Multi-objective optimization of mixture inventory system experiencing order crossover

This paper addresses the inventory problem under order crossover. Order crossover occurs when orders do not arrive in same order in which they were issued. In this work, order crossover phenomenon is examined in a multi-objective mixture inventory system. Shortages in the model are considered as a combination of backorders and lost sales. Multi-objective cuckoo search (MOCS) algorithm is used to solve the inventory problem and generate Pareto curve for practitioners. A numerical problem is shown to demonstrate the results. The results show a remarkable reduction in inventory cost and a significant rise in service levels with proposed inventory system considering order crossover in comparison to existing inventory systems that ignore order crossover. Proposed multi-objective inventory system with order crossover is more sustainable in comparison to existing inventory systems. The performance of MOCS algorithm is compared with two high performing evolutionary algorithms such as non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. A benchmark problem is considered for comparison.