West Bengal, India

West Bengal, India
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Khan I.,Chandrakon Vidyasagar Mahavidyalaya | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Communications in Computer and Information Science | Year: 2017

In this paper combining the features of swap sequence and swap operation based Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and K-Opt operation a hybrid algorithm is proposed to solve well known Traveling Salesman Problem (TSP). Interchange of two cities of a path of a TSP is known as swap operation and a sequence of such operations is called swap sequence. Using swap operation and swap sequence PSO operations are redefined to solve TSP. Here ACO is used a small number of iterations to generate initial swarm of PSO. Then PSO operations are made on this swarm a sufficient number of times to find optimal path. During PSO iterations if a particle does not change its position for a predefined number of iterations then K-Opt operation is made on it a finite number of times to improve its position. The algorithm is tested with bench mark test problems from TSPLIB and it is observed that algorithm is more efficient with respect to accuracy as well as execution time to solve standard TSPs (Symmetric as well as Asymmetric) compared to existing algorithms. Details of the proposed algorithm along with swap operation, swap sequence and K-opt operation for the algorithm are elaborately discussed for the readers. © Springer Nature Singapore Pte Ltd. 2017.

Pramanik P.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Applied Soft Computing Journal | Year: 2017

In this paper, an economic ordered quantity (EOQ) model, specifically for a newly launched product has been developed with selling price, customers’ credit period and customers’ credit amount induced demand under three levels of partial trade credit policy, where a supplier, a wholesaler and a retailer offer some credit periods on some fraction of the total purchased amount to the wholesaler, the retailer and the customer respectively. Also, here it is assumed that the retailer obtained a quantity discount from the wholesaler on purchased units above a certain level. In addition, the wholesaler and the retailer both enjoy freight charge discount according to the ordered quantity. Retailer introduces a promotional cost to increase the base demand of the item. Objective of this investigation is to maximize the profit of the retailer as well as the wholesaler. It is established that if the wholesaler contributes some portion of the promotional cost then individual profits as well as the joint profit increases. Due to the uncertainty and vagueness of different inventory costs, the proposed model is also discussed in fuzzy and rough environments. Combining the features of particle swarm optimization (PSO) and simulated annealing (SA) a hybrid algorithm named Particle Swarm-Simulated Annealing (PSSA) is developed to find the most appropriate strategies for the proposed model. Efficiency of this algorithm is tested and compared with PSO and genetic algorithm (GA) for a set of benchmark test problems. The model is illustrated with numerical examples and some managerial insights are outlined. © 2017 Elsevier B.V.

Pramanik P.,Vidyasagar University | Maiti M.K.,Mahishadal Raj College | Maiti M.,Vidyasagar University
Computers and Industrial Engineering | Year: 2017

In this paper, an integrated supply chain model has been developed under three level trade credit policy with price, credit period and credit amount dependent demand, where a supplier offers a credit period to his/her wholesaler to boost the demand of the item. Due to this facility, wholesaler also offers a credit period to his/her retailer and the same practice is followed by the retailer to increase the base demand of the item. Here it is assumed that, both wholesaler and retailer enjoy the same full credit facility but retailer just offers the partial trade credit to his/her customers. The main purpose of this paper is to maximize the joint profit of the wholesaler and the retailer. Model is formulated in crisp, fuzzy and rough environments. Here, a Particle Swarm Optimization (PSO) algorithm is used to find marketing decision for the proposed models. For fuzzy model, credibility measure of fuzzy event and for rough model, trust measure of rough event are used to compare the corresponding objectives for PSO. Models are illustrated with numerical examples and some parametric studies are performed. © 2017 Elsevier Ltd

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.

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.