M. Fatih TasgetirenQuanke PanPonnuthurai Nagaratnam SuganthanAdalet Oner2025-10-0620130307904X0307-904X10.1016/j.apm.2013.02.011https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878203450&doi=10.1016%2Fj.apm.2013.02.011&partnerID=40&md5=e309850bf524a161999dcc56ea818823https://gcris.yasar.edu.tr/handle/123456789/10100In this paper we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly some novel methods of calculating the total tardiness from makespan are introduced for the no-idle permutation flowshop scheduling problem. Finally the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. © 2013 Elsevier Inc. © 2013 Elsevier B.V. All rights reserved.EnglishArtificial Bee Colony Algorithm, Evolutionary Algorithms, Genetic Algorithm, Metaheuristics, No-idle Permutation Flowshop Scheduling Problem, Artificial Bee Colony Algorithms, Competitive Performance, Computational Results, Meta Heuristics, No-idle Permutation Flowshop Scheduling Problems, Permutation Flow Shops, Permutation Flowshop Scheduling Problems, Traditional Genetic Algorithms, Evolutionary Algorithms, Genetic Algorithms, Scheduling, Scheduling Algorithms, Problem SolvingArtificial bee colony algorithms, Competitive performance, Computational results, Meta heuristics, No-idle permutation flowshop scheduling problems, Permutation flow shops, Permutation flowshop scheduling problems, Traditional genetic algorithms, Evolutionary algorithms, Genetic algorithms, Scheduling, Scheduling algorithms, Problem solvingArticle