Mehmet A. Begen

Recent Refereed Articles

• Babashov, V., Aivas, I., Begen, M.A., Cao, J.Q., Rodrigues, G.B., D'Souza, D., Lock, M., Zaric, G.S., 2017, "Reducing Patient Wait Times for Radiation Therapy and Improving Treatment Planning Process: A Discrete-event Simulation Model", Clinical Oncology, June 29(6): 385 - 391.

  Abstract: Background and Purpose We analyzed the radiotherapy planning process at the London Regional Cancer Program (LRCP) to determine the bottlenecks and quantify the impact of specific resource levels with the goal of reducing wait times. Material and Methods We developed a discrete-event simulation (DES) model of a patient’s journey from a point of referral to radiation oncologist to a start of radiotherapy, considering the sequential steps and resources of the treatment planning process. We measured the impact of several resource changes on the ready-to-treat to treatment (RTTT) wait time and on the percent treated within 14-calendar-days target. Results Increasing the number of dosimetrists by one reduced the mean RTTT by 6.55%, leading to 84.92% of patients being treated within the 14-calendar-days target. Adding one more oncologist decreased the mean RTTT from 10.83 to 10.55 days, while a 15% increase in arriving patients increased the wait time by 22.53%. The model was relatively robust to the changes in quantity of other resources. Conclusions Our model identified sensitive and non-sensitive system parameters. A similar approach could be applied by other cancer programs, using their respective data and individualized adjustments, which may be beneficial in making the most effective use of limited resources.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.clon.2017.01.039

• Babashov, V., Begen, M.A., Mangel, J., Zaric, G.S., 2017, "Economic evaluation of brentuximab vedotin for persistent Hodgkin lymphoma", Current Oncology 24(1): e6 - e14.

  Abstract: Background: We conducted a cost-effectiveness analysis of brentuximab vedotin (Adcetris) for the treatment of relapsed and refractory Hodgkin lymphoma (HL) in the post-autologous stem cell transplantation (ASCT) failure period, from the perspective of the Canadian health care payer. Methods: We developed a decision-analytical model to simulate lifetime costs and benefits of brentuximab vedotin versus best supportive care for the treatment of HL patients after failure of ASCT. We parameterized the model using administrative data from Ontario, Canada. Results: In the base case, brentuximab vedotin treatment resulted in incremental quality-adjusted life-years (QALYs) of 0.544 and an incremental cost of $89,366 per patient, corresponding to an incremental cost-effectiveness ratio (ICER) of $164,248 per QALY gained. The ICER was sensitive to the cost of brentuximab vedotin, the hazard ratio used to assess the efficacy of brentuximab vedotin treatment, and health state utilities. Conclusions: In light of the available information, brentuximab vedotin has an ICER higher than $100,000 per QALY gained, which is a level often classified as having “weak evidence for adoption and appropriate utilization” in Canada. However, it is worth noting that provincial cancer agencies take into account not only the costs and associated ICER, but also other factors such as lack of alternative treatment options and clinical benefits of the expensive cancer drugs. Pricing arrangements should be negotiated and risk-sharing agreements or patient access schemes should be explored.

  Link(s) to publication:

  http://dx.doi.org/10.3747/co.24.3369

• Ozturk, O., Begen, M.A., Zaric, G.S., 2017, "A branch and bound algorithm for scheduling unit size jobs on parallel batching machines to minimize makespan", International Journal of Production Research 55(6): 1815 - 1831.

  Abstract: In this paper we present a branch and bound algorithm for the parallel batch scheduling of jobs having different processing times, release dates and unit sizes. There are identical machines with a fixed capacity and the number of jobs in a batch cannot exceed the machine capacity. All batched jobs are processed together and the processing time of a batch is given by the greatest processing time of jobs in that batch. We compare our method to a mixed integer program as well as a method from the literature that is capable of optimally solving instances with a single machine. Computational experiments show that our method is much more efficient than the other two methods in terms of solution time for finding the optimal solution.

  Link(s) to publication:

  http://dx.doi.org/10.1080/00207543.2016.1253889

  http://mehmet.begen.net/files/unit_sch.pdf

• Gencosman, B.C., Begen, M.A., Ozmutlu, C., Yilmaz, I.O., 2016, "Scheduling Methods for Efficient Stamping Operations at an Automotive Company", Production and Operations Management, November 25(11): 1902 - 1918.

  Abstract: We consider scheduling issues at Beyçelik, a Turkish automotive stamping company that uses presses to give shape to metal sheets in order to produce auto parts. The problem concerns the minimization of the total completion time of job orders (i.e., makespan) during a planning horizon. This problem may be classified as a combined generalized flowshop and flexible flowshop problem with special characteristics. We show that the Stamping Scheduling Problem is NP-Hard. We develop an integer programming-based method to build realistic and usable schedules. Our results show that the proposed method is able to find higher quality schedules (i.e., shorter makespan values) than both the company's current process and a model from the literature. However, the proposed method has a relatively long run time, which is not practical for the company in situations when a (new) schedule is needed quickly (e.g., when there is a machine breakdown or a rush order). To improve the solution time, we develop a second method that is inspired by decomposition. We show that the second method provides higher-quality solutions—and in most cases optimal solutions—in a shorter time. We compare the performance of all three methods with the company's schedules. The second method finds a solution in minutes compared to Beyçelik's current process, which takes 28 hours. Further, the makespan values of the second method are about 6.1% shorter than the company's schedules. We estimate that the company can save over €187,000 annually by using the second method. We believe that the models and methods developed in this study can be used in similar companies and industries.

  Link(s) to publication:

  http://dx.doi.org/10.1111/poms.12584

• Begen, M.A., Pun, H., Yan, X.H., 2016, "Supply and Demand Uncertainty Reduction Efforts and Cost Comparison", International Journal of Production Economics, October 180: 125 - 134.

  Abstract: In industries like health care, consumer goods and agriculture, shortages are widely observed and the consequences can be costly. One of the main drivers of such shortages is the uncertain nature of supply and demand. To reduce uncertainties, sufficient information about supply and demand can be obtained by gathering relevant data (e.g., auditing suppliers and conducting market research). In this paper, we conduct an analysis to examine the impacts of supply uncertainty, demand uncertainty and uncertainty reduction efforts on production quantity and total cost. We show that in the absence of uncertainty reduction efforts, when the financial consequences of shortages are large or when the unit benefit is large, supply uncertainty is more costly than demand uncertainty. In addition, exerting supply uncertainty reduction effort always causes the firm to produce fewer units than exerting demand uncertainty reduction effort. Although supply uncertainty reduction effort delivers a larger degree of improvement to total cost (and hence, is more efficient), reduced supply uncertainty still leads to a higher system cost than does reduced demand uncertainty.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ijpe.2016.07.013

• Ozturk, O., Begen, M.A., Zaric, G.S., 2014, "A branch and bound based heuristic for makespan minimization of washing operations in hospital sterilization services", European Journal of Operational Research, November 239(1): 214 - 226.

  Abstract: In this paper, we address the problem of parallel batching of jobs on identical machines to minimize makespan. The problem is motivated from the washing step of hospital sterilization services where jobs have different sizes, different release dates and equal processing times. Machines can process more than one job at the same time as long as the total size of jobs in a batch does not exceed the machine capacity. We present a branch and bound based heuristic method and compare it to a linear model and two other heuristics from the literature. Computational experiments show that our method can find high quality solutions within short computation time.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2014.05.014

• Begen, M.A., Levi, R., Queyranne, M., 2012, "A sampling-based approach to appointment scheduling", Operations Research, May/June 60(3): 675 - 681.

  Abstract: We consider the problem of appointment scheduling with discrete random durations, recently studied by Begen and Queyranne (2011), but under the assumption that the duration probability distributions are not known and only a set of independent samples is available, e.g., historical data. For a given sequence of appointments (jobs, tasks), the goal is to determine the planned starting time of each appointment such that the expected total underage and overage costs due to the mismatch between allocated and realized durations is minimized. We use the convexity and subdifferential of the objective function of the appointment scheduling problem to determine bounds on the number of independent samples required to obtain a provably near-optimal solution with high probability.

  Link(s) to publication:

  http://dx.doi.org/10.1287/opre.1120.1053

• Begen, M.A., Queyranne, M., 2011, "Appointment Scheduling with Discrete Random Durations", Mathematics of Operations Research, May 36(2): 240 - 257.

  Abstract: We consider the problem of determining an optimal appointment schedule for a given sequence of jobs (e.g., medical procedures) on a single processor (e.g., operating room, examination facility, physician), to minimize the expected total underage and overage costs when each job has a random processing duration given by a joint discrete probability distribution. Simple conditions on the cost rates imply that the objective function is submodular and L-convex. Then there exists an optimal appointment schedule which is integer and can be found in polynomial time. Our model can handle a given due date for the total processing (e.g., end of day for an operating room) after which overtime is incurred, and no-shows and some emergencies.

  Link(s) to publication:

  http://dx.doi.org/10.1287/moor.1110.0489

• Santibanez, P., Begen, M.A., Atkins, D., 2007, "Surgical Block Scheduling in a System of Hospitals: An Application to Resource and Wait List Management in a BC Health Authority", Health Care Management Science, September 10(3): 269 - 282.

  Abstract: Scheduling surgical specialties in a medical facility is a very complex process. The choice of schedules and resource availability impact directly on the number of patients treated by specialty, cancellations, wait times, and the overall performance of the system. In this paper we present a systemwide model developed to allow management to explore tradeoffs between OR availability, bed capacity, surgeons' booking privileges, and wait lists. We developed a mixed integer programming model to schedule surgical blocks for each specialty into ORs and applied it to the hospitals in a British Columbia Health Authority, considering OR time availability and post-surgical resource constraints. The results offer promising insights into resource optimization and wait list management, showing that without increasing post-surgical resources hospitals could handle more cases by scheduling specialties differently.

• Begen, M.A., Puterman, M.L., 2003, "Development of a Catch Allocation Tool Design for Production Planning at JS McMillan Fisheries", INFOR, August 41(3): 235 - 244.

  Abstract: JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based commercial fishing, production and distribution company. As the operations of JSM evolved, the process of allocating a commercial salmon catch to a set of final products has become complex and time-consuming. We developed a linear programming based decision support tool to assist JSM management with this allocation decision. The decision support tool yields a production plan that maximizes the profit potential of the catch and allows management to carry out "what if" analyses. Moreover, this paper explores implementation issues such as modeling fish quality deterioration, measuring the effect of by-product and addressing catch-size uncertainty.

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