Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning

Sharma, Dinesh K.; Jana, Rabin K.

doi:10.1016/j.ijpe.2009.05.009

Cited by 37 publications

(14 citation statements)

References 21 publications

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“…Some of the systems include crop land suitability proposed by T. V. R e s h m i d e v i et al [7], running of biogas reactor on energy crops by P. S c h e r e r et al [8], managing water resources as mentioned by B. M a l e k m o h a m m a d i et al [9], irrigation systems described by P. J a v a d i K i a et al [10], yield behaviour using fuzzy cognitive maps proposed by E. I. P a p a g e o r g i o u et al [11], assessment of soil conditions in agro-eco systems proposed by D i e g o O. F e r r a r o [12], planning of nutrient management for rice crops listedby D. K. S h a r m a and R. K. J a n a [13], minimizing grain losses especially at position of straw walker and upper sieve by automatic adjustment and control of a harvester as proposed by M a h m o u d O m i d et al [14], studying land reallocation by T a y f u n C a y a n d and F a t i h I s c a n [15], sustainability of soil productivity by N e v c i h a n D u r u et al [16], assessment of uncertainties in different management aspects of an environmental system by J. A. E. B. J a n s s e n et al [17], T a n g H u i l i et al [18], presented an agriculture disease diagnosis expert system using fuzzy reasoning and an Euclidean distance method which is used to calculate the comparability; efficient diagnosis results and reliabilities to fulfill the complexity of an agricultural disease problem.…”

Section: Refrences Reviewmentioning

confidence: 99%

Error reduction in promoted confidence factor of a rule using improved fuzzy rule promotion technique

Jain

Kumar

Singla

et al. 2014

Cybernetics and Information Technologies

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Section: Refrences Reviewmentioning

confidence: 99%

Error reduction in promoted confidence factor of a rule using improved fuzzy rule promotion technique

Jain

Kumar

Singla

et al. 2014

Cybernetics and Information Technologies

View full text Add to dashboard Cite

“…The corresponding linear membership function of the three new objective functions can be defined according to Equations. (17) to (19). The authors used MATLAB computer software to solve the proposed Fuzzy Based Genetic Algorithm (FBGA) approach for the CCKL case.…”

Section: IVmentioning

confidence: 99%

Solving an Aggregate Production Planning Problem by Fuzzy Based Genetic Algorithm (FBGA) Approach

Chakrabortty¹,

Hasin

2013

IJFLS

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This paper work demonstrated an interactive Fuzzy Based Genetic Algorithm (FBGA) approach for solving a two products & two periods aggregate production planning (APP) with some vulnerable managerial constraints like imprecise demands, variable manufacturing costs etc. This proposed approach used the strategy of simultaneously minimizing the most possible value, the most pessimistic value & also most optimistic value of the imprecise total costs in lieu of some strong resource constraints. Another important purpose of this study is to derive & observe the variations along with the scope of the imprecise total cost, maximizing the possibility of obtaining lower total costs and also minimizing the risk of obtaining higher total costs. Here the authors employed different unique genetic algorithm parameters scrupulously for solving nondeterministic polynomials problems like APP problems. For reinforcing & accelerating the decision making for the decision maker a case study was considered in a Ready Made garment company in Bangladesh. Consequently, the proposed FBGA approach yields an efficient APP compromise solution and could be efficient for large scale problems.

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“…The decision-maker can find alternative optimal solutions from a series of alternative values (Sharma & Jana, 2009). In order for GAs to surpass their more traditional cousins in the quest for robustness, they must vary in some very fundamental ways (Goldberg, 1989).…”

Section: Introductionmentioning

confidence: 99%

Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach

Chakrabortty¹,

Hasin²

2013

ijiec

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In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

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Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning

Cited by 37 publications

References 21 publications

Error reduction in promoted confidence factor of a rule using improved fuzzy rule promotion technique

Error reduction in promoted confidence factor of a rule using improved fuzzy rule promotion technique

Solving an Aggregate Production Planning Problem by Fuzzy Based Genetic Algorithm (FBGA) Approach

Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach

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