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Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
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Publication Metadata only A coordinated production and shipment model in a supply chain(Elsevier Science Bv, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a deterministic inventory system. In this supply chain, the customers are willing to wait at the expense of a waiting cost. Accordingly, the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time. The supplier produces the items, holds the inventory and ships the products to the retailer to satisfy the external demand. We investigate both a coordinated production/transportation model and a decentralized model. In the decentralized model, the retailer manages his own system and sends orders to the supplier, while the supplier determines her own production process and the amount to produce in an inventory replenishment cycle according to the order quantity of the retailer. However, in the coordinated model, the supplier makes all the decisions, so that she determines the length of the replenishment and transportation cycles as well as the shipment quantities to the retailer. We determine the structure of the optimal replenishment and transportation cycles hi both coordinated and decentralized models and the corresponding costs. Our computational results compare the optimal costs under the coordinated and decentralized models. We also numerically investigate the effects of several parameters on the optimal solutions.Publication Metadata only A lab-scale manufacturing system environment to investigate data-driven production control approaches(Elsevier Sci Ltd, 2021) N/A; N/A; Department of Business Administration; Khayyati, Siamak; Tan, Barış; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600Controlling production and release of material into a manufacturing system effectively can lower work-inprogress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a datadriven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the datadriven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.Publication Metadata only A learning based algorithm for drone routing(Pergamon-Elsevier Science Ltd, 2022) N/A; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Ermağan, Umut; Yıldız, Barış; Salman, Fatma Sibel; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 258791; 178838We introduce a learning-based algorithm to solve the drone routing problem with recharging stops that arises in many applications such as precision agriculture, search and rescue, and military surveillance. The heuristic algorithm, namely Learn and Fly (L&F), learns from the features of high-quality solutions to optimize recharging visits, starting from a given Hamiltonian tour that ignores the recharging needs of the drone. We propose a novel integer program to formulate the problem and devise a column generation approach to obtain provably high-quality solutions that are used to train the learning algorithm. Results of our numerical experiments with four groups of instances show that the classification algorithms can effectively identify the features that determine the timing and location of the recharging visits, and L&F generates energy feasible routes in a few seconds with around 5% optimality gap on the average.Publication Metadata only A new magnetorheological damper for chatter stability of boring tools(Elsevier Science Sa, 2021) N/A; N/A; Department of Chemistry; Department of Mechanical Engineering; Saleh, Mostafa Khalil Abdou; Nejatpour, Mona; Acar, Havva Funda Yağcı; Lazoğlu, İsmail; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Chemistry; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; N/A; N/A; 178902; 179391Chatter is a limiting factor during boring of deep holes with long slender boring bars. In this article, a new magnetorheological (MR) damper is introduced to increase the stability of the boring process. The sponge-type configuration of the damper utilizes a minimal amount of MR fluid in the annulus around the boring bar. The MR fluid layer and the electromagnetic circuit are externally applied to the boring bar, which allows easy installation and adjustability in bar length. A custom made, bidisperse MR fluid is used to eliminate particle sedimentation and enhance the lifetime of the damper. The modal analysis of the boring bar with the new MR damper shows improvements in both the damping and the dynamic stiffness of the system. This enhancement significantly increases the chatter-free depth of cut on the stability lobe diagrams. This article presents the experimental validations on the boring of AL 7075 and Inconel 718 workpieces which are materials widely used in many aerospace applications. The damper is installed on a conventional boring bar for a CNC machining center setup, and its performance is tested under various machining conditions.Publication Metadata only A novel analytical algorithm for prediction of workpiece temperature in end milling(Elsevier, 2022) N/A; Department of Mechanical Engineering; Akhtar, Waseem; Lazoğlu, İsmail; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391Temperature is a critical parameter in machining as it directly affects the cutting performance, part quality, residual stresses, distortion, tool life, etc. In this article, a novel analytical algorithm for fast temperature predic-tion in intermittent cutting processes like milling is proposed. For the first time, the temperature drop during the noncutting period is taken into consideration for the workpiece side. The model also takes into account time-varying chip thickness due to the trochoidal motion of the milling tool. Validation tests with Ti6Al4V showed the promise of the algorithm in predicting the milling temperature under various cutting conditions.(c) 2022 CIRP. Published by Elsevier Ltd. All rights reserved.Publication Metadata only A preference-based appointment scheduling problem with multiple patient types(TMMOB Makine Mühendisleri Odası, 2019) N/A; Department of Industrial Engineering; Tunçalp, Feray; Örmeci, Lerzan; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 32863This paper focuses on the appointment scheduling mechanism of a physician or a diagnostic resource in a healthcare facility. Multiple patient types with different revenues use the facility. The facility observes the number of appointment requests arriving from each patient type at the beginning of each day. It decides on how to allocate available appointment slots to these appointment requests. Patients prefer a day in the booking horizon with a specific probability and they have only one preference. Patients are either given an appointment for their preferred days or their appointment requests are rejected. The facility wants to keep the rejection costs at a certain level, while maximizing its revenues. This process is modeled with a discrete time and constrained Markov Decision Process to maximize the infinitehorizon expected discounted revenue. The constraint guarantees that the infinite-horizon expected discounted rejection cost is below a specific threshold. We have proved that the optimal policy is a randomized booking limit policy. To solve the model, we have implemented Temporal Difference (TD) Learning Algorithm, which is a well-known Approximate Dynamic Programming (ADP) method. We have compared the ADP results with other heuristics numerically / Bu makale, bir sağlık tesisindeki bir doktor ya da tanı cihazının randevu planlama mekanizmasına odaklanmaktadır. Bu tesisi, getirileri birbirinden farklı olan birden çok hasta tipi kullanmaktadır. Tesis, her hasta tipinden gelen randevu isteklerini her günün başında gözlemlemektedir. Müsait randevu saatlerini bu randevu isteklerine nasıl tahsis edeceğine karar vermektedir. Hastalar belli bir olasılıkla rezervasyon dönemindeki bir günü tercih etmektedirler ve sadece bir tercihleri vardır. Hastalara ya tercih ettiği güne bir randevu verilmektedir ya da randevu istekleri reddedilmektedir. Tesis, getirilerini maksimize ederken reddedilme maliyetlerini belli bir seviyede tutmak istemektedir. Bu süreç, sonsuz zamanlı beklenen indirgenmiş karı maksimize etmek için ayrık zamanlı ve kısıtlı Markov Karar Süreci ile modellenmektedir. Kısıt, sonsuz zamanlı beklenen indirgenmiş reddedilme maliyetlerinin belli bir eşik değerinin altında olmasını garanti etmektedir. En iyi politikanın rassallaştırılmış bir rezervasyon limiti politikasının olduğunu gösterdik. Modeli çözmek için iyi bilinen bir “Yaklaşık Dinamik Programlama” metodu olan “Geçici Farklarla Öğrenme Algoritmasını” uyguladık. “Yaklaşık Dinamik Programlama” sonuçlarını diğer buluşsal yöntemlerle sayısal olarak karşılaştırdık.Publication Metadata only A tabu search algorithm for order acceptance and scheduling(Pergamon-Elsevier Science Ltd, 2012) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Cesaret, Bahriye; Oğuz, Ceyda; Salman, Fatma Sibel; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 256439; 6033; 178838We consider a make-to-order production system, where limited production capacity and order delivery requirements necessitate selective acceptance of the orders. Since tardiness penalties cause loss of revenue, scheduling and order acceptance decisions must be taken jointly to maximize total revenue. We present a tabu search algorithm that solves the order acceptance and scheduling problem on a single machine with release dates and sequence dependent setup times. We analyze the performance of the tabu search algorithm on an extensive set of test instances with up to 100 orders and compare it with two heuristics from the literature. In the comparison, we report optimality gaps which are calculated with respect to bounds generated from a mixed integer programming formulation. The results show that the tabu search algorithm gives near optimal solutions that are significantly better compared to the solutions given by the two heuristics. Furthermore, the run time of the tabu search algorithm is very small, even for 100 orders. The success of the proposed heuristic largely depends on its capability to incorporate in its search acceptance and scheduling decisions simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.Publication Metadata only A variable neighborhood search for minimizing total weighted tardiness with sequence dependent setup times on a single machine(Pergamon-Elsevier Science Ltd, 2012) N/A; N/A; Department of Industrial Engineering; Kirlik, Gökhan; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033This paper deals with the single machine scheduling problem to minimize the total weighted tardiness in the presence of sequence dependent setup. Firstly, a mathematical model is given to describe the problem formally. Since the problem is NP-hard, a general variable neighborhood search (GVNS) heuristic is proposed to solve it. Initial solution for the GVNS algorithm is obtained by using a constructive heuristic that is widely used in the literature for the problem. The proposed algorithm is tested on 120 benchmark instances. The results show that 37 out of 120 best known solutions in the literature are improved while 64 instances are solved equally. Next, the GVNS algorithm is applied to single machine scheduling problem with sequence dependent setup times to minimize the total tardiness problem without changing any implementation issues and the parameters of the GVNS algorithm. For this problem, 64 test instances are solved varying from small to large sizes. Among these 64 instances, 35 instances are solved to the optimality, 16 instances' best-known results are improved, and 6 instances are solved equally compared to the best-known results. Hence, it can be concluded that the GVNS algorithm is an effective, efficient and a robust algorithm for minimizing tardiness on a single machine in the presence of setup times.Publication Metadata only Active damping of chatter in the boring process via variable gain sliding mode control of a magnetorheological damper(Elsevier, 2021) N/A; N/A; Department of Mechanical Engineering; Saleh, Mostafa Khalil Abdou; Ulasyar, Abasin; Lazoğlu, İsmail; PhD Student; Researcher; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; N/A; College of Engineering; N/A; N/A; 179391In this article, a sliding mode control of a magnetorheological fluid damper is presented for active damping of chatter in the boring process for the first time. A boring bar is integrated with an in-house developed magnetorheological fluid damper system. The variable gain super twisting sliding mode control algorithm is designed and implemented for suppressing the chatter in the boring process. Simulations of the controller show its fast response and robustness against disturbances and parametric uncertainties. Validation cutting tests performed under various machining conditions showed that the stability limit can be increased significantly with the sliding mode control of the magnetorheological fluid damper.Publication Metadata only Analysis of mobile phone call data of İstanbul residents(IGI Global, 2015) Department of Industrial Engineering; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Sivaslıoğlu, Erbil; Memiş, Burak; Faculty Member; Undergraduate Student; PhD Student; Department of Industrial Engineering; College of Engineering; College of Engineering; Graduate School of Business; 178838; N/A; N/AIn this chapter, we analyze call detail records of subscribers of a major cellular network provider in Turkey with a focus on subscribers that reside in Istanbul. We consider a sample of 10,000 opt-in subscribers, chosen proportionally to the population density of each district of Istanbul. The anonymized cell phone usage data for 6 weeks are combined with demographic and subscription package attributes. Our methodology consists of data retrieval and cleaning, analysis and visualization. The analysis aims to extract information to be used mainly in disaster preparedness, marketing and public service design, and is categorized under: 1) understanding call habits in terms of call duration and call location with respect to gender and age categories, 2) tracking population density changes by time and district, 3) segmentation of people visiting specified locations, 4) information on mobility of disabled subscribers, and 5) international travel patterns by roaming data analysis.