Debjani Chakraborti

Using genetic algorithm for solving quadratic bilevel programming problems via fuzzy goal programming

This article presents how genetic algorithm (GA) can be efficiently used to fuzzy goal programming (FGP) formulation of quadratic bilevel programming problems (QBLPPs) in a hierarchical decision system. In the proposed approach, the concept of tolerance membership functions in fuzzy sets for measuring the achievement of highest membership value (unity) of the defined fuzzy goals of a problem to the extent possible by minimising the under-deviational variables of the defined membership goals on the basis of priorities of achieving the fuzzy goals is considered. In the decision making process, the sensitivity analysis with variations of priority structure of the goals is performed and then the notion of Euclidean distance function is used to identify the appropriate priority structure under which the most satisfactory decision can be reached in the fuzzy decision environment. The potential use of the approach is illustrated by a numerical example.

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.

A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system

This paper presents how the fuzzy goal programming (FGP) and interval valued goal programming (IVGP) can be efficiently used for modelling and solving land allocation problems for optimal production of seasonal crops in agricultural system.

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 genetic algorithm based stochastic simulation approach to chance constrained interval valued multiobjective decision making problems

This article presents how the stochastic simulation through genetic algorithm (GA) can be used to modeling and solving chance constrained interval valued multiobjective decision making (MODM) problems. In the proposed method, a stochastic simulation approach to the chance constraints is employed for interval valued goal representation of the objectives as well as decision identification through the use of an GA method in an inexact decision making context. In the executable goal programming (GP) model of the problem, both the aspects of the GP, minsum GP and minmax GP [1], are addressed within goal achievement function for minimizing possible regrets associated with the deviational variables of the defined goals for goal achievement within the target intervals specified in the decision making environment. A numerical example is solved and a comparison is made with the conventional GP approach.

A genetic algorithm method to fuzzy goal programming formulation based on penalty function for academic personnel management in university system

This article demonstrates how the GA approach can be efficiently used to the penalty function based fuzzy goal programming (FGP) formulation of academic personnel planning problems in university management system. In the proposed approach, requirement of total full-time teaching staff and allocation of pay-roll budget to each of academic departments are fuzzily described. The recruitment of minimum number of teaching and non-teaching staff and maintaining of certain ratios of part-time teaching staff and non-teaching staff individually with full-time teaching staff, and a ratio of total number of students with total teaching staff in each department for smooth functioning of the academic activities of the departments are considered as constraints in the academic planning horizon. In the model formulation of the problem, the concept of penalty functions for measuring the degree of achievement of membership goals in different ranges for the defined fuzzy goals and thereby arriving at a satisfactory decision is considered. In the solution process, an GA scheme is employed in an iterative manner to achieve the fuzzy goals on the basis of their assigned priorities in the decision making environment. A case example of the University of Kalyani, West Bengal (W.B), India is considered to illustrate the potential use of the approach.

A genetic algorithm based fuzzy goal programming solution approach to chance constrained bilevel programming problems

In this article, a genetic algorithm (GA) based fuzzy goal programming (FGP) procedure for solving bilevel programming problems (BLPPs) having the chance constraints in large hierarchical decision problems is presented.

Bio-inspired Computation Technique to Chance Constrained Fuzzy Goal Programming Model for Resource Allocation in Farm Planning

In the proposed approach, utilization of total cultivable land, different productive resources, achievement of target levels of the production of seasonal crops and expected profit from the farm are fuzzily described. In the model formulation of the problem, the concept of tolerance membership functions in fuzzy sets for measuring the degree of optimality of crops-production by utilizing the productive resources is considered. In the solution process, achievement of the defined membership goals to the highest degree (unity) to the extent possible on the basis of priorities is determined by employing genetic algorithm (GA) scheme in the decision making environment.

A genetic algorithm based goal programming approach to Interval-valued multilevel programming problems

This article presents a goal programming (GP) procedure for solving Interval-valued multilevel programming (IVMLP) problems by using genetic algorithm (GA) in a large hierarchical decision making and planning organization. In the proposed approach, first the individual best and least solutions of the objectives of the decision makers (DMs) located at different hierarchical decision levels are determined by using an GA method. Then, the target interval for achievement of each of the objectives as well as the target interval of the decision vector controlled by the upper-level DM is defined in the inexact decision making environment. In the model formulation of the problem, first the interval valued objectives and control vectors are transformed into the conventional form of goal by using interval arithmetic technique and then introducing under- and over-deviational variables to each of them. In the solution process, both the aspects of minsum and minmax GP formulation are adopted to minimize the lower bounds of the regret intervals for goal achievement within the specified interval from the optimistic point of view and thereby distribution of proper decision powers to the DMs of the hierarchical levels. The potential use of the approach is illustrated by a numerical example.

A genetic algorithm approach to fuzzy quadratic bilevel programming

This article presents a genetic algorithm (GA) based fuzzy goal programming (FGP) procedure for modelling and solving quadratic bilevel programming problems (QBLPPs) of a hierarchical decision organization. In the FGP model formulation, the concept of tolerance membership functions for measuring the degree of satisfaction of the decision makers (DMs) regarding goal achievement of the fuzzy objectives and the degree of optimality of the decision vector controlled by the upper-level DM are defined in the decision making horizon. In the goal achievement function of the model, minimization of the under-deviations of the defined membership goals from the highest membership value (unity) on the basis of the assigned priorities is considered. In the solution process, sensitivity analysis on different priority structures by using an GA scheme is made to reach an ideal-point dependent solution in the decision environment. The potential use of the approach is illustrated by a numerical example.