Multi-item fuzzy EOQ models using genetic algorithm
Computers and Industrial Engineering
A genetic algorithm for solving economic lot size scheduling problem
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Multi-item fuzzy inventory model with three constraints: genetic algorithm approach
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Genetic algorithm for inventory lot-sizing with supplier selection under fuzzy demand and costs
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
IEEE Transactions on Evolutionary Computation
Computers & Mathematics with Applications
Engineering Applications of Artificial Intelligence
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The purpose of this research is to solve the mixed integer constrained optimization problem with interval coefficient by a real-coded genetic algorithm (RCGA) with ranking selection, whole arithmetical crossover and non-uniform mutation for non-integer decision variables. In the ranking selection, as well as in finding the best solution in each generation of RCGA, recently developed modified definitions of order relations between interval numbers with respect to decision-making are used. Also, for integer decision variables, new types of crossover and mutation are introduced. This methodology is applied to solve a finite time horizon inventory model with constant lead-time, uniform demand rate and a discount by paying an amount of money in advance. Moreover, different inventory costs are considered to be interval valued. According to the consumption of items during lead-time and reorder level, two cases may arise. For each case, the mathematical model becomes a constrained nonlinear mixed integer problem with interval objective. Our objective is to determine the optimal number of cycles in the finite time horizon, lot-size in each cycle and optimal profit. The model is illustrated with some numerical examples and sensitivity analysis has been done graphically with the variation of different inventory parameters.