İhsan Yanıkoğlu

İhsan Yanıkoğlu
Ozyegin University · Department of Industrial Engineering

Ph.D.

About

18
Publications
14,109
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527
Citations
Introduction
I currently work at the Department of Industrial Engineering, Ozyegin University, İstanbul, Turkey. My research focuses on optimization under uncertainty and its applications. Applications generally involve engineering designs of technological products, logistics, smart energy systems, and (re)manufacturing.
Education
November 2013 - March 2014
September 2010 - September 2014
Tilburg University
Field of study
  • Operations Research
September 2009 - September 2010
Tilburg University
Field of study
  • Operations Research

Publications

Publications (18)
Article
Full-text available
We study two-stage stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version and upper bounds on a pessimistic version of the leader's problem. Both bounding probl...
Article
Full-text available
This 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...
Article
Full-text available
Static 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 compu-tationally tractable methodology and has a wide range of applications i...
Article
Full-text available
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach uses the available historical data for the uncertain parameters and is based on goodness-of-fit statistics. It guarantees that the probability the uncertain constraint holds is at least the prescribed value. Compared to existing safe approximation meth...
Article
Full-text available
This 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...
Article
Full-text available
This 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 th...
Article
In 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 gr...
Article
We focus on rapid needs assessment operations conducted immediately after a disaster to identify the urgent needs of the affected community groups, and address the problem of selecting the sites to be visited by the assessment teams during a fixed assessment period and constructing assessment routes under travel time uncertainty. Due to significant...
Article
Full-text available
This 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 pr...
Preprint
Full-text available
The aim of this study is to increase the luminance performance of direct light emitting diodes (DLED) televisions through case design improvement. We first gather luminance measurements of several regions of active area displays for different DLED models, along with mechanical parameters such as distance to active area from a point of cross section...
Preprint
Full-text available
There may be significant uncertainties in transportation network conditions after a disaster, and only rough information on travel times is usually available while planning disaster response operations. In this study, we focus on rapid needs assessment operations conducted immediately after a disaster to investigate the conditions of different affe...
Preprint
Full-text available
This paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a box or an ellipsoidal uncertainty set. Using the robust optimization paradigm, the deterministic counterparts of the (ambiguous) chance constraints are reformulated as mixed-integer programming problem...
Chapter
Full-text available
In this chapter, the authors give a brief introduction to important concepts of RO paradigm. The remainder of the chapter is organized as follows: Section 2 gives an introduction on optimization under uncertainty, and presents brief comparisons among the well-known sub-fields of optimization under uncertainty such as RO, stochastic programming (SP)...
Preprint
Full-text available
This paper surveys decision rule approaches proposed in the realm of adaptive multi-stage robust optimization, and gives a guideline to derive adaptive robust counterparts for different classes of decision rules using modern optimization techniques. We also prose a class of nonlinear decision rules and derive their tractable robust counterparts. De...

Projects

Project (1)
Archived project
Multiple publications in the realm of Robust and Stochastic Optimization, some of them are listed below: 1) Decision Rule Bounds for Two Stage Stochastic Bilevel Programs 2) Adjustable Robust Optimization - A Survey and Tutorial 3) A Tutorial on Deriving Robust Counterparts in Adaptive Robust Optimization For the complete list of ongoing and published research please see: http://ihsanyanikoglu.com/publication.html http://ihsanyanikoglu.com/work-in-progress.html