Advantages of combining linear programming and weighted goal programming for agriculture application

Prišenk, Jernej; Turk, Jernej; Rozman, Črtomir; Borec, Andreja; Zrakić, Magdalena; Pažek, Karmen

doi:10.1007/s12351-014-0159-4

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…In some cases, in the agriculture sector, the problems are defined with multi goals. So LP has a shortcoming in optimizing solutions with multi goals problem [34]. Other mathematical programming approaches that are utilized for Copyright © 2021 MECS I.J.…”

Section: Discussionmentioning

confidence: 99%

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A Review of Applications of Linear Programming to Optimize Agricultural Solutions

Alotaibi¹,

Nadeem²

2021

IJIEEB

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Quantitative methods help farmers plan and make decisions. An apt example of these methods is the linear programming (LP) model. These methods acknowledge the importance of economizing on available resources among them being water supply, labor, and fertilizers. It is through this economizing that farmers maximize their profit. The significance of linear programming is to provide a solution to the existing real-world problems through the evaluation of existing resources and the provision of relevant solutions. This research studies various LP applications including feed mix, crop pattern and rotation plan, irrigation water, and product transformation; that have the main role to enhance various facets of the agriculture sector. The paper will be a review that will probe into the applications of the LP model and it will also highlight the various tools that are central to analyzing LP model results. The review will culminate in a discussion on the different approaches that help optimize agricultural solutions.

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Section: Discussionmentioning

confidence: 99%

“…These approaches were used besides LP to get valuable results [23,24,25]. For example; The benefits of combining LP with WGP are that LP can help in minimizing or maximizing the objectives, while WGP simultaneously meets all LP sub-model objectives [34]. This combination was tested and gave reliable results [31,32].…”

Section: Discussionmentioning

confidence: 99%

A Review of Applications of Linear Programming to Optimize Agricultural Solutions

Alotaibi¹,

Nadeem²

2021

IJIEEB

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“…Examples are the multicriteria model for the assessment of rural development plans in Greece [13], the model for rural households to measure the effects of the Common Agricultural Policy (CAP) in three Southern European countries [14], and similar examples are the work of Xu et al [15], Valiakos and Siskos [16] and Prišenk et al [17]. The use of multicriteria models for ranking regions is also very common in the literature [18,19].…”

Section: Multicriteria Methods For Biomass Productionmentioning

confidence: 99%

Biomass Production from Crops Residues: Ranking of Agro-Energy Regions

Moulogianni¹,

Bournaris²

2017

Energies

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Abstract:The aim of the paper is to rank the agro-energy regions according to their potentials of biomass production in the Region of Central Macedonia (RCM). For this reason, a model of Multi-Criteria Analysis (MCDA) is developed with the ELimination and Et Choix Traduisant la REalite (ELECTRE) III method, with the construction of outranking relations. The aim is to compare in a comprehensive way each pair of action, in our case the agro-energy regions of the RCM, in order to satisfy the main goal which is to rank the seven regions as regards their biomass production. The final goal is to select the optimal crop plan as a pilot case for biomass production in the region. In the case of ELECTRE III multicriteria model, we used several conflicting criteria such as the farm income, the biomass production from crop residues, the variable costs, and the production of thermal energy and electrical energy. Alongside a technical and economic analysis of the study area is conducted for the existent crop plans of each agro-energy region. The results show that agro-energy regions with cereals and arable crops have better results than regions with fruit trees and other crops.

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“…In Baraku et al (2015), production planning across a set of eight agricultural farms is optimized via goal programming. In Prišenk and Turk (2015) and Prišenk et al (2014), the weighted goal programming is used to treat the crop rotation problem in organic farms in Slovakia.…”

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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Advantages of combining linear programming and weighted goal programming for agriculture application

Cited by 21 publications

References 13 publications

A Review of Applications of Linear Programming to Optimize Agricultural Solutions

A Review of Applications of Linear Programming to Optimize Agricultural Solutions

Biomass Production from Crops Residues: Ranking of Agro-Energy Regions

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

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