Nisa SaracalıoğluYiğit SonbaharGüner Asrın AkdenizMeryem ÜnsalYarkın Yiğit YıldızSalih Kaan ÇakmakçıÖnder BulutSinem ÖzkanCeylin HökenekN.M. Durakbasa , M.G. Gençyılmaz2025-10-0620229789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 978981974805121954364, 2195435610.1007/978-3-030-90421-0_65https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119880694&doi=10.1007%2F978-3-030-90421-0_65&partnerID=40&md5=586f5091b83df39044eedb6c8135f66dhttps://gcris.yasar.edu.tr/handle/123456789/8851In this study we consider an Assemble-to-Order (ATO) system with multiple components common machines and multiple customer classes. We first identify the research problems related to all the above-mentioned system features. Thereafter as the solution methodology we propose different policies for the described problems. We develop a simulation model of the system and benefit from Genetic Algorithm (GA) metaheuristic that finds near-optimal solutions for inventory control and rationing policies. The simulation model of a quite general ATO system that is integrated with a Genetic algorithm provides solutions for several real-life ATO practices. © 2022 Elsevier B.V. All rights reserved.EnglishAssemble-to-order, Common Machine Scheduling, Production-inventory Control Policy, Rationing Policy, Simulation Optimization, Inventory Control, Assemble To Order, Common Machine Scheduling, Inventory Control Policies, Machine Scheduling, Ordering System, Production-inventory Control, Production-inventory Control Policy, Rationing Policy, Simulation Model, Simulation Optimization, Genetic AlgorithmsInventory control, Assemble to order, Common machine scheduling, Inventory control policies, Machine scheduling, Ordering system, Production-inventory control, Production-inventory control policy, Rationing policy, Simulation model, Simulation optimization, Genetic algorithmsConference Object