M. Fatih TasgetirenQuanke PanLing WangAngela Hsiang Ling Chen2025-10-0620119789819698936, 9789819698042, 9789819698110, 9789819698905, 9789819512324, 9783032026019, 9783032008909, 9783031915802, 9789819698141, 978303198413616113349, 0302974310.1007/978-3-642-25944-9_11https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862933509&doi=10.1007%2F978-3-642-25944-9_11&partnerID=40&md5=07d3be54a5ceef88861944e7e93e7d7dhttps://gcris.yasar.edu.tr/handle/123456789/10208In this paper we present a variable iterated greedy (vIGP-DE) algorithm where its parameters (basically destruction size and cooling parameter for the simulated annealing type of acceptance criterion) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented such that first chromosome represents the destruction size and cooling parameter of the iterated greedy algorithm while second chromosome is simply a permutation assigned to each individual in the population randomly. As an application area we choose to solve the no-idle permutation flowshop scheduling problem with the total flowtime criterion. To the best of our knowledge the no-idle permutation flowshop problem hasn't yet been studied thought it's a variant of the well-known permutation flowshop scheduling problem. The performance of the vIGP-DE algorithm is tested on the Taillard's benchmark suite and compared to a very recent variable iterated greedy algorithm from the existing literature. The computational results show its highly competitive performance and ultimately we provide the best known solutions for the total flowtime criterion for the Taillard's benchmark suit. © 2012 Springer-Verlag. © 2012 Elsevier B.V. All rights reserved.EnglishDifferential Evolution Algorithm, Iterated Greedy Algorithm, Local Search, No-idle Permutation Flowshop Scheduling Problem, Acceptance Criteria, Application Area, Benchmark Suites, Computational Results, Cooling Parameters, Differential Evolution Algorithms, Iterated Greedy Algorithm, Local Search, No-idle, Permutation Flow Shops, Permutation Flowshop Scheduling Problems, Solution Representation, Total Flowtime, Benchmarking, Chromosomes, Computation Theory, Intelligent Computing, Parameter Estimation, Simulated Annealing, Evolutionary AlgorithmsAcceptance criteria, Application area, Benchmark suites, Computational results, Cooling parameters, Differential evolution algorithms, Iterated greedy algorithm, Local search, No-idle, Permutation flow shops, Permutation flowshop scheduling problems, Solution representation, Total flowtime, Benchmarking, Chromosomes, Computation theory, Intelligent computing, Parameter estimation, Simulated annealing, Evolutionary algorithmsConference Object