Person: YANIKOĞLU, Ihsan
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Ihsan
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YANIKOĞLU
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ArticlePublication Metadata only A column generation-based approach for the adaptive stochastic blood donation tailoring problem(Taylor & Francis, 2023) Elyasi, Milad; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Dolgui, A.; Industrial Engineering; ÖZENER, Okan Örsan; YANIKOĞLU, Ihsan; EKİCİ, Ali; Elyasi, MiladManaging blood donations is a challenging problem due to the perishability of blood, limited donor pool, deferral time restrictions, and demand uncertainty. The problem addressed here combines two important aspects of blood supply chain management: the inventory control of blood products and the donation schedule. We propose a stochastic scenario-based reformulation of the blood donation management problem that adopts multicomponent apheresis and utilises donor pool segmentation into here-and-now and wait-and-see donors. We propose a flexible donation scheme that is resilient against demand uncertainty. This scheme enables more flexible donation schedules because wait-and-see donors may adjust their donation schedules according to the realised values of demand over time. We propose a column generation-based approach to solve the associated multi-stage stochastic donation tailoring problem. The numerical results show the effectiveness of the proposed optimisation model, which provides solutions with less than a 7% optimality gap on average with respect to a lower bound. It also improves the operational cost of the standard donation scheme that does not use wait-and-see donors by more than 18% on average. Utilising multicomponent apheresis and flexible wait-and-see donations are suggested for donation organisations because they yield significant cost reductions and resilient donation schedules.Conference ObjectPublication Metadata only Load dependent lead time modelling: a robust optimization approach(IEEE, 2018-01-04) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, IhsanAlthough production planning models using nonlinear CFs have shown promising results for semiconductor wafer fabrication facilities, the lack of an effective methodology for estimating the CFs is a significant obstacle to their implementation. Current practice focuses on developing point estimates using least-squares regression approaches. This paper compares the performance of a production planning model using a multi-dimensional CF and its robust counterpart under several experimental settings. As expected, as the level of uncertainty is increased, the resulting production plan deviates from the optimal solution of the deterministic model. On the other hand, production plans found using the robust counterpart are less vulnerable to parameter estimation errors.Conference ObjectPublication Metadata only Capacitated stochastic lot-sizing and production planning problem under demand uncertainty(Elsevier, 2022) Seyfishishavan, Seyed Amin; Yılmaz, Görkem; Yanıkoğlu, İhsan; Industrial Engineering; YILMAZ, Görkem; YANIKOĞLU, Ihsan; Seyfishishavan, Seyed AminThis paper proposes two multi-period, multi-item capacitated stochastic lot-sizing problems under demand uncertainty. We model uncertainty via a scenario tree. The first model considers production, inventory, backlogging, line status, and worker group assignment decisions, where inventory and backlogging decisions have wait-and-see structure. The second model converts line status and worker group assignment decisions to the wait-and-see structure. Also, the second model enables us to take corrective extra-ordering decisions using scenario-based wait-and-see decisions. Numerical results compare the optimality and CPU time performances of two models and solution approaches using a data set inspired by a real-life electronics company.Conference ObjectPublication Metadata only A robust optimization approach for production planning under exogenous planned lead times(IEEE, 2019) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, IhsanMany production planning models applied in semiconductor manufacturing represent lead times as fixed exogenous parameters. However, in reality, lead times must be treated as realizations of released lots' cycle times, which are in fact random variables. In this paper, we present a distributionally robust release planning model that allows planned lead time probability estimates to vary over a specified ambiguity set. We evaluate the performance of non-robust and robust approaches using a simulation model of a scaled-down wafer fabrication facility. We examine the effect of increasing uncertainty in the estimated lead time parameters on the objective function value and compare the worst-case, average optimality, and feasibility of the two approaches. The numerical results show that the average objective function value of the robust solutions are better than that of the nominal solution by a margin of almost 20% in the scenario with the highest uncertainty level.ArticlePublication Open Access A practical guide to robust optimization(Elsevier, 2015-06) Gorissen, B. L.; Yanıkoğlu, İhsan; Hertog, D. den; Industrial Engineering; YANIKOĞLU, IhsanRobust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.ArticlePublication Metadata only Robust parameter design and optimization for quality engineering(Springer, 2022-03) Yanıkoğlu, İhsan; Albey, Erinç; Okçuoğlu, S.; Industrial Engineering; YANIKOĞLU, Ihsan; ALBEY, ErinçThis paper proposes a methodology to determine the optimal settings of key decision variables that affect the resilience of an engineering design against uncertainty. Uncertainty in quality engineering is often caused by environmental factors, and scarcity of data due to limitations in the experimentation phase amplifies the level of ambiguity. The proposed robust parameter design and optimization approach utilizes the Taguchi method to find critical variables to be used in the optimization, and it utilizes robust optimization to immunize the obtained solution against uncertainty. To demonstrate our approach, we focus on design optimization of an injection molding product, a refrigerator door cap, made from thermoplastic raw material and its key quality characteristic, warpage. The near-optimal designs found by the robust parameter design and optimization approach are implemented in a real-life manufacturing environment. The numerical experiments show that the new designs significantly improve the warpage quality characteristic and the total production cycle time compared to the current design used in the manufacturing company.ArticlePublication Metadata only Robust strategic planning of dynamic wireless charging infrastructure for electric buses(Elsevier, 2022-02-01) Alwesabi, Y.; Avishan, Farzad; Yanıkoğlu, İhsan; Liu, Z.; Wang, Y.; Industrial Engineering; AVISHAN, Farzad; YANIKOĞLU, IhsanElectromobility in public bus systems is growing rapidly and experiencing a fundamental transformation in their infrastructure and operations. The dilemma of limited driving range and charging time of battery electric buses (BEBs) hinders their adoption. A novel approach to address BEB limitations is to utilize dynamic wireless charging (DWC) technology that allows buses to charge while in motion. This paper aims to analyze robust strategic planning of DWC and BEB fleet scheduling based on a real bus network at Binghamton University. The problem is first formulated as a new deterministic mixed-integer linear programming model to simultaneously optimize both the charging planning problem and fleet scheduling problem in an integrated fashion. To address the uncertainty of energy demand and charging time, a robust counterpart model (RCM) has been derived. To increase RCM flexibility, the battery status variable is formulated in a cumulative form. Dependent and independent budget uncertainty sets have been developed to control the robustness. A sensitivity analysis has been conducted to study the system behavior in response to different charging types, auxiliary energy demand, depth of discharge, charging options at terminals, battery degradation, and electricity cost. The deterministic model shows that eight homogeneous BEBs are required to operate on the selected routes with a battery capacity of 54.01 kWh and a total cost of $3,636,347. The results show that joint planning of charging infrastructure and fleet scheduling can save 19.2% of total cost compared to disjoint planning. The RCM results in 10 BEBs to ensure feasiblility against uncertainty.ArticlePublication Metadata only The value of quality grading in remanufacturing under quality level uncertainty(Taylor & Francis, 2021) Yanıkoğlu, İhsan; Denizel, M.; Industrial Engineering; YANIKOĞLU, IhsanIn remanufacturing, variability in quality levels of available cores (end-of-life products) has an impact on both the process cost and the process time. While previous research suggests that quality grading adds value, there are also concerns raised regarding how reliably the grades can be identified. We argue that uncertainty is inherent to the grading process and investigate the value of grading by taking into account the underlying uncertainty. We develop a robust optimisation model for remanufacturing planning, where both the per-unit cost and resource requirement to remanufacture a core are uncertain parameters that are assumed to reside in two different uncertainty sets; box and ellipsoidal. We analyse both uncapacitated and capacitated cases, and based on extensive numerical analysis, conclude that while on average, there is still value in grading, it becomes significantly smaller when the inherent uncertainty is accounted for. For the capacitated case, we also consider a cost for grading and find that it may cause a significant deterioration in the value of grading, if not rendering the grading totally useless. We show the validity of our approach through extensive numerical analyses.ArticlePublication Metadata only Robust dual response optimization(Taylor & Francis, 2016) Yanıkoğlu, İhsan; Hertog, D. den; Kleijnen, J. P. C.; Industrial Engineering; YANIKOĞLU, IhsanThis article presents a robust optimization reformulation of the dual response problem developed in response surface methodology. The dual response approach fits separate models for the mean and the variance, and analyzes these two models in a mathematical optimization setting. We use metamodels estimated from experiments with both controllable and environmental inputs. These experiments may be performed with either real or simulated systems; we focus on simulation experiments. For the environmental inputs, classic approaches assume known means, variances or covariances, and sometimes even a known distribution. We, however, develop a method that uses only experimental data, so it does not need a known probability distribution. Moreover, our approach yields a solution that is robust against the ambiguity in the probability distribution. We also propose an adjustable robust optimization method that enables adjusting the values of the controllable factors after observing the values of the environmental factors. We illustrate our novel methods through several numerical examples, which demonstrate their effectiveness.ArticlePublication Metadata only Humanitarian relief distribution problem: an adjustable robust optimization approach(Informs, 2023-07) Avishan, Farzad; Elyasi, Milad; Yanıkoğlu, İhsan; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; AVISHAN, Farzad; YANIKOĞLU, Ihsan; EKİCİ, Ali; ÖZENER, Okan Örsan; Elyasi, MiladManagement of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and resources, and they must provide equitable distribution of supplies to affected people. This paper proposes an adjustable robust optimization approach for the associated humanitarian logistics problem. The approach creates routes for relief logistics teams and decides the service times of the visited sites to distribute relief supplies by taking the uncertainty in travel times into account. The associated model allows relief logistics teams to adjust their service decisions according to the revealed information during the process. Hence, our solutions are robust for the worst-case realization of travel times, but still more flexible and less conservative than those of static robust optimization. We propose novel reformulation techniques to model these adjustable decisions. The resulting models are computationally challenging optimization problems to be solved by exact methods, and, hence, we propose heuristic algorithms. The state-of-the-art heuristic, which is based on clustering and a dedicated decision-rule algorithm, yields near-optimal results for medium-sized instances and is scalable even for large-sized instances. We have also shown the effectiveness of our approach in a case study using a data set obtained from an earthquake that hit the Van province of Turkey in 2011.