Multi-reservoir operation planning is a complex task involving many variables, objectives, and decisions. This paper applies a hybrid method using genetic algorithm (GA) and linear programming (LP) developed by the authors to determine operational decisions for a reservoir system over the...

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management areimperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater...

Energy saving devices are installed in rural areas of many developing countries with Government subsidy. A linear programming approach for the installation of these devices is described, either to maximize the energy saved for a given subsidy amount or to find the minimum subsidy amount to...

Purpose – The purpose of this paper is to prove the effectiveness of minimum semi-absolute deviations (MSAD) method in dynamic portfolio investment. Design/methodology/approach – In financial investment, the classical static portfolio theory of Markowitz type lacks the dynamic adaptability...

This paper proposes a procedure for an effective investment allocation: a Linear Programming Model using a Social Accounting Matrix (SAM). The Gross Regional Products (GRP) is the objective function. Several constraints, such as job creation for different levels of human force, income...

A Markov decision process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagent problems in which the value of a decision must be evaluated according to several viewpoints, sometimes...

This note shows how the linear programs needed to compute cost and revenue functions under constant returns to scale and a single output or input, respectively, can be replaced with a more efficient enumeration algorithm. A numerical example illustrates this algorithm

In this paper, we propose a two-stage variable selection procedure for high dimensional quantile varying coefficient models. The proposed method is based on basis function approximation and LASSO-type penalties. We show that the first stage penalized estimator with LASSO penalty reduces the...

The dual simplex algorithm is the method of choice when linear programs have to be reoptimized after adding constraints or fixing variables. In this paper we discuss a modication of the standard dual simplex which allows for taking longer steps when proceeding from one dual feasible solution to...

This paper seeks to highlight two approaches to the solution of stochastic control and optimal stopping problems in continuous time. Each approach transforms the stochastic problem into a deterministic problem. Dynamic programming is a well-established technique that obtains a partial/ordinary...