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Publication Open Access An integrated data-driven method using deep learning for a newsvendor problem with unobservable features(Elsevier, 2022) Pirayesh Neghab, D.; Khayyati, S.; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579We consider a single-period inventory problem with random demand with both directly observable and unobservable features that impact the demand distribution. With the recent advances in data collection and analysis technologies, data-driven approaches to classical inventory management problems have gained traction. Specially, machine learning methods are increasingly being integrated into optimization problems. Although data-driven approaches have been developed for the newsvendor problem, they often consider learning from the available data and optimizing the system separate tasks to be performed in sequence. One of the setbacks of this approach is that in the learning phase, costly and cheap mistakes receive equal attention and, in the optimization phase, the optimizer is blind to the confidence of the learner in its estimates for different regions of the problem. To remedy this, we consider an integrated learning and optimization problem for optimizing a newsvendor's strategy facing a complex correlated demand with additional information about the unobservable state of the system. We give an algorithm based on integrating optimization, neural networks and hidden Markov models and use numerical experiments to show the efficiency of our method. In an empirical experiment, the method outperforms the best competitor benchmark by more than 27%, on average, in terms of the system cost. We give further analyses of the performance of the method using a set of numerical experiments.
Publication Metadata only Bounded rationality in clearing service systems(Elsevier, 2020) Department of Industrial Engineering; Canbolat, Pelin Gülşah; Faculty Member; Department of Industrial Engineering; College of Engineering; 108242This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) for an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) system revenue grows arbitrarily large as customers tend to being irrational, (iii) social welfare is maximized when customers are perfectly rational, (iv) in all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality. (C) 2019 Elsevier B.V. All rights reserved.Publication Open Access Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen(Nature Publishing Group (NPG), 2019) Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, Donatien Chedom; Fourches, Denis; Friend, Stephen; Frigessi, Arnoldo; Gao, Feng; Gao, Xiaoting; Gerold, Jeffrey M.; Gestraud, Pierre; Ghosh, Samik; Gillberg, Jussi; Godoy-Lorite, Antonia; Godynyuk, Lizzy; Godzik, Adam; Goldenberg, Anna; Gomez-Cabrero, David; de Graaf, Chris; Gray, Harry; Grechkin, Maxim; Guimera, Roger; Guney, Emre; Haibe-Kains, Benjamin; Han, Younghyun; Hase, Takeshi; He, Di; He, Liye; Heath, Lenwood S.; Hellton, Kristoffer H.; Helmer-Citterich, Manuela; Hidalgo, Marta R.; Hidru, Daniel; Hill, Steven M.; Hochreiter, Sepp; Hong, Seungpyo; Hovig, Eivind; Hsueh, Ya-Chih; Hu, Zhiyuan; Huang, Justin K.; Huang, R. Stephanie; Hunyady, Laszlo; Hwang, Jinseub; Hwang, Tae Hyun; Hwang, Woochang; Hwang, Yongdeuk; Isayev, Olexandr; Walk, Oliver Bear Don't; Jack, John; Jahandideh, Samad; Ji, Jiadong; Jo, Yousang; Kamola, Piotr J.; Kanev, Georgi K.; Karacosta, Loukia; Karimi, Mostafa; Kaski, Samuel; Kazanov, Marat; Khamis, Abdullah M.; Khan, Suleiman Ali; Kiani, Narsis A.; Kim, Allen; Kim, Jinhan; Kim, Juntae; Kim, Kiseong; Kim, Kyung; Kim, Sunkyu; Kim, Yongsoo; Kim, Yunseong; Kirk, Paul D. W.; Kitano, Hiroaki; Klambauer, Gunter; Knowles, David; Ko, Melissa; Kohn-Luque, Alvaro; Kooistra, Albert J.; Kuenemann, Melaine A.; Kuiper, Martin; Kurz, Christoph; Kwon, Mijin; van Laarhoven, Twan; Laegreid, Astrid; Lederer, Simone; Lee, Heewon; Lee, Jeon; Lee, Yun Woo; Leppaho, Eemeli; Lewis, Richard; Li, Jing; Li, Lang; Liley, James; Lim, Weng Khong; Lin, Chieh; Liu, Yiyi; Lopez, Yosvany; Low, Joshua; Lysenko, Artem; Machado, Daniel; Madhukar, Neel; De Maeyer, Dries; Malpartida, Ana Belen; Mamitsuka, Hiroshi; Marabita, Francesco; Marchal, Kathleen; Marttinen, Pekka; Mason, Daniel; Mazaheri, Alireza; Mehmood, Arfa; Mehreen, Ali; Michaut, Magali; Miller, Ryan A.; Mitsopoulos, Costas; Modos, Dezso; Van Moerbeke, Marijke; Moo, Keagan; Motsinger-Reif, Alison; Movva, Rajiv; Muraru, Sebastian; Muratov, Eugene; Mushthofa, Mushthofa; Nagarajan, Niranjan; Nakken, Sigve; Nath, Aritro; Neuvial, Pierre; Newton, Richard; Ning, Zheng; De Niz, Carlos; Oliva, Baldo; Olsen, Catharina; Palmeri, Antonio; Panesar, Bhawan; Papadopoulos, Stavros; Park, Jaesub; Park, Seonyeong; Park, Sungjoon; Pawitan, Yudi; Peluso, Daniele; Pendyala, Sriram; Peng, Jian; Perfetto, Livia; Pirro, Stefano; Plevritis, Sylvia; Politi, Regina; Poon, Hoifung; Porta, Eduard; Prellner, Isak; Preuer, Kristina; Angel Pujana, Miguel; Ramnarine, Ricardo; Reid, John E.; Reyal, Fabien; Richardson, Sylvia; Ricketts, Camir; Rieswijk, Linda; Rocha, Miguel; Rodriguez-Gonzalvez, Carmen; Roell, Kyle; Rotroff, Daniel; de Ruiter, Julian R.; Rukawa, Ploy; Sadacca, Benjamin; Safikhani, Zhaleh; Safitri, Fita; Sales-Pardo, Marta; Sauer, Sebastian; Schlichting, Moritz; Seoane, Jose A.; Serra, Jordi; Shang, Ming-Mei; Sharma, Alok; Sharma, Hari; Shen, Yang; Shiga, Motoki; Shin, Moonshik; Shkedy, Ziv; Shopsowitz, Kevin; Sinai, Sam; Skola, Dylan; Smirnov, Petr; Soerensen, Izel Fourie; Soerensen, Peter; Song, Je-Hoon; Song, Sang Ok; Soufan, Othman; Spitzmueller, Andreas; Steipe, Boris; Suphavilai, Chayaporn; Tamayo, Sergio Pulido; Tamborero, David; Tang, Jing; Tanoli, Zia-ur-Rehman; Tarres-Deulofeu, Marc; Tegner, Jesper; Thommesen, Liv; Tonekaboni, Seyed Ali Madani; Tran, Hong; De Troyer, Ewoud; Truong, Amy; Tsunoda, Tatsuhiko; Turu, Gabor; Tzeng, Guang-Yo; Verbeke, Lieven; Videla, Santiago; Vis, Daniel; Voronkov, Andrey; Votis, Konstantinos; Wang, Ashley; Wang, Hong-Qiang Horace; Wang, Po-Wei; Wang, Sheng; Wang, Wei; Wang, Xiaochen; Wang, Xin; Wennerberg, Krister; Wernisch, Lorenz; Wessels, Lodewyk; van Westen, Gerard J. P.; Westerman, Bart A.; White, Simon Richard; Willighagen, Egon; Wurdinger, Tom; Xie, Lei; Xie, Shuilian; Xu, Hua; Yadav, Bhagwan; Yau, Christopher; Yeerna, Huwate; Yin, Jia Wei; Yu, Michael; Yu, MinHwan; Yun, So Jeong; Zakharov, Alexey; Zamichos, Alexandros; Zanin, Massimiliano; Zeng, Li; Zenil, Hector; Zhang, Frederick; Zhang, Pengyue; Zhang, Wei; Zhao, Hongyu; Zhao, Lan; Zheng, Wenjin; Zoufir, Azedine; Zucknick, Manuela; Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Publication Open Access Inventory policies for two products under Poisson demand: interaction between demand substitution, limited storage capacity and replenishment time uncertainty(Wiley, 2018) Burnetas, Apostolos; Department of Industrial Engineering; Kanavetas, Odysseas; Faculty Member; Department of Industrial Engineering; College of EngineeringWe consider a two-product inventory system with independent Poisson demands, limited joint storage capacity and partial demand substitution. Replenishment is performed simultaneously for both products and the replenishment time may be fixed or exponentially distributed. For both cases we develop a Continuous Time Markov Chain model for the inventory levels and derive expressions for the expected profit per unit time. We establish analytic expressions for the profit function and show that it satisfies decreasing differences properties in the order quantities, which allows for a more efficient algorithm to determine the optimal ordering policy. Using computational experiments, we assess the effect of substitution and replenishment time uncertainty on the order quantities and the profit as a function of the storage capacity.Publication Open Access Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes(Springer, 2019) Department of Business Administration; Department of Industrial Engineering; N/A; Tan, Barış; Dizbin, Nima Manafzadeh; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; Graduate School of Business; 28600; N/AEmpirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a base-stock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.Publication Metadata only Pricing in a transportation station with strategic customers(Wiley, 2017) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Department of Industrial Engineering; Manou, Athanasia; Canbolat, Pelin Gülşah; Karaesmen, Fikri; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 108242; 3579We consider a transportation station, where customers arrive according to a Poisson process, observe the delay information and the fee imposed by the administrator and decide whether to use the facility or not. a transportation facility visits the station according to a renewal process and serves all present customers at each visit. We assume that every customer maximizes her individual expected utility and the administrator is a profit maximizer. We model this situation as a two-stage game among the customers and the administrator, where customer strategies depend on the level of delay information provided by the administrator. We consider three cases distinguished by the level of delay information: observable (the exact waiting time is announced), unobservable (no information is provided) and partially observable (the number of waiting customers is announced). in each case, we explore how the customer reward for service, the unit waiting cost, and the intervisit time distribution parameters affect the customer behavior and the fee imposed by the administrator. We then compare the three cases and show that the customers almost always prefer to know their exact waiting times whereas the administrator prefers to provide either no information or the exact waiting time depending on system parameters.Publication Metadata only Risk-sensitive control of branching processes(Taylor and Francis inc, 2021) Department of Industrial Engineering; Canbolat, Pelin Gülşah; Faculty Member; Department of Industrial Engineering; College of Engineering; 108242This article solves the risk-sensitive control problem for branching processes where the one-period progeny of an individual can take values from a finite set. the decision maker is assumed to maximize the expected risk-averse exponential utility (or to minimize the expected risk-averse exponential disutility) of the rewards earned in an infinite horizon. individuals are assumed to produce progeny independently, and with the same probability mass function if they take the same action. This article characterizes the expected disutility of stationary policies, identifies necessary and sufficient conditions for the existence of a stationary optimal policy that assigns the same action to all individuals in all periods, and discusses computational methods to obtain such a policy. are available for this article. See the publisher's online edition of IIE Transactions, datasets, Additional tables, detailed proofs, etc.Publication Open Access Supervised learning-based approximation method for single-server open queueing networks with correlated interarrival and service times(Taylor _ Francis, 2021) Department of Industrial Engineering; Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Industrial Engineering; Department of Business Administration; College of Engineering; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AEfficient performance evaluation methods are needed to design and control production systems. We propose a method to analyse single-server open queueing network models of manufacturing systems composed of delay, batching, merge and split blocks with correlated interarrival and service times. Our method (SLQNA) is based on using a supervised learning approach to determine the mean, the coefficient of variation, and the first-lag autocorrelation of the inter-departure time process as functions of the mean, coefficient of variation and first-lag autocorrelations of the interarrival and service times for each block, and then using the predicted inter-departure time process as the input to the next block in the network. The training data for the supervised learning algorithm is obtained by simulating the systems for a wide range of parameters. Gaussian Process Regression is used as a supervised learning algorithm. The algorithm is trained once for each block. SLQNA does not require generating additional training data for each unique network. The results are compared with simulation and also with the approximations that are based on Markov Arrival Process modelling, robust queueing, and G/G/1 approximations. Our results show that SLQNA is flexible, computationally efficient, and significantly more accurate and faster compared to the other methods.Publication Open Access Sustainability in supply chain management: aggregate planning from sustainability perspective(Public Library of Science, 2016) Saraçoğlu, O.; Arslan, M.C.; Department of Industrial Engineering; Türkay, Metin; Saraçoğlu, Öztürk; Arslan, Mehmet Can; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956; N/A; N/ASupply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.Publication Open Access The fungal metabolite chaetocin is a sensitizer for pro-apoptotic therapies in glioblastoma(Nature Publishing Group (NPG), 2019) Gezen, Melike; Tolay, Nazife; Erman, Batu; Dunford, James; Oppermann, Udo; N/A; Department of Industrial Engineering; Department of Molecular Biology and Genetics; N/A; Uyulur, Fırat; Gönen, Mehmet; Önder, Tuğba Bağcı; Özyerli, Ezgi; Sur, İlknur Erdem; Şeker-Polat, Fidan; Cingöz, Ahmet; Kayabölen, Alişan; Kahya, Zeynep; Faculty Member; Department of Industrial Engineering; Department of Molecular Biology and Genetics; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; Graduate School of Health Sciences; N/A; 237468; 184359; N/A; N/A; N/A; N/A; N/A; N/AGlioblastoma Multiforme (GBM) is the most common and aggressive primary brain tumor. Despite recent developments in surgery, chemo- and radio-therapy, a currently poor prognosis of GBM patients highlights an urgent need for novel treatment strategies. TRAIL (TNF Related Apoptosis Inducing Ligand) is a potent anti-cancer agent that can induce apoptosis selectively in cancer cells. GBM cells frequently develop resistance to TRAIL which renders clinical application of TRAIL therapeutics inefficient. In this study, we undertook a chemical screening approach using a library of epigenetic modifier drugs to identify compounds that could augment TRAIL response. We identified the fungal metabolite chaetocin, an inhibitor of histone methyl transferase SUV39H1, as a novel TRAIL sensitizer. Combining low subtoxic doses of chaetocin and TRAIL resulted in very potent and rapid apoptosis of GBM cells. Chaetocin also effectively sensitized GBM cells to further pro-apoptotic agents, such as FasL and BH3 mimetics. Chaetocin mediated apoptosis sensitization was achieved through ROS generation and consequent DNA damage induction that involved P53 activity. Chaetocin induced transcriptomic changes showed induction of antioxidant defense mechanisms and DNA damage response pathways. Heme Oxygenase 1 (HMOX1) was among the top upregulated genes, whose induction was ROS-dependent and HMOX1 depletion enhanced chaetocin mediated TRAIL sensitization. Finally, chaetocin and TRAIL combination treatment revealed efficacy in vivo. Taken together, our results provide a novel role for chaetocin as an apoptosis priming agent and its combination with pro-apoptotic therapies might offer new therapeutic approaches for GBMs.