Minimizing Number of Tardy Jobs in Flow Shop Scheduling Using A Hybrid Whale Optimization Algorithm

Abstract

Abstract The number of Tardy Jobs is a critical performance in scheduling because it can increase customer trust in the company. If the number of tardy jobs is large, trust in the company decreases. This article aims to minimize the number of tardy jobs in Permutation Flow Shop Scheduling Problems (PFSSP) using Hybrid Whale Optimization Algorithm (HWOA). We propose the HWOA method for minimizing the number of tardy jobs in PFSSP. There are 5 phases of the proposed HWOA algorithm. Phase 1 is replacing 1 of the initial search agent population replaced by the NEH-EDD heuristic procedure. The Random Search Agent position’s initialization and the Large Rank Value (LRV) is the second phase. Phase 3 is the whale exploration phase. Phase 4 is the whale exploitation phase. The last phase (5) is the local search exchange procedure. Numerical experiments were conducted to test the performance of HWOA, and it was compared with the previous research algorithm. Algorithm experiment results show that the HWOA algorithm has better performance than Particle Swarm Optimization and Genetic Algorithms.