Computational Intelligence Approach on a Deterministic Production-Inventory Control Model with Shortages

Abstract

Here, an attempt has been made to determine an optimal solution of a deterministic production-inventory model that consists of single deteriorating items and a constant rate of deterioration. In this proposed production-inventory model, lead time is taken to be negligible and demand rate is a ramp type function of time. Shortages are allowed and partially backlogged. During this shortage period, the backlogging rate is a variable which depends on the length of the waiting time over the replenishment period. Mathematical formulation of the problem highlighted the model as a complex nonlinear constrained optimization problem. Considering the complexities towards solution, modified real-coded genetic algorithms (elitist modified real coded genetic algorithm [MRCGA]) with ranking selection, whole arithmetic crossover, and nonuniform mutation on the age of the population has been developed. The proposed production-inventory model has been solved via MRCGA and simulated annealing and as well as standard optimization methods. Finally, the results are embedded with numerical example and sensitivity analysis of the optimal solution with respect to the different parameters of the system is carried out.