A production inventory model with fuzzy production and demand using fuzzy differential equation: An interval compared genetic algorithm approach

Authors:
Partha Guchhait;Manas Kumar Maiti;Manoranjan Maiti
Affiliations:
Department of Applied Mathematics, Vidyasagar University, Midnapore, Paschim-Medinipur, West Bengal 721102, India;Department of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, West Bengal 721628, India;Department of Applied Mathematics, Vidyasagar University, Midnapore, Paschim-Medinipur, West Bengal 721102, India
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 17
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Abstract

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. @a-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.