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.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.ArticlePublication Metadata only Electric bus fleet scheduling under travel time and energy consumption uncertainty(Elsevier, 2023-11) Avishan, Farzad; Yanıkoğlu, İhsan; Alwesabi, Y.; Industrial Engineering; YANIKOĞLU, Ihsan; Avishan, FarzadThe public transportation system is experiencing a substantial shift due to the rapid expansion of electromobility infrastructure and operations. This transformation is anticipated to contribute to decarbonizing and promoting environmental sustainability significantly. Among the most pressing planning issues in this area is the optimization of operational and strategic costs associated with electric fleets, which has recently garnered the attention of researchers. This paper investigates the scheduling and procurement problem of electric fleets under travel time and energy consumption uncertainty. A novel mixed-integer linear programming model is proposed, which determines the number of buses required to cover all trips, yields the schedule of the trips, and creates bus charging plans. The robust optimization paradigm is employed to address uncertainty, and a new budget uncertainty set is introduced to control the robustness of the solution. The efficiency of the model is evaluated through an extensive Monte Carlo simulation. Additionally, a case study is conducted on the off-campus college transport network at Binghamton University to demonstrate the real-world applicability of the model. The numerical results have shown that ignoring uncertainty can lead to schedules where up to 48% of the trips are affected, which are either delayed or missed. The proposed approach can also be applied to other transportation networks with similar characteristics and uncertainties.ReviewPublication Metadata only A survey of adjustable robust optimization(Elsevier, 2019-09) Yanıkoğlu, İhsan; Gorissen, B. L.; Hertog, D. den; Industrial Engineering; YANIKOĞLU, IhsanStatic robust optimization (RO) is a methodology to solve mathematical optimization problems with uncertain data. The objective of static RO is to find solutions that are immune to all perturbations of the data in a so-called uncertainty set. RO is popular because it is a computationally tractable methodology and has a wide range of applications in practice. Adjustable robust optimization (ARO), on the other hand, is a branch of RO where some of the decision variables can be adjusted after some portion of the uncertain data reveals itself. ARO generally yields a better objective function value than that in static robust optimization because it gives rise to more flexible adjustable (or wait-and-see) decisions. Additionally, ARO also has many real life applications and is a computationally tractable methodology for many parameterized adjustable decision variables and uncertainty sets. This paper surveys the state-of-the-art literature on applications and theoretical/methodological aspects of ARO. Moreover, it provides a tutorial and a road map to guide researchers and practitioners on how to apply ARO methods, as well as, the advantages and limitations of the associated methods.ArticlePublication Metadata only Branch-and-price approach for robust parallel machine scheduling with sequence-dependent setup times(Elsevier, 2022-09-16) Yanıkoğlu, İhsan; Yavuz, Tonguç; Industrial Engineering; YANIKOĞLU, Ihsan; Yavuz, TonguçThis paper studies a machine scheduling problem that minimizes the worst-case total tardiness for unrelated parallel machines with sequence-dependent setup and uncertain processing times. We propose a robust optimization reformulation of the related machine scheduling problem and discuss several important properties of the mathematical model and the reformulation approach. The proposed model generalizes robust parallel machine scheduling problems by including sequence-dependent setup times and ellipsoidal uncertainty sets. Another key contribution of the paper is to show that scheduling problems usually have alternative optimal solutions for the worst-case tardiness objective, whose performance under nominal processing times may vary or vice a versa. This issue has been addressed by studying the Pareto efficient extensions of the proposed robust optimization models to provide solutions that are immune to changes in processing times. A branch-and-price algorithm has been developed to solve realistically sized instances in less than one hour, which a commercial solver cannot achieve. Numerical results show the effectiveness of the proposed approach since realistically sized instances such as (4 machines, 32 jobs) and (150 machines, 300 jobs) can be solved to optimality within the time limit, and the (average) objective function value improvement made by the robust approach can get as high as 56% compared with the (nominal) optimal solutions that ignore uncertainty in problem data.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 Open Access Robust reformulations of ambiguous chance constraints with discrete probability distributions(Balikesir University, 2019) Yanıkoğlu, İhsan; Industrial Engineering; YANIKOĞLU, IhsanThis paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a so-called ``p-box'' or ``p-ellipsoidal'' uncertainty set. Using the robust optimization paradigm, the deterministic counterparts of the ambiguous chance constraints are reformulated as mixed-integer programming problems which can be tackled by commercial solvers for moderate sized instances. For larger sized instances, we propose a safe approximation algorithm that is computationally efficient and yields high quality solutions. The associated approach and the algorithm can be easily extended to joint chance constraints, nonlinear inequalities, and dependent data without introducing additional mathematical optimization complexity to that of the original robust reformulation. In numerical experiments, we first present our approach over a toy-sized chance constrained knapsack problem. Then, we compare optimality and computational performances of the safe approximation algorithm with those of the exact and the randomized approaches for larger sized instances via Monte Carlo simulation.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.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.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.