Richard Ivey Building 3340
- Management Consulting
- Data Driven Approaches
- Operations Research
- Incentive Alignment
- Healthcare Operations
- Spreadsheets & VBA
- Process & Flow Analysis
- Project Management
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Professor Mehmet A. Begen is an industrial engineer, a management scientist and an associate in the Ivey Business School at the Western University. Besides Ivey, he is cross-appointed at the departments of Statistical & Actuarial Sciences and Epidemiology & Biostatistics at the Western.
Mehmet's research interests are management science/analytics applications, data-driven approaches and in particular scheduling and operations management in healthcare. He has been a PI or co-PI for NSERC Discovery Grants, Cancer Care Ontario Research Grant, NSERC Undergraduate Student Awards and others. Mehmet’s research won a top prize in the “Optimize the Real World” competition hosted by FICO for solving real business problems with use of analytics, developing mathematical models with data and obtaining managerial insights.
He has PhD and MS degrees in management science from Sauder School of Business at the University of British Columbia, and a BS degree in industrial engineering from Middle East Technical University in Turkey.
Mehmet is a Certified Analytics Professional (CAP), worked in management consulting before his PhD studies and is a recipient of CORS (Canadian Operational Research Society) Practice Prize and served as the president of CORS. He teaches courses on analytical modelling, financial analytics, analytics projects, big data tools and statistics.
- Decision Making with Analytics, MBA, AMBA and HBA
- Ivey Field Project, MMA
- Art of Modelling, MSc, MMA
- Business Statistics, MSc
- Business Fundamentals (Analytics), MBA Direct
- Applications of Stochastic Modeling, PhD
- Quantitative Analysis, Preparatory Knowledge Program, MBA
- End User Modeling (Spreadsheet Analytics and VBA), HBA
- Healthcare Analytics (Health Sector), MBA
- Best Practices: Competing with Analytics, MSc
- Business Project, MSc
- Financial Analytics, MBA and HBA
- PhD, Management Science - Sauder School of Business, University of British Columbia
- MSc, Management Science - Sauder School of Business, University of British Columbia
- BSc, Industrial Engineering - Middle East Technical University
Recent Refereed Articles
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, September 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:
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, August 48(4): 368 - 375. Abstract:
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.
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.
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.
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:
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:
- Sadeghi, J.; Begen, M. A.; Ødegaard, F., 2023, "Refined bounds for the non-Archimedean in DEA", Computers & Operations Research, June 154: 106163 - 106163.
- 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 - 102805.
Naderi, B.; Begen, M. A.; Zaric, G. S., (Forthcoming), "Type-2 integrated process-planning and scheduling problem: Reformulation and solution algorithms", Computers and Operations Research Abstract: We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for this problem are all based on heuristics and metaheuristics. We introduce three properties that enable us to fully formulate all aspects of the type-2 IPPS problem with a mathematical programming model for the first time. To solve our model, we develop a logic-based Benders decomposition method hybridized with constraint programming. We decompose the problem into two smaller ones such that we can use the best solution technique for each one, master problem and subproblem. To enhance our solution approach, we incorporate a combinatorial relaxation of subproblem into the master problem. We evaluate our method using a well-known benchmark including 24 instances and compare its performance with six existing solution methods solving the same benchmark. We solve all the 24 instances of this benchmark to optimality where seven of these 24 instances are solved to optimality for the first time. We also generate a new set of 144 larger instances to further evaluate our solution methods and provide insights on when each method performs better.
Begen, M. A.; Okuyan, H. M., (Forthcoming), "LncRNAs in Osteoarthritis", Clinica Chimica Acta 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.
Naderi, B.; Begen, M. A.; Zaric, G. S.; Roshanaei, V., (Forthcoming), "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 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:
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, October (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:
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:
Begen, M. A.; Bayley, T.; Rodrigues, F. F.; Barrett, D., 2022, "Relative Efficiency of Radiation Treatment Centres: An Application of Data Envelopment Analysis", Healthcare, June 10(6): 1033 - 1033. Abstract: We evaluate a number of cancer treatment centres in Ontario and determine their relative efficiency so that their performance can be compared against the provincial targets by taking into account the differences among them. These differences can be in physical and financial resources, and patient demographics. We develop an analytical framework based on a three- step data envelopment analysis (DEA) model to build efficiency metrics for planning, delivery, and quality of treatment at each centre, use regression analysis to explain our efficiency metrics, and demonstrate how our findings can inform continuous improvement efforts.
Link(s) to publication:
Okuyan, C. B.; Begen, M. A., (Forthcoming), "Working from Home During the COVID-19 Pandemic, its Effects on Health, and Recommendations: The Pandemic and Beyond", Perspectives In Psychiatric Care Abstract: Purpose: We provide an overview of how to work from home during the COVID-19 pandemic, and what measures should be taken to minimize the negative effects of working from during this time. Conclusions: The COVID-19 pandemic has forced an adaptation process for the whole world and working life. One of the most adaptation measures is working from home. Working from home comes with challenges and concerns but it also has its favorable aspects. Practice Implications: It is crucial to develop and implement best practices for working from home to maintain a good level of productivity, achieve the right level of work and life balance and maintain a good level of physical and mental health.
Link(s) to publication:
Caglar Gencosman, B.; Begen, M. A., (Forthcoming), "Exact Optimization and Decomposition Approaches for Shelf Space Allocation", European Journal Of Operational Research 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:
Naderi, B.; Roshanaei, V.; Begen, M. A.; Aleman, D.; Urbach, D., 2021, "Increased surgical capacity without additional resources: Generalized operating room planning and scheduling", Production and Operations Management, October 30(8): 2608 - 2635. Abstract: We study a generalized operating room planning and scheduling (GORPS) problem at the Toronto General Hospital (TGH) in Ontario, Canada. GORPS allocates elective patients and resources (i.e., operating rooms, surgeons, anesthetists) to days, assigns resources to patients, and sequences patients in each day. We consider patients’ due-date, resource eligibility, heterogeneous performances of resources, downstream unit requirements, and lag times between resources. The goal is to create a weekly surgery schedule that minimizes fixed- and over-time costs. We model GORPS using mixed-integer and constraint programming models. To efficiently and effectively solve these models, we develop new‘ multi-featured logic-based Benders decomposition approaches. Using data from TGH, we demonstrate that our best algorithm solves GORPS with an average optimality gap of 2.71% which allows us to provide our practical recommendations. First, we can increase daily OR utilization to reach 80%—25% higher than the status quo in TGH. Second, we do not require to optimize for the daily selection of anesthetists —this finding allows for the development of effective dominance rules that significantly mitigate intractability. Third, solving GORPS without downstream capacities (like many papers in literature) makes GORPS easier to solve, but such OR schedules are only feasible in 24% of instances. Finally, with existing ORs’ safety capacities, TGH can manage 40% increase in its surgical volumes. We provide recommendations on how TGH must adjust its downstream capacities for varying levels of surgical volume increases (e.g., current urgent need for more capacity due to the current Covid-19 pandemic).
Link(s) to publication:
Sang, P.; Begen, M. A.; Cao, J., 2021, "Appointment (Surgery) Scheduling with a Quantile Objective", Computers and Operations Research, August 132: 105295 - 105295. Abstract: Appointment scheduling has many applications (e.g., surgery scheduling, airport gate scheduling, container vessel dockings and radiation therapy bookings) and it has a direct and significant operational and economic impact. For example, in healthcare, surgical departments are one of the main drivers of hospital costs and revenue, and appointment scheduling is used to book surgeries. Effective scheduling not only enables patients' timely access to care but also enables more efficient operations. This becomes especially important as healthcare costs and demand are on the rise in many countries. We study appointment scheduling where there are jobs (e.g., patients, container vessels, airplanes) with random processing durations, an expensive processor (e.g., a doctor, dock crane, airport gate) and significant costs for processor idle time, processor overtime, and job waiting. The goal is to determine an appointment schedule that minimizes a measure of total costs as the objective. The appointment scheduling problem has been well studied in the literature with the expected cost objective. Almost all papers in the literature on appointment scheduling use the expected cost criterion, which may not be suitable when risk measures and/or service levels are considered. In this paper, we study this problem with a new objective: minimization of any quantile of the cost distribution, e.g., median, 90th percentile. We obtain theoretical results for some special cases and develop an algorithm for the general case. Our algorithm does not require a specific distribution assumption and can work directly with data samples. We present numerical examples with real data on surgeries. Our results show that allocated schedules based on the quantile objective with identical jobs are different than the ones generated by the expected cost objective and they do not show the well-known dome-shaped pattern but a semi-dome-shaped pattern which first increases (like the dome-shaped pattern) but then its decrease is not monotone (unlike the dome-shaped pattern). To the best of our knowledge, this is the first paper on appointment scheduling problem with the objective of the quantile function minimization.
Link(s) to publication:
Honours & Awards
- Ivey Research Merit Award
- Ivey Teaching Excellence Award
- Dean’s Research Faculty Fellowship
- Meritorious Reviewer Award (INFORMS Journal on Computing)
- Eldon Gunn Service Award, Canadian Operational Research Society (CORS)
- Ivey Bridge Award
- Practice Prize, Canadian Operational Research Society (CORS)
- CORS President
- Winner of FICO’s “Optimize the Real World” contest, 2014
- Certified Analytics Professional (CAP)