Management decision-making for sugarcane fertilizer mix problems through goal programming

Sharma, Dinesh K.; Ghosh, Debasis; Alade, Julius A.

doi:10.1007/bf02936095

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In Sharma et al (2003), the optimal mix of sugarcane fertilizer is found using lexicographic goal programming with a quadratic distance measure. A case study arising from Indian sugarcane farms is used to illustrate the methodology.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…The Goal programming (GP) [12,13] is an efficient tool for dealing problems involving multiple and conflicting objectives. Lee [14], Romero [15], and Sharma et al [16] successfully implemented the GP approach in different decision making problems. For optimal production of seasonal crops, Ghosh et al [17] used penalty functions in the GP model for land allocation.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Fuzzy Chance Constrained Programming Model for Land Allocation in Agricultural Sector: A case study

Biswas¹,

Modak²

2017

IJCIS

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In this article a fuzzy multiobjective chance constrained programming model is used for modeling and solving land allocation problems efficiently with the help of fuzzy goal programming. Optimal production of seasonal crops and related expenditures are considered from the viewpoint of proper utilization of total cultivating land and different farming resources. Some resource parameters associated with the probabilistic constraints are taken as normally distributed fuzzy random variables. The potential use of this methodology is illustrated by a case example.

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“…Moreover, the multi criteria decision can be used in interactive decision making processes, as interaction becomes a dialogue where the model responds to an initial set of preferences and tradeoffs (Herath and Prato, 2006). Several authors (Lee 1972;Goodman 1974;Palmini 1982;Romero 1991;Sharma et al 2003) have successfully implemented the GP technique in order to solve different decision making problems (Sharma et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Sustainable cropping pattern in North Iran: application of fuzzy goal programming

Joolaie

Sarvestani

Taheri

et al. 2016

Environ Dev Sustain

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Due to the important important role that the application of mathematical programming models have in determining optimal cropping patterns, this research presents a sustainable cropping pattern that considers selected economic, environmental, and social goals together. Using a random sampling method, a sample size of 168 farmers was selected in the Sari County, Iran. Our results showed that economic, self-sufficiency, environmental, and social goals have a distinctly different impact on cropping pattern performance. Compared to the current cropping pattern, the gross margins for economic and social goals increased by nearly 11% and 2%, respectively, and the gross margins for self-sufficiency and environmental goals decreased by nearly 2% and 36%. Interestingly, it has been found that the performance of the current cropping pattern has an average positive impact of 6% if economic, self-sufficiency, environmental, and social (employment) goals are realized simultaneously.

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Management decision-making for sugarcane fertilizer mix problems through goal programming

Cited by 8 publications

References 6 publications

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

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

A Multiobjective Fuzzy Chance Constrained Programming Model for Land Allocation in Agricultural Sector: A case study

Sustainable cropping pattern in North Iran: application of fuzzy goal programming

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