Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling

Abstract

Production scheduling is a strategic process that organizes the execution of jobs on available resources to optimize specific objectives. One significant scheduling challenge is the Cost-based Hybrid Flow Shop (CHFS) problem, which involves optimizing job scheduling across multiple stages to minimize scheduling-related costs. However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO) algorithm for CHFS. Unlike previous studies that focus on isolated cost factors, this research formulated an integrated mathematical model for CHF holistically capturing labor, energy consumption, maintenance, and late penalty costs. The GTLBO algorithm incorporates a unique hybrid initialization strategy, generating 10 % of the initial population using a Greedy algorithm to enhance exploration efficiency. The performance of GTLBO was evaluated through computational experiments involving 12 test instances, with comparative algorithms included for analysis. Results from the Wilcoxon rank-sum test indicated a significant difference between the outputs of GTLBO and other algorithms, with GTLBO outperforming the comparative algorithms in 75 % of the test instances. Additionally, the case study validation showed that GTLBO can reduce costs by 0.23 % to 4.31 % compared to other algorithms. This research offers valuable insights for manufacturers seeking to optimize CHFS scheduling to reduce production expenses.