West Bengal, India
West Bengal, India

Time filter

Source Type

Ghoshal N.,Mahishadal Raj College | Mukhopadhyay K.,Sundarban Mahavidyalaya | Roy S.K.,Jadavpur University
Liquid Crystals | Year: 2012

Monte Carlo simulation performed on a lattice system of biaxial molecules possessing D 2h symmetry and interacting with a second rank anisotropic dispersion potential yields three distinct macroscopic phases depending on the biaxiality of the constituent molecules. The phase diagram of such a system as a function of molecular biaxiality is greatly modified when a transverse dipole is considered to be associated with each molecule so that the symmetry is reduced to C 2v. Our results indicate the splitting of the Landau point, i.e. the point in the phase diagram where a direct transition from the isotropic phase to the biaxial nematic phase occurs, into a Landau line for a system of biaxial molecules with strong transverse dipoles. The width of the Landau line becomes maximum for an optimal value of the relative dipolar strength. The presence of transverse dipoles leads to the stabilization of the thermotropic biaxial nematic phase at higher temperature and for a range of values of molecular biaxiality. The structural properties in the uniaxial and biaxial phases are investigated by evaluating the first rank and second rank orientational correlation functions. The dipole-induced long-range order of the anti-ferroelectric structure in the biaxial nematic phase, is revealed. © 2012 Copyright Taylor and Francis Group, LLC.


Guchhait P.,Dherua Anchal Satabala High School | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Industrial Engineering | Year: 2010

Items made of glass, ceramic, etc. are normally stored in stacks and get damaged during the storage due to the accumulated stress of heaped stock. These items are known as breakable items. Here a multi-item inventory model of breakable items is developed, where demands of the items are stock dependent, breakability rates increase linearly with stock and nonlinearly with time. Due to non-linearity and complexity of the problem, the model is solved numerically and final decisions are made using Genetic Algorithm (GA). In a particular case, model is solved analytically as well as numerically and results are compared. Models are developed with both crisp and uncertain inventory costs. For uncertain inventory costs both fuzzy and stochastic parameters are considered. A chance constrained approach is followed to deal with simultaneous presence of stochastic and fuzzy parameters. Different numerical examples are used to illustrate the problem for different cases. © 2010 Elsevier Ltd. All rights reserved.


Bera U.K.,National Institute of Technology Agartala | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Mathematics with Applications | Year: 2012

The real-world inventory control problems are normally imprecisely defined and human interventions are often required in solving these decision-making problems. In this paper, a realistic inventory problem with an infinite rate of replenishment over a prescribed finite but imprecise time horizon is formulated considering time dependent ramp type demand, which increases with time. Lead time is also assumed as fuzzy in nature. Shortages are allowed and backlogged partially. Two models are considered depending upon the ordering policies of the decision maker (DM). The imprecise parameters are first transformed to corresponding nearest interval numbers depending upon some distance metric on fuzzy numbers and then following the interval mathematics, the objective function for total profit from the planning horizon is obtained (which is an interval function). Then interval objective decision making problem is reduced to multi-objective problems using different approaches. Finally a fast and elitist multi-objective genetic algorithm (FEMOGA) is used for solving these multi-objective models to find pareto-optimal decisions for the DM. The models are illustrated numerically. As a particular case, the results due to linear trended and constant demands have been presented. © 2012 Elsevier Ltd. All rights reserved.


Giri P.K.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Industrial Engineering | Year: 2014

A fixed charge fuzzy stochastic solid transportation problem (FCFSSTP) is formulated with random budget and time constraints, random sources, demands and capacities of conveyances. Fuzzy stochastic constraints involving the symbols ‘⩾̃’ (approximately or fuzzily greater than or equal to) and ‘⩽̃’ (approximately or fuzzily less than or equal to) are used and appropriately transformed to deterministic ones. Fuzzy goal programming (FGP) approach is applied to solve the said FCFSSTP under several constraints. This paper also presents additive FGP models for the FCFSSTP. This method aggregates the membership functions of the stochastic constraints with the help of crisp and fuzzy weights based on importance of the objectives to construct the relevant decision function. From this general formulation, different particular models can be derived. As an example, one particular model with two fuzzy-stochastic constraints has been formulated. Moreover, as a particular case, three dimensional representation of an existing model is also presented. Transformed deterministic models are derived and solved by a gradient based non-linear optimization method-Generalized Reduced Gradient (GRG) technique. Two dimensional (with single conveyance) representation of a proposed FCFSSTP is derived and solved numerically. The optimum results of this model are compared with the solid transportation model. The suggested models and approaches are illustrated by a real-life practical problem. © 2014 Elsevier Ltd


Giri P.K.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Applied Soft Computing Journal | Year: 2015

This paper presents fully fuzzy fixed charge multi-item solid transportation problems (FFFCMISTPs), in which direct costs, fixed charges, supplies, demands, conveyance capacities and transported quantities (decision variables) are fuzzy in nature. Objective is to minimize the total fuzzy cost under fuzzy decision variables. In this paper, some approaches are proposed to find the fully fuzzy transported amounts for a fuzzy solid transportation problem (FSTP). Proposed approaches are applicable for both balanced and unbalanced FFFCMISTPs. Another fuzzy fixed charge multi-item solid transportation problem (FFCMISTP) in which transported amounts (decision variables) are not fuzzy is also presented and solved by some other techniques. The models are illustrated with numerical examples and nature of the solutions is discussed. © 2014 Elsevier B.V. All rights reserved.


Khanra A.,Bohichberia High School H.S. | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Industrial Engineering | Year: 2015

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company's product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one's result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented. © 2015 Published by Elsevier Ltd.


Guchhait P.,Vidyasagar University | Kumar Maiti M.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Engineering Applications of Artificial Intelligence | Year: 2013

In this paper, a production inventory model, specially for a newly launched product, is developed incorporating fuzzy production rate in an imperfect production process. Produced defective units are repaired and are sold as fresh units. It is assumed that demand coefficients and lifetime of the product are also fuzzy in nature. To boost the demand, manufacturer offers a fixed price discount period at the beginning of each cycle. Demand also depends on unit selling price. As production rate and demand are fuzzy, the model is formulated using fuzzy differential equation and the corresponding inventory costs and components are calculated using fuzzy Riemann-integration. α-cut of total profit from the planning horizon is obtained. A modified Genetic Algorithm (GA) with varying population size is used to optimize the profit function. Fuzzy preference ordering (FPO) on intervals is used to compare the intervals in determining fitness of a solution. This algorithm is named as Interval Compared Genetic Algorithm (ICGA). The present model is also solved using real coded GA (RCGA) and Multi-objective GA (MOGA). Another approach of interval comparison-order relations of intervals (ORI) for maximization problems is also used with all the above heuristics to solve the model and results are compared with those are obtained using FPO on intervals. Numerical examples are used to illustrate the model as well as to compare the efficiency of different approaches for solving the model. © 2012 Elsevier Ltd.


Guchhait P.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Industrial Engineering | Year: 2014

In this paper, an inventory model of a deteriorating item with stock and selling price dependent demand under two-level credit period has been developed. Here, the retailer enjoys a price discount if he pays normal purchase cost on or before the first level of credit period, or an interest is charged for the delay of payments. In return, retailer also offers a fixed credit period to his customers to boost the demand. In this regard, the authors develop an EOQ model incorporating the effect of inflation and time value of money over all the costs. Keeping the business of seasonal products in mind, it is assumed that planning horizon of business is random and follows a normal distribution with a known mean and standard deviation. The model is formulated as retailer's profit maximization problem for both crisp and fuzzy inventory costs and solved using a modified Genetic Algorithm (MGA). This algorithm is developed following fuzzy age based selection process for crossover and gradually reducing mutation parameter. For different values of MGA parameters, optimum results are obtained. Numerical experiments are performed to illustrate the model. © 2014 Elsevier Ltd. All rights reserved.


Guchhait P.,Vidyasagar University | Kumar Maiti M.,Mahishadal Raj College | Maiti M.,Vidyasagar University
International Journal of Production Economics | Year: 2013

In this paper, economic production quantity (EPQ) models for breakable or deteriorating item are developed with variable demands, being dependent on time or on-hand stock. Here rate of production and holding cost are time dependent, unit production cost is a function of both production reliability indicator and production rate. Set-up cost is also partially production rate dependent. The production process produces some imperfect quantities which are instantly reworked at a cost to bring back those units to the perfect ones. The production process ultimately depends on both time and reliability indicator. The models are formulated as optimal control problems and the total profit functions with effect of inflation and time-value of money are expressed as finite integrals over the finite planning horizon. The problems are solved using Euler-Lagrange function based on variational calculus and Newton-Raphson method to determine the optimal production reliability indicator (r) and then corresponding production rates and total profits. In some cases, results of the models for deteriorating item are obtained as particular cases from those of breakable item models. Similarly, results of simple EPQ models (without damageability) are deduced as particular cases. Numerical experiments are performed to illustrate the models both numerically and graphically. © 2013 Elsevier B.V. All rights reserved.


Guchhait P.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Applied Soft Computing Journal | Year: 2013

In this paper, a two-warehouse inventory model for deteriorating item with stock and selling price dependent demand has been developed. Above a certain (fixed) ordered label, supplier provides full permissible delay in payment per order to attract more customers. But an interest is charged by the supplier if payment is made after the said delay period. The supplier also offers a partial permissible delay in payment even if the order quantity is less than the fixed ordered label. For display of goods, retailer has one warehouse of finite capacity at the heart of the market place and another warehouse of infinite capacity (that means capacity of second warehouse is sufficiently large) situated outside the market but near to first warehouse. Units are continuously transferred from second warehouse to first and sold from first warehouse. Combining the features of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) a hybrid heuristic (named Particle Swarm-Genetic Algorithm (PSGA)) is developed and used to find solution of the proposed model. To test the efficiency of the proposed algorithm, models are also solved using another two established heuristic techniques and results are compared with those obtained using proposed PSGA. Here order quantity, refilling point at first warehouse and mark-up of selling price of fresh units are decision variables. Models are formulated for both crisp and fuzzy inventory parameters and illustrated with numerical examples. © 2012 Elsevier B.V. All rights reserved.

Loading Mahishadal Raj College collaborators
Loading Mahishadal Raj College collaborators