Papun Biswas

An application of genetic algorithm method for solving patrol manpower deployment problems through fuzzy goal programming in traffic management system: a case study

This article demonstrates a fuzzy goal programming (FGP) approach with the use of genetic algorithm (GA) for proper deployment of patrol manpower to various road-segment areas in urban environment in different shifts of a time period to deterring violation of traffic rules and thereby reducing the accident rates in a traffic control planning horizon. To expound the potential use of the approach, a case example of the city Kolkata, West Bengal, INDIA, is solved.

Using Genetic Algorithm to Goal Programming Model of Solving Economic-Environmental Electric Power Generation Problem with Interval-Valued Target Goals

This article presents how genetic algorithm (GA) method can be efficiently used to the goal programming (GP) formulation of Economic-Environmental Power Dispatch (EEPD) problem with target intervals in a power system operation and planning environment.

Using Genetic Algorithm for Solving Linear Multilevel Programming Problems via Fuzzy Goal Programming

This article presents a fuzzy goal programming (FGP) procedure for modeling and solving multilevel programming (MLP) problems by using genetic algorithm (GA) in a large hierarchical decision making system.

A Trilevel Programming Approach to Solve Reactive Power Dispatch Problem Using Genetic Algorithm Based Fuzzy Goal Programming

This article demonstrates how trilevel programming (TLP) in a hierarchical decision structure can be efficiently used for modeling and solving reactive power dispatch (RPD) problems of electrical power system by using genetic algorithms (GAs) in the framework of fuzzy goal programming (FGP) in uncertain environment. In the proposed approach, various objectives associated with a RPD problem are considered at three hierarchical levels in a planning horizon. In the solution process, a GA scheme is employed to obtain the individual values of objectives and thereby to evaluate the developed FGP model to reach a solution for optimal RPD decision. The proposed approach is tested on the standard IEEE 6-Generator 30-Bus System.

Soft Computing Approach to Electrical Transmission Network Congestion Management

In this paper an efficient technique is described for managing the congestion in electric transmission network based on rescheduling the nearby generators and/or shedding some of the loads. To incorporate the uncertainty in the system objectives and parameters, fuzzy environment is considered for the formulation of the problem. In the solution process bio-inspired computational technique, genetic algorithm (GA) is used. The approach is illustrated by standard IEEE 30-bus 6-generator test system.

Modelling Multiobjective Bilevel Programming for Environmental-Economic Power Generation and Dispatch Using Genetic Algorithm

This article describes a multiobjective bilevel programming (MOBLP) model to solve environmental-economic power generation and dispatch (EEPGD) problem through genetic algorithm (GA) based fuzzy goal programming (FGP) in a thermal power plant operational system. In MOBLP approach, first the objectives of problem are divided into two sets of objectives, and they are separately included at two hierarchical decision levels (top-level and bottom-level), where each level contains one or more controls variables associated with power generation decision system. Then, optimization problems of both the levels are described fuzzily to accommodate the impression arises with regard to optimizing them. In FGP model formulation, the membership functions associated with defined fuzzy goals are designed, and then they are converted into membership goals by assigning highest membership value (unity) as achievement level and introducing under- and over-deviational variables to each of them. In achievement function, minimization of under-deviational variables of membership goals according to weights of importance is considered to achieve optimal solution in decision environment. In the process of solving FGP model, a GA scheme is adopted at two stages, direct optimization of individual objectives at the first stage for fuzzy representation of them and, at the second stage, evaluation of goal achievement function to reach optimal power generation decision. The use of the proposed method is demonstrated via IEEE 30-bus system.

Fuzzy Goal Programming Approach for Solving Congestion Management Problem in Electrical Transmission Network Using Genetic Algorithm

This article presents an efficient genetic algorithm (GA) based fuzzy goal programming (FGP) approach to solve Congestion Management (CM) problem in electrical power transmission network by generation rescheduling or load shedding of participating generators and loads. In the proposed approach, FGP and GA are applied at two stages for model formulation and solving the problem of CM. The proposed approach is tested on the standard IEEE 6-Generator 30-Bus System and the model solution is compared with the solution obtained in a previous study.

A Fuzzy Goal Programming Procedure for Solving Multiobjective Load Flow Problems via Genetic Algorithm

This paper describes how the genetic algorithms (GAs) can be efficiently used to fuzzy goal programming (FGP) formulation of optimal power flow problems having multiple objectives. In the proposed approach, the different constraints, various relationships of optimal power flow calculations are fuzzily described.In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first for measuring the degree of achievement of the aspiration levels of the goals specified in the decision making context. Then, the achievement function for minimizing the regret for under‐deviations from the highest membership value (unity) of the defined membership goals to the extent possible on the basis of priorities is constructed for optimal power flow problems.In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision m...