Rice is the dominant crop of West Bengal. Proper nitrogen, phosphorus and potassium applications to the soil are necessary to increase its production, and improve the economic position of farmers. Application of nutri...
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Rice is the dominant crop of West Bengal. Proper nitrogen, phosphorus and potassium applications to the soil are necessary to increase its production, and improve the economic position of farmers. Application of nutrients to the soil is commonly done by using fertilizers. This paper presents a goal programming technique for nutrient management by determining the optimum fertilizer combination for rice production in West Bengal. sensitivity analysis has been performed on the cost of fertilizer combinations and production of rice to obtain nutrient information. Game Theory has been applied to identify the nutrient proportion. (C) 2003 Elsevier B.V. All rights reserved.
作者：
de Oliveira, FVolpi, NMPSanquetta, CRUFPR
Dept Math BR-81531990 Curitiba Parana Brazil UFPR
Post Graduat Engn Numer Methods UEPG Dept Math & Stat BR-81531990 Curitiba Parana Brazil UFPR
Dept Forestry Curitiba Parana Brazil
The objective of this paper is to apply one of the techniques of multiobjective programming (goal programming) in a brazilian forest problem, in a case study accomplished in the Santa Candida Farm, Parana, Brazil. The...
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The objective of this paper is to apply one of the techniques of multiobjective programming (goal programming) in a brazilian forest problem, in a case study accomplished in the Santa Candida Farm, Parana, Brazil. The areas of this farm can be managed for timber (pine and native species), harvesting of erva-mate leaves, pasture, and tourism. There is also a concern of the farm managers with increasing the diversity of flora and fauna, increasing environmental protection conditions and maintaining employees in the farm. goal programming was used to develop a project of land allocation, in which all the goals would be reached as closest as possible of the ideal, in a way to attend all the operational restrictions considered. In goal programming, the concept of optimum solution of LP problems is substituted by a satisfactory solution (nondominated). Several solutions can be obtained, and the best solution will depend on the priority associated to each goal. (C) 2002 Elsevier Science Inc. All rights reserved.
This paper applies a multiobjective goal programming (GP) model to define the profile of the most profitable insurers by focusing on 14 firm-decision variables and considering different scenarios resulting from the ex...
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This paper applies a multiobjective goal programming (GP) model to define the profile of the most profitable insurers by focusing on 14 firm-decision variables and considering different scenarios resulting from the exogenous change in interest rate and GDP per capita growth variables. We consider a detailed database of Spanish non-life insurers over the period 2003-2012 taking into account two dimensions of insurers' results: underwriting results and investment results. A prior econometric analysis is used to find out relevant relations among the variables. Next, a GP model is formulated on the basis of the relationships obtained. The model is tested in a robust environment, allowing changes in the coefficients of the objective functions, and for several scenarios regarding crisis/noncrisis situations and changes in interest rates. We find that having the stock organizational form, being an unaffiliated single company and maintaining low levels of investment risk, leverage, and regulatory solvency are recommended for result optimization. Growth and reinsurance utilization are not advisable for optimizing the results, whereas size should be positively emphasized even more in instability periods and when interest rates increase. The results also show that the optimal level of the diversification/specialization strategy depends on economic conditions. More specialization is advisable as negative changes in interest rates increase. However, we find that the optimal values of the diversification variable are higher for the crisis scenarios compared to the corresponding noncrisis scenarios, suggesting that diversification creates value in crisis. Further sensitivity analyses show the soundness of the conclusions obtained.
Cross efficiency evaluation has long been proposed as an alternative method for ranking the decision making units (DMUs) in data envelopment analysis (DEA). This study proposes goal programming models that could be us...
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Cross efficiency evaluation has long been proposed as an alternative method for ranking the decision making units (DMUs) in data envelopment analysis (DEA). This study proposes goal programming models that could be used in the second stage of the cross evaluation. Proposed goal programming models have different efficiency concepts as classical DEA, minmax and minsum efficiency criteria. Numerical examples are provided to illustrate the applications of the proposed goal programming cross efficiency models. (C) 2011 Elsevier Inc. All rights reserved.
goal programming (GP) is one of the most commonly used mathematical programming tools to model multiple objective optimisation (MOO) problems, There are numerous MOO problems of various complexity modelled using GP in...
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goal programming (GP) is one of the most commonly used mathematical programming tools to model multiple objective optimisation (MOO) problems, There are numerous MOO problems of various complexity modelled using GP in the literature, One of the main difficulties in the GP is to solve their mathematical formulations optimally. Due to difficulties imposed by the classical solution techniques there is a trend in the literature to solve mathematical programming formulations including goal programmes, using the modern heuristics optimisation techniques, namely genetic algorithms (GA), tabu search (TS) and simulated annealing (SA). This paper uses the multiple objective tabu search (MOTS) algorithm, which was proposed previously by the author to solve Gl? models. In the proposed approach, GP models are first converted to their classical MOO equivalent by using some simple conversion procedures. Then the problem is solved using the MOTS algorithm. The results obtained from the computational experiment show that MOTS can be considered as a promising candidate tool for solving GP models.
In this paper, we present a goal programming model for block level energy planning in order to make a block self-sufficient in electricity consumption, which includes the commercial energy consumption goal, the goal o...
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In this paper, we present a goal programming model for block level energy planning in order to make a block self-sufficient in electricity consumption, which includes the commercial energy consumption goal, the goal of generating electricity from biomass and food production goals with linear constraints on the available sources such as human power, animal power, tractor power, land area and on the requirement of the block such as cooking energy, lighting energy and energy for other operations, such as fodder for animal population. We try to achieve these goals through proper allocation of land for different crops. After reformulating the developed goal programming model into a linear programming format, we use the HYPER LINDO software package to solve it in a Pentium-based IBM-PC compatible computer system. The developed model is solved for a typical Indian block, namely Nilakkottai Block in Tamil Nadu, India. The model solution shows that the goal of generating electricity from biomass is achieved, the commercial energy consumption goal and pulses requirement goal are under-achieved and the sugar requirement goal is over-achieved. Furthermore, the cereal, vegetable and oilseed production goals are achieved. Copyright (C) 2000 John Wiley & Sons, Ltd.
The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framew...
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The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners' NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure. Published by Elsevier Ltd.
We present deterministic and stochastic goal programming models for coordinating of flock collection activities in a poultry processing company. The aim is to develop weekly schedules that balance three goals: ensurin...
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We present deterministic and stochastic goal programming models for coordinating of flock collection activities in a poultry processing company. The aim is to develop weekly schedules that balance three goals: ensuring a steady workload in the processing facility, reducing processing defects due to weight differences, and fulfilling production targets of farmers, while satisfying logistical constraints. Two deterministic goal programming models are proposed: a weighted model that considers the collective interests of farmers, and a min-max model that prevents large deviations from the production target for any individual farmer. Furthermore, two-stage stochastic programming models are developed, in which forecasts of the average flock weights are uncertain. The proposed approaches are applied to a real case study in Nova Scotia (Canada). Numerical results show that, compared to the weighted models, the min-max models considerably reduce the maximum expected deviation from optimality without significantly increasing the gross deviation from production targets. Furthermore, the stochastic models led to substantial improvements over the deterministic ones, thus justifying the transition to a two-stage planning procedure. The proposed stochastic min-max model was also shown to outperform the current manual approach in terms of reducing the average weight spread between flocks collected on the same day.
Community-based management of natural resources (CBNRM) is a priority in Mozambique's policy on forestry and wildlife resources. In essence the government's policy is to manage the natural resources in partner...
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Community-based management of natural resources (CBNRM) is a priority in Mozambique's policy on forestry and wildlife resources. In essence the government's policy is to manage the natural resources in partnership with the rural communities and the private sector. This represents a change in policy in the agricultural and natural resources sectors, and has potential for significant impact in economic development. This paper demonstrates the potential for employing goal programming as a planning tool in participatory natural resource management in Mozambique. The focus is on the miombo woodlands, which are the main natural forest resources in the country and which most of the local communities, the forestry and tourist industries depend on for a variety of forest products and services. (C) 2001 Elsevier Science B.V. All rights reserved.
In choosing the best alternative with respect to multiple criteria, one approach is to estimate the criterion weights that influence the preferences of all the alternatives presented. When decision makers are able to ...
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In choosing the best alternative with respect to multiple criteria, one approach is to estimate the criterion weights that influence the preferences of all the alternatives presented. When decision makers are able to make preference judgements based on the alternative as a whole, preference decomposition methods attempt to determine part-worths which represent the contribution of the criterion levels to the overall preference values. For each alternative, the sum of the part-worths estimates its overall preference value;In this work, we use linear goal programming to determine the part-worths of all the criterion levels. Simulation experiments are conducted to compare the performances of linear goal programming and ordinary least squares regression in preference decomposition and to examine the effectiveness of including constraints on the parameters. Our simulated results suggest that the linear goal programming model with constrained parameters has better predictive power.
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