Browsing by Author "Aygün, Nazli"

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Citation - Scopus: 1
Customer Order Scheduling in Hybrid Flow Shop Manufacturing System
(Springer Science and Business Media Deutschland GmbH, 2021) Eylül Kacar; Esra Karakoç; İrem Kartop; Almira Öztürk; Görkem Bozkurt; Nazlı Karatas Aygün; Erdinc Oner; Kartop, İrem; Kacar, Eylül; Bozkurt, Görkem; Aygün, Nazli; Öner, Erdinç; Karakoç, Esra; Öztürk, Almira; N.M. Durakbasa , M.G. Gençyılmaz
The problem of customer order scheduling in a paint company that has five production stages is handled in this study. The production stages are pre-mixing grinding sub-addition quality control and filling. At each of these stages several identical and unrelated parallel machines are available. Customer orders are assigned to exactly one machine at each stage and do not have to be processed in all stages. As a result of the comprehensive literature review our problem is categorized as hybrid flow shop scheduling for the production system of the company. The mathematical model is developed by utilizing the existing studies in the literature. This developed mathematical model is solved and optimal results are obtained for small-size problem instances. According to the analysis of the results generated by the mathematical model and the literature the problem is found to be NP-hard. Since the problem is NP-hard a heuristic algorithm is proposed for the solution of larger job sizes. Considering its convenience and applications in the scheduling literature GA is selected as a heuristic algorithm to solve our proposed model. Utilizing a genetic algorithm jobs are sorted and assigned to the proper machines to minimize the sum of earliness and tardiness. As a novel approach a user-friendly DSS is designed in addition to efficient scheduling. The designed DSS targets to respond to changes made by the user instantly. © 2020 Elsevier B.V. All rights reserved.