A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

• Published in: Scientific reports Vol. 14; no. 1; pp. 2376 - 22
• Main Authors: Khurshid, Bilal, Maqsood, Shahid, Khurshid, Yahya, Naeem, Khawar, Khalid, Qazi Salman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705/117; Algorithms; Humanities and Social Sciences; Hybridization; Immunoglobulins; multidisciplinary; Objective function; Offspring; Science; Science (multidisciplinary); Variance analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-47729-x

This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.