar. Os resultados computacionais obtidos são apresentados e discutidos.Palavras-chave. Problema de programação linear inteira, planejamento otimizado, canade-açúcar, algoritmo genético. IntroduçãoO problema de seleção de variedades de cana (SSVP-selection of sugarcane varieties problem)é muito importante devidoàs suas implicações no auxílio do planejamento econômico e ambiental necessários nas empresas sucroenergéticas. Portanto, uma extensa pesquisa tem sido conduzida abordando a escolha das variedades de cana-de-açúcar a serem plantadas e o planejamento da cultura da cana com diversos objetivos, entre eles: minimizar custos, aproveitar resíduos de colheita, maximizar lucro e otimizar a produção de açúcar [2][3][4]7].Mas a maioria dos autores trabalhou com casos simples e tamanhos moderados de SSVP, o que não condiz com a realidade atual. Estes sempre citam a complexidade 1 helenice@ibb.unesp.br 2