Ali Ozan ÖzkulGamze KüçükCeren ÇelikNevzatcem ÖztunaMert DemirkanEzgi Çağlar NizamYıldırımşan BüyükmertoğluDamla YükselAyhan Özgür ToyNizam, Ezgi ÇağlarDemirkan, MertKüçük, GamzeÖztuna, NevzatcemÖzkul, Ali OzanToy, Ayhan ÖzgürÇelik, CerenN.M. Durakbasa , M.G. Gençyılmaz2025-10-0620219789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 9789819748051978303062783621954364, 219543562195-435610.1007/978-3-030-62784-3_682-s2.0-85096491434https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096491434&doi=10.1007%2F978-3-030-62784-3_68&partnerID=40&md5=32739a92c1b2c6e4a4dc0f89e3eecfb4https://gcris.yasar.edu.tr/handle/123456789/9107https://doi.org/10.1007/978-3-030-62784-3_68In this study we consider the flexible job-shop scheduling problem at a local metal sheet processing company. We aim to develop a model and an algorithm to generate a weekly production plan for the company. The objective is to minimize the makespan while meeting the demands of products for a given planning horizon. First we provide an LP formulation of this problem. The computational complexity of the problem is NP-hard hence the input data prohibits obtaining the optimal solution in a reasonable time. Therefore we implement a metaheuristic and several rule-based heuristics. These are Genetic Algorithm Giffler and Thompson’s Algorithm and three other Rule-Based Heuristic Algorithms that we developed. We first test our model and heuristics over a set of sample instances then we solve for the real data. Our experimental study indicates that one of the rule-based heuristics we developed outperforms others in most of the instances. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDecision Support System, Flexible Job-shop Scheduling, Genetic Algorithm, Giffler And Thompson’s Algorithm, Meta-heuristics, OptimizationGenetic AlgorithmGiffler and Thompson’s AlgorithmFlexible Job-Shop SchedulingOptimizationMeta-heuristicsDecision Support SystemConference Object