An application of real coded Self-organizing Migrating Genetic Algorithm on a two-warehouse inventory problem with Type-2 interval valued inventory costs via mean bounds optimization technique

Abstract

Hybrid meta-heuristic algorithms play significant role for solving highly non-linear and non-convex optimization problems in uncertain environment because any hybrid algorithm is more efficient and robust as well as takes less computational cost than the basic algorithms. The objective of this work is to implement hybrid real coded Self-organizing Migrating Genetic Algorithm (RCSOMGA) in inventory control problems with Type-2 interval uncertainty. To achieve this goal, in this work, a two-warehouse inventory problem is modelled for deteriorating commodities under preservation technology with Type-2 interval valued inventory costs. Then the corresponding Type-2 interval valued average profit of the proposed model is obtained using interval mathematics. Thereafter, using Type-2 interval order relations, mean bounds optimization technique is established to maximize the average profit. To illustrate the proposed model, six numerical examples are considered and solved by the hybrid algorithm RCSOMGA. Also, the same numerical examples are solved by some of the state-of-the-art meta-heuristic algorithms and the obtained results are compared with the results obtained by RCSOMGA. Then ANOVA and non-parametric statistical tests are carried out to show the significance of hybrid RCSOMGA. Numerical comparison and statistical analysis of the computational results demonstrate that hybrid RCSOMGA outperforms than other existing algorithms. Finally, sensitivity analyses are carried out to establish the impacts of the inventory parameters on the optimal policy of the proposed model and the work is concluded with some managerial insights.