Önder BulutM. Fatih TasgetirenMehmet Murat Fadiloglu2025-10-0620119789819698936, 9789819698042, 9789819698110, 9789819698905, 9789819512324, 9783032026019, 9783032008909, 9783031915802, 9789819698141, 978303198413616113349, 0302974310.1007/978-3-642-25944-9_8https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855665274&doi=10.1007%2F978-3-642-25944-9_8&partnerID=40&md5=036d6a2e203c425f2432edd97dc4c6a5https://gcris.yasar.edu.tr/handle/123456789/10213In this study we propose a genetic algorithm (GA) for the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. The proposed GA employs a multi-chromosome solution representation to encode PoT multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. Furthermore a variable neighborhood search (VNS) algorithm is also fused into GA to further enhance the solution quality. The experimental results show that the proposed GA is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy. © 2012 Springer-Verlag. © 2012 Elsevier B.V. All rights reserved.EnglishEconomic Lot Scheduling Problem, Extended Basic Period, Genetic Algorithm, Power-of-two Policy, Variable Neighborhood Search, Constraint Handling, Economic Lot Scheduling Problems, Extended Basic Period, Power-of-two, Power-of-two Policies, Solution Quality, Solution Representation, Variable Neighborhood Search, Computation Theory, Intelligent Computing, Operations Research, Genetic AlgorithmsConstraint handling, Economic lot scheduling problems, extended basic period, Power-of-two, Power-of-two policies, Solution quality, Solution representation, Variable neighborhood search, Computation theory, Intelligent computing, Operations research, Genetic algorithmsConference Object