Browsing by Author "Baykasoglu, Adil"

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Greedy randomized adaptive search for dynamic flexible job-shop scheduling
(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2020) Baykasoglu, Adil; Madenoglu, Fatma S.; Hamzadayi, Alper; 0000-0002-5577-4471; 0000-0002-4952-7239; AGÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü
Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.
Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive maintenance activities in dynamic flexible job shops
(SPRINGERONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES, 2021) Baykasoglu, Adil; Madenoglu, Fatma S.; AGÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü; Madenoglu, Fatma S.
In the present study, we proposed a greedy randomized adaptive search procedure (GRASP) for integrated scheduling of dynamic flexible job shops with a novel preventive maintenance policy. In most of the real-life scheduling practices, unexpected and unknown events occur frequently, which necessitates solving operations and maintenance scheduling problems dynamically. Dynamic events like new order arrival, machine breakdown, changes in due date, order cancellation, and urgent order are considered in this study. Moreover, order acceptance/rejection decisions and an order review release mechanism are also taken into account in order to enhance the overall performance by adjusting capacity regarding to customer due date requirements. Four objectives namely mean tardiness, schedule instability, makespan, and mean flow time are considered within a lexicographic programming logic. Random test instances are generated for the stated dynamic scheduling problem. In order to confirm the applicability of the proposed GRASP-based algorithm, extensive experiments were also conducted on well-known job shop scheduling benchmark instances and flexible job shop scheduling benchmark instances with preventive maintenance activities. Computational experiments conducted under various experimental settings such as flexibility level and due date tightness in addition to different preventive maintenance policies. To the best of our knowledge, the present study presents the first attempt through GRASP for simultaneous dynamic scheduling of operations and preventive maintenance activities in flexible job shops. Results of the extensive computational experiments demonstrate that simultaneous scheduling of manufacturing operations and preventive maintenance activities is a viable and effective approach for performance improvement in dynamic flexible job shop scheduling environments.