Comparisons of some improving strategies on NSGA-II for multi-objective inventory system
Soheila Khishtandar, M. Zandieh · Journal of Industrial and Production Engineering · 2016
Different inventory control systems attempt to determine how much and when to order at the least relevant cost, while maintaining a desirable service level for customers. In this paper, a continuous review stochastic inventory system, with three objectives and certain resource constraints is studied. In the model for this system, contrary to the traditional inventory models, customer service is not considered a shortage cost in the objective function. Moreover, the frequency of stock-out occasions and the number of items stocked out annually are to be minimized. For determining the Pareto optimal set, Constrained Multi-Objective Evolutionary Algorithms are used. Constrained Reference-point-based Non-dominated Sorting Genetic Algorithm (C-R-NSGA-II) which integrates decision-makers’ preferences in the optimization process, is compared with the basic algorithm, constrained non-dominated sorting genetic algorithm (C-NSGA-II). Then, the best algorithms for each criterion are presented. Results show that C-R-NSGA-II has good scores for most criteria.