Mehmet A. Begen

• Lofti, A.; Jiang, Z.; Naoum-Sawaya, J.; Begen, M. A., (Forthcoming), "Modeling Sales of Multigeneration Technology Products in the Presence of Frequent Repeat Purchases: A Fractional Calculus-Based Approach", Production and Operations Management
  Abstract: Frequently releasing a new product generation has become a common practice to sustain sales over time, thus accurately forecasting the sales trajectory of each product generation plays a vital role in the short-, medium-, and long-term planning of a firm. Classic multigeneration diffusion models do not incorporate within-generation repeat purchases, making them unusable for product lines with high rates of such purchases. Concentrating on technology products, we develop a multigeneration sales model to fill this void. We demonstrate that the new model can be used for predictive and prescriptive analytics. Our empirical results show that the new model estimates and forecasts sales more accurately than a state-of-the-art benchmark model that does not account for within-generation repeat purchases, underscoring the importance of incorporating repeat purchases. Furthermore, we use two different versions of our model to examine market entry timing under two main strategies, i.e., (i) a phase-out transition strategy in which firms continue to sell the old generation after the release of the new generation, and (ii) a total transition strategy in which firms discontinue the old generation after the introduction of the new generation. Our results indicate that the repeat purchases rate determines whether it is optimal to expedite or delay the new product launch, underscoring the importance of incorporating repeat purchases in market entry strategies.

  Link(s) to publication:

  http://dx.doi.org/10.1177/10591478241240744

• Fišar, M.; Greiner, B.; Huber, C.; Katok, E.; Ozkes, A. I.; Begen, M. A.; Management Science Reproducibility Collaboration, ., 2024, "Reproducibility in Management Science", Management Science, March 70(3): 1343 - 2022.
  Abstract: With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis?fast track.Supplemental Material: The online appendices and data are available at https://doi.org/10.1287/mnsc.2023.03556.

  Link(s) to publication:

  https://doi.org/10.1287/mnsc.2023.03556

  http://dx.doi.org/10.1287/mnsc.2023.03556

• Begen, M. A.; Rodrigues, F. F.; Rice, T.; Zaric, G. S., (Forthcoming), "A Forecasting Tool for a Hospital to Plan Inbound Transfers of COVID-19 Patients from Other Regions", Bmc Public Health, February 24(505)
  Abstract: Background: In April 2021, Ontario, Canada, was at the peak of its third wave of the COVID-19 pandemic. Intensive Care Unit (ICU) capacity in the Toronto metropolitan area was insufficient to handle local COVID patients. As a result, some patients from the Toronto metropolitan area were transferred to other regions. Methods: A spreadsheet-based Monte Carlo simulation tool was built to help a large tertiary hospital plan and make informed decisions about the number of transfer patients it could accept from other hospitals. The model was implemented in Microsoft Excel to enable it to be widely distributed and easily used. The model estimates the probability that each ward will be overcapacity and percentiles of utilization daily for a one-week planning horizon. Results: The model was used from May 2021 to February 2022 to support decisions about the ability to accept transfers from other hospitals. The model was also used to ensure adequate inpatient bed capacity and human resources in response to various COVID-related scenarios, such as changes in hospital admission rates, managing the impact of intra-hospital outbreaks and balancing the COVID response with planned hospital activity. Conclusions: Coordination between hospitals was necessary due to the high stress on the health care system. A simple planning tool can help to understand the impact of patient transfers on capacity utilization and improve the confidence of hospital leaders when making transfer decisions. The model was also helpful in investigating other operational scenarios and may be helpful when preparing for future outbreaks or public health emergencies.

  Link(s) to publication:

  http://dx.doi.org/10.1186/s12889-024-18038-3

• Zacharias, C.; Liu, N.; Begen, M. A., 2024, "Dynamic Interday and Intraday Scheduling", Operations research, January 72(1): 317 - 335.
  Abstract: Adaptive Patient Flow Management Appointment scheduling has significant clinical, operational, and economical impact on healthcare systems. An informed scheduling strategy that can effectively match patient demand and service capacity dynamically is vital for the business of medical providers, quality of care, and patient satisfaction. By regulating patient flow via an appointment system, healthcare providers can mitigate arrival process variability and improve operational performance. The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is an important theoretical and practical problem that has remained open because of its stochastic nature, complex structure, and large dimensionality. Zacharias et al. (2022) fill this critical gap in the literature. They introduce a novel dynamic programming framework, designed with the intention of bridging two independently established streams of literature, and to leverage their latest advances in tackling the joint problem. They advance the theory of the field to provide a rigorous and practically implantable solution. The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is a theoretical and practical problem that has remained open. We introduce a novel dynamic programming framework that incorporates jointly these scheduling decisions in two timescales. Our model is designed with the intention of bridging the two streams of literature on interday and intraday scheduling and to leverage their latest theoretical developments in tackling the joint problem. We establish theoretical connections between two recent studies by proving novel theoretical results in discrete convex analysis regarding constrained multimodular function minimization. Grounded on our theory, we develop a practically implementable and computationally tractable scheduling paradigm with performance guarantees. Numerical experiments demonstrate that the optimality gap is less than 1% for practical instances of the problem.

  Link(s) to publication:

  https://pubsonline.informs.org/doi/10.1287/opre.2022.2342

  http://dx.doi.org/10.1287/opre.2022.2342

• Begen, M. A.; Odegaard, F.; Sadeghi, J., (Forthcoming), "Intra-provincial benchmark analysis of COVID-19 in Canada", INFOR: Information Systems and Operational Research: 1 - 42.
  Abstract: The COVID-19 pandemic posed unheralded challenges to people, business, government at all levels (federal, provincial, regional), and society at large. In addition to the direct consequences of taking care of infected people, which in some countries led to a virtual collapse of the healthcare system, the pandemic strained eldercare, employment, economic growth, and exacerbated mental health and social problems. During the first year of the pandemic, researchers? and policy makers? main focus was on ?flattening the curve,? and on predictive modeling of infections and deaths. In this paper we present a non-parametric data-driven descriptive analysis based on Data Envelopment Analysis to assess COVID-19 in ten Canadian provinces over the two year period 2020 to 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if and to what extent the situation could have been worse respectively better. To take account for any indirect socio-economic impact our analysis incorporates official monthly unemployment rates and a stringency index reflecting the level of social policy restrictions imposed by the provincial governments. A major contribution of the model framework is that it provides a mechanism for measuring the impact of the two main strategies in curbing the pandemic, namely vaccination and social policy restrictions. As a robustness check, the bench-mark results are compared against bias-corrected efficiency measures.

  Link(s) to publication:

  https://doi.org/10.1080/03155986.2024.2302298

  http://dx.doi.org/10.1080/03155986.2024.2302298

• Begen, M. A.; Odegaard, F.; Sadeghi, J., (Forthcoming), "On aggregation of technical and revenue efficiency measures", Journal of Productivity Analysis
  Abstract: We discuss and propose new results regarding combined production efficiencies of an aggregate group, such as industry or region. Based on the production economic notion of aggregate technology, we present an algebraic representation of the aggregation output set, and use this to derive a new decomposition of the aggregation unit’s technical efficiency, e.g. a group’s technical efficiency. Our novel decomposition can identify more clearly the inefficiency due to the underlying units and the inefficiency due to the aggregation. For the special single output case, the proposed aggregation measure is consistent with the extant literature, and identical to Farrell’s measure of structural efficiency of a group. However, for the general multiple outputs case we show that there is a gap between the proposed measure and the extant literature. We illustrate the formal results with a numerical example based on a dataset from literature.

  Link(s) to publication:

  https://doi.org/10.1007/s11123-023-00710-2

  http://dx.doi.org/10.1007/s11123-023-00710-2

• Lyons, J. S. F.; Begen, M. A.; Bell, P. C., 2023, "Surgery Scheduling and Perioperative Care: Smoothing and Visualizing Elective Surgery and Recovery Patient Flow", Analytics, August 2(3): 656 - 675.
  Abstract: This paper addresses the practical problem of scheduling operating room (OR) elective surgeries to minimize the likelihood of surgical delays caused by the unavailability of capacity for patient recovery in a central post-anesthesia care unit (PACU). We segregate patients according to their patterns of flow through a multi-stage perioperative system and use characteristics of surgery type and surgeon booking times to predict time intervals for patient procedures and subsequent recoveries. Working with a hospital in which 50+ procedures are performed in 15+ ORs most weekdays, we develop a constraint programming (CP) model that takes the hospital’s elective surgery pre-schedule as input and produces a recommended alternate schedule designed to minimize the expected peak number of patients in the PACU over the course of the day. Our model was developed from the hospital’s data and evaluated through its application to daily schedules during a testing period. Schedules generated by our model indicated the potential to reduce the peak PACU load substantially, 20-30% during most days in our study period, or alternatively reduce average patient flow time by up to 15% given the same PACU peak load. We also developed tools for schedule visualization that can be used to aid management both before and after surgery day; plan PACU resources; propose critical schedule changes; identify the timing, location, and root causes of delay; and to discern the differences in surgical specialty case mixes and their potential impacts on the system. This work is especially timely given high surgical wait times in Ontario which even got worse due to the COVID-19 pandemic.

  Link(s) to publication:

  http://dx.doi.org/10.3390/analytics2030036

• Akioyamen, P.; Begen, M. A., 2023, "A Spatio-Temporal Analysis of OECD Member Countries’ Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference", International Journal of Environmental Research and Public Health, June 20(13): 6265 - 6265.
  Abstract: Determinants of health care quality and efficiency are of importance to researchers, policy-makers, and public health officials as they allow for improved human capital and resource allocation as well as long-term fiscal planning. Statistical analyses used to understand determinants have neglected to explicitly discuss how missing data are handled, and consequently, previous research has been limited in inferential capability. We study OECD health care data and highlight the importance of transparency in the assumptions grounding the treatment of data missingness. Attention is drawn to the variation in ordinary least squares coefficient estimates and performance resulting from different imputation methods, and how this variation can undermine statistical inference. We also suggest that parametric regression models used previously are limited and potentially ill-suited for analysis of OECD data due to the inability to deal with both spatial and temporal autocorrelation. We propose the use of an alternative method in geographically and temporally weighted regression. A spatio-temporal analysis of health care system efficiency and quality of care across OECD member countries is performed using four proxy variables. Through a forward selection procedure, medical imaging equipment in a country is identified as a key determinant of quality of care and health outcomes, while government and compulsory health insurance expenditure per capita is identified as a key determinant of health care system efficiency.

  Link(s) to publication:

  http://dx.doi.org/10.3390/ijerph20136265

• Dogan, S.; Okuyan, H. M.; Bal, T.; Çabalak, M.; Begen, M. A., 2023, "Relationship of thrombospondin-1 and thrombospondin-2 with hematological, biochemical and inflammatory markers in COVID-19 patients", Turkish Journal of Biochemistry-Turk Biyokimya Dergisi, June 48(4): 368 - 375.
  Abstract: Objectives: Roles of thrombospondin-1 (TSP-1) and thrombospondin-2 (TSP-2) in tissue repair and inflammation are well-documented, but the association of their serum expressions with the pathogenesis of COVID-19 remains unclear. We investigate the roles of TSP-1 and TSP-2 in COVID-19. Methods: 106 SARS-CoV-2 infected patients and 23 healthy people were enrolled in our study. COVID-19 patients were divided into two groups as non-severe and severe. TSP-1 and TSP-2 concentrations were measured with an enzyme-linked Immunosorbent Assay, and blood markers were analyzed with routine laboratory techniques. Results: COVID-19 patients had significantly higher TSP-1 and TSP-2 levels than healthy controls. TSP-1 and TSP-2 positively correlated with inflammatory markers, including ESR, CRP, PCT, ferritin, and biochemical parameters such as ALT, AST, BUN, CK, and LDH. In addition, TSP-1 and TSP-2 were negatively correlated with hematological markers such as LYM, EOS, and HGB. Receiver operating characteristic analyses revealed that COVID-19 may be predicted with TSP-1 levels over 189.94 ng/mL and TSP-2 levels higher than 0.70 ng/mL. Conclusions: Our analysis suggests that TSP-1 and TSP-2 expressions at the systemic level may have clinical importance for COVID-19.

  Link(s) to publication:

  https://doi.org/10.1515/tjb-2022-0265

  http://dx.doi.org/10.1515/tjb-2022-0265

• Naderi, B.; Begen, M. A.; Zaric, G. S.; Roshanaei, V., 2023, "A Novel and Efficient Exact Technique for Integrated Staffing, Assignment, Routing, and Scheduling of Home Care Services Under Uncertainty", Omega-International Journal of Management Science, April 116: 102805 - 102805.
  Abstract: We model and solve an integrated multi-period staffing, assignment, routing, and scheduling for home care services under uncertainty. The goal is to construct a weekly schedule that adheres to related operational considerations and determines optimal staffing of caregivers by minimizing caregivers’ fixed- and overtime costs. For tractability, we incorporate a priori generated visit patterns—an existing practical approach that deals effectively with hard assignment decisions in. First, we propose a novel mixed-integer program (MIP) for the nominal problem. We then incorporate uncertainty in service and travel times and develop a robust counterpart by hybridizing interval and polyhedral uncertainty sets. Second, we show that there is a special mathematical structure within the model that allows us to develop a novel logic-based Benders branching-decomposition algorithm that systematically delays the resolution of difficult routing/ scheduling problems and efficiently solves both the nominal and robust MIP models. Using a dataset from the literature, we show that CPLEX can solve our nominal and robust models with an average optimality gaps of 44.56% and 45.53%, respectively. Using the same dataset, we demonstrate that our new exact technique can solve our nominal and robust mixed-integer models to an average optimality gap of 2.8% and 4.5%, respectively. Third, we provide practical insights into (i) the price of robustness and (ii) the impacts of nurse flexibility and overtime. The average total cost does not increase beyond 12.7% than the nominal solution and the cost-savings of nurse flexibility is approximately five times higher than that of overtime.

  Link(s) to publication:

  https://www.sciencedirect.com/science/article/pii/S0305048322002110

  http://dx.doi.org/10.1016/j.omega.2022.102805

• Naderi, B.; Begen, M. A.; Zaric, G. S.; Roshanaei, V., 2023, "A novel and efficient exact technique for integrated staffing, assignment, routing, and scheduling of home care services under uncertainty", Omega, April 116: 102805 - .
  Abstract: We model and solve integrated multi-period staffing, assignment, routing, and scheduling of caregivers for home care services and obtain insights for the case under uncertainty. The goal is to construct a weekly schedule that adheres to related operational considerations and determines optimal staffing of caregivers by minimizing caregivers’ fixed- and overtime costs. For tractability, we incorporate a priori-generated visit pattern—an existing practical approach that deals effectively with hard assignment decisions. First, we propose a novel mixed-integer program (MIP) for the nominal (deterministic) problem. We then incorporate uncertainty in service and travel times and develop a robust counterpart by hybridizing interval and polyhedral uncertainty sets. Second, we show that there is a special mathematical structure within the model that allows us to develop a novel logic-based Benders branching-decomposition algorithm that systematically delays the resolution of difficult routing/ scheduling problems and efficiently solves both the nominal and robust MIP models, i.e., our solution approach for the deterministic case allowed us to solve the robust model. Using a benchmark and newly generated instances from the literature, we show that CPLEX can solve our nominal model with average optimality gap of 56%. On the other hand, our new exact technique can solve our nominal model to an average optimality gap of 5%. Third, we provide practical insights into (i) the price of robustness and (ii) the impacts of nurse flexibility and overtime. The average total cost does not increase beyond 18% than the nominal solution and the cost-savings of nurse flexibility is about 19% on average and higher than overtime’s. We also demonstrate that nurse flexibility impact is high and comparable in both cases, deterministic and stochastic.

  Link(s) to publication:

  https://www.sciencedirect.com/science/article/pii/S0305048322002110

  http://dx.doi.org/10.1016/j.omega.2022.102805

• BAYAZIT, B.; UÇARKUS, G.; ÇAGLAR GENÇOSMAN, B.; Begen, M. A., 2022, "Meta-Heuristic Algorithms based on Integer Programming for Shelf Space Allocation Problems", The European Journal of Science and Technology, November (41): 100 - 117.
  Abstract: Retail shelf space management, which is one of the most complex aspects of retailing, can be defined as determining when, where and in what quantities products will be displayed and dynamically updating the display considering changing market conditions. Although it is an important problem, research papers that study rectangular arrangement of products to optimize profit are limited. In this paper, we determine rectangular facing units of products to maximize profit for shelf space allocation and the display problem. To solve our two-dimensional shelf space allocation problem, we develop two matheuristic algorithms by using integer programming and genetic algorithm (TP-GA) and integer programming and firefly algorithm (TP-ABA) meta-heuristics together. The performances of the mathheuristics were compared with a real-world dataset from a bookstore. TP-GA and TP-ABA methods were able to generate near optimal solutions with an average of 4.47% and 4.57% GAPs, respectively. We can also solve instances up to 900 products. These matheuristic algorithms, which are successful in the two-dimensional shelf assignment problem, can also be used to solve similar problems such as allocation of books in a bookstore, allocation of product families in a grocery store, or display of advertisements on websites.

  Link(s) to publication:

  https://dergipark.org.tr/tr/pub/ejosat/issue/72892/1121006

  http://dx.doi.org/10.31590/ejosat.1121006

• Okuyan, C. B.; Caglar, S.; Begen, M. A., 2022, "How does the COVID-19 pandemic influence educational and psychological health of nursing students in Turkey: What can be done to minimize adverse effects of the pandemic?", Technium Education and Humanities, September 2(4): 63 - 72.
  Abstract: We evaluate the effects of the COVID-19 pandemic on the educational processes, and psychological health of nursing students and provide an overview of the measures that should be taken against minimizing the adverse effects of the pandemic. We find that nursing students experience anxiety and stress during the pandemic due to online education and social isolation. Social media-based health education planning (provided by nursing trainers) and inclusion of online simulation applications to the curriculum can be effective ways to prevent the negative effects of the pandemic on education of nursing students and to protect students' health and ensure that they gain necessary nursing skills as in formal education.

  Link(s) to publication:

  https://techniumscience.com/index.php/education/article/view/7483

• Begen, M. A.; Okuyan, H. M., 2022, "LncRNAs in Osteoarthritis", Clinica Chimica Acta, July 532: 145 - 163.
  Abstract: Osteoarthritis (OA) is a progressive joint disease that affects millions of older adults around the world. With increasing rates of incidence and prevalence worldwide, OA has become an enormous global socioeconomic burden on healthcare systems. Long non-coding ribonucleic acids (lncRNAs), essential functional molecules in many biological processes, are a group of non-coding RNAs that are greater than approximately 200 nucleotides in length. Fast-growing and recent developments in lncRNA research are captivating and represent a novel and promising field in understanding the complexity of OA pathogenesis. The involvement of lncRNAs in OA’s pathological processes and their altered expressions in joint tissues, blood and synovial fluid make them attractive candidates for the diagnosis and treatment of OA. We focus on the recent advances in major regulator mechanisms of lncRNAs in the pathophysiology of OA and discuss potential diagnostic and therapeutic uses of lncRNAs for OA. We investigate how upregulation or downregulation of lncRNAs influences the pathogenesis of OA and how we can use lncRNAs to elucidate the molecular mechanism of OA. Furthermore, we evaluate how we can use lncRNAs as a diagnostic marker or therapeutic target for OA. Our study not only provides a comprehensive review of lncRNAs regarding OA’s pathogenesis but also contributes to the elucidation of its molecular mechanisms and to the development of diagnostic and therapeutic approaches for OA.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.cca.2022.05.030

• Caglar Gencosman, B.; Begen, M. A., 2022, "Exact Optimization and Decomposition Approaches for Shelf Space Allocation", European Journal Of Operational Research, June 299(2): 432 - 447.
  Abstract: Shelf space is one of the scarcest resources, and its effective management to maximize profits has become essential to gain a competitive advantage for retailers. We consider the two-dimensional shelf space allocation problem (2DSSAP) with additional features motivated by literature and our interactions with a local bookstore. Two dimensions represent the width and height of rectangular arrangement space of a product. We determine optimal number of facings of all products in both dimensions and allocate them as contiguous rectangles to maximize profit. We first develop a mixed-integer linear mathematical programming model (MIP) for our problem and propose a solution method based on logic-based Benders decomposition (LBBD). Next, we construct an exact 2-stage algorithm (IP1/IP2), inspired by LBBD, which can handle larger and real-world size instances. To compare performances of our methods, we generate 100 test instances inspired by real-world applications and benchmarks from the literature. We observe that IP1/IP2 finds optimal solutions for real-world instances efficiently and can increase the local bookstore's profit up to 16.56%. IP1/IP2 can provide optimal solutions for instances with 100 products in minutes and optimally solve up to 250 products (assigned to 8 rows x 160 columns) within a time limit of 1800 seconds. This exact 2-stage IP1/IP2 solution approach can be effective in solving similar problems such as display problem of webpage design, allocation of product families in grocery stores, and flyer advertising.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2021.08.047

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