A Novel ICA-GA Algorithm for Solving Multiobjective Optimization problems

Abstract

The Master Production Schedule (MPS), which connects effective utilization of production resources to customer service, is a "crucial interface between marketing and manufacturing". One of the main issues with operation is mismanagement of the (MPS), which has the ability to lower customer satisfaction. This work presents a hybrid evolutionary algorithm (ICA-GA) for solving multiobjective (MPS) problems that combines best features of the genetic algorithm (GA) and the imperialist competitive algorithm (ICA). The colonies of each empire are represented by a small population in the algorithm that is being given, and they interact with one another through genetic operators. The numerical results of the (ICA-GA) algorithm, which was tested on five production scenarios, demonstrate the effectiveness and potential of the hybrid algorithm in locating the best solutions.When compared to the outcomes of (GA) and (SA) in all production situations, the (ICA-GA) solutions result in a lower inventory level, maintain a high level of customer satisfaction, and require less overtime.