Abstract
The dynamic lot sizing plays an important role when the demand of an item varies with time. This paper addresses a dynamic lot sizing problem (DLSP) with capacity constraint and discount price structure. Although the well-known dynamic programming (DP) of Wagner-Within is capable of providing an optimal solution for single stage lot sizing problems, it suffers from its high computational complexity. This limits the use of DP in practical problems that are generally larger in size. Two meta-heuristics, genetic algorithm (GA) and Hopfield neural network (HNN) are designed for DLSP to get best trade-off between solution quality and computational time. The DP algorithm is modeled to derive the optimal solution to the experimental problem. The optimality of GA and HNN are tested by comparing the percentage deviation of GA and HNN results against the optimal solution derived using DP.
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