O. Erhun Kundakcioglu

• Ph.D.
• Professor at Özyeğin University

We study the inventory review policy for a healthcare facility to minimize the impact of inevitable drug shortages. Usually, healthcare facilities do not rely on a single source of supply, and alternative mechanisms are present. When the primary supplier is not available, items are produced in-house or supplied through another supplier, albeit with...

The purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-in...

In this study, we consider a health network that faces uncertain supply disruptions in the form of regional, nationwide, or worldwide drug shortages. Each hospital observes stochastic demand and if the drug is unavailable, patients leave and receive care in another network. As these instances of unavailability diminish the brand value, health netwo...

Fare allocation for legs and O&D pairs plays a crucial role in airline revenue management. Despite a large number of dynamic pricing studies, there are only a few widely adopted studies in which assumptions affect most tactical decisions with potentially large impacts on airline profitability. These decisions involve approximating future pricing sc...

Decomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and tre...

There are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp-based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to chall...

We study a workforce scheduling problem faced in contact centers with considerations on a fair distribution of shifts in compliance with agent preferences. We develop a mathematical model that aims to minimize operating costs associated with labor, transportation of agents, and lost customers. Aside from typical work hour-related constraints, we al...

In this paper, we consider a store that sells two vertically differentiated items that might substitute each other. These items do not only differ in quality and price, but they also target two different customer segments. Items deteriorate over time and might require price adjustments to avoid cannibalization. We provide closed-form solutions for...

This paper considers a k-out-of-n system that has just failed. There is an associated cost of testing each component. In addition, we have apriori information regarding the probabilities that a certain set of components is the reason for the failure. The goal is to identify the subset of components that have caused the failure with the minimum expe...

Background: Sustained efforts at preventing diabetic foot ulcers (DFUs) and subsequent leg amputations are sporadic in most health care systems despite the high costs associated with such complications. We sought to estimate effectiveness targets at which cost-savings (i.e. improved health outcomes at decreased total costs) might occur. Methods:...

In this paper, we present a review and analysis of studies that focus on humanitarian inventory planning and management. Specifically, we focus on papers which develop policies and models to determine how much to stock, where to stock, and when to stock throughout the humanitarian supply chain. We categorize papers according to the disaster managem...

We introduce an economic order quantity model that incorporates product assortment, pricing and space-allocation decisions for a group of perishable products. The goal is to maximize the retailer’s profit under shelf-space and backroom storage capacity constraints. We assume that the demand rate of a product is a function of the selling prices and...

Despite the importance and value of the pharmaceutical market, a significant portion of procurement spending including pharmaceuticals are lost. Coupling poor and reactive management practices with the inevitable national drug shortages, leads to lack of medicines causing patient suffering and direct life or death consequences. In this paper, we pr...

Exceptional opportunities exist for researchers and practitioners to invest in conducting innovative and transformative research in data mining and health informatics. This IEEE Intelligent Systems "Trends and Controversies" (T&C) department hopes to raise awareness and highlight recent research to move toward such goals. The introduction, "Healthc...

This paper presents the multiple instance classification problem that can be used for drug and molecular activity prediction, text categorization, image annotation, and object recognition. In order to model a more robust representation of outliers, hard margin loss formulations that minimize the number of misclassified instances are proposed. Altho...

This chapter presents data mining techniques that are formulated as combinatorial optimization problems together with their applications. There are a number of cases where fundamental data mining tool is not combinatorial in nature, yet widely used special-purpose combinatorial extensions exist. For the sake of completeness, these fundamental tools...

In this study, a layout optimization framework for offshore wind farms is proposed under widely accepted assumptions. Although wind power meets sustainable electricity standards and has less environmental impact than conventional sources, onshore wind farms currently supply only 3% of the nation's electricity. Nevertheless, onshore wind farms avoid...

Natural gas is one of the most abundant sources of energy in the United States. Being treated as a commodity, natural gas price is constantly subject to fluctuations. Forecasting energy prices has become one of the major goals in the industry due to its potential economical benefits. An accurate natural gas price prediction model is useful for manu...

With the rapid development of nanotechnology products, there is a significant concern on the adverse effects that might be associated with them. Traditional biological assays are typically used to asses the toxicity in vitro. There are, however, questions regarding the suitability of these assays for this purpose, mainly due to the potential intera...

In this paper, we consider the classification problem within the multiple instance learning (MIL) context. Training data is composed of labeled bags of instances. Despite the large number of margin maximization based classification methods, there are only a few methods that consider the margin for MIL problems in the literature. We first formulate...

In this study we introduce a generalized support vector classification problem: Let X i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x i,k , k=1,…,|X i | have the same class label y i . Select only one pattern vector from each set X i such that the margin between the set of selected positive and negat...

In the present study, Raman spectroscopy is employed to assess the potential toxicity of chemical substances. Having several advantages compared to other traditional methods, Raman spectroscopy is an ideal solution for investigating cells in their natural environment. In the present work, we combine the power of spectral resolution of Raman with on...

Biclustering is simultaneous classification of the samples and features in a way that samples from the same class have similar values for that class' characteristic features. A biclustering is consistent if in each sample (feature) from any set, the average expression of features (samples) that belong to the same class is greater than the average e...

This survey concerns applications of mathematical programming in the context of classification. We mainly discuss two supervised learning methods: Support Vector Machines (SVMs) and consistent biclustering together with their extensions. We also refer to some recently proposed classification techniques that utilize optimization theory.

We introduce a generalized support vector classiflcation problem: Let Xi, i = 1;:::;n be mutually exclusive sets of pattern vectors such that all pattern vectors xi;k, k = 1;:::;jXij have the same class label yi 2 f¡1;+1g. Select only one pattern vector xi;k⁄ from each set Xi such that the margin be- tween the set of selected positive and negative...

Introduction Extensions Multiple-Resource Generalized Assignment Problem Multilevel Generalized Assignment Problem Dynamic Generalized Assignment Problem Bottleneck Generalized Assignment Problem Generalized Assignment Problem with Special Ordered Set Stochastic Generalized Assignment Problem Bi-Objective Generalized Assignment Problem Generalized...

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier’s ability to influence demand characteristics can lead to an improved match between supply and demand. This paper presents a class of opti...

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as a test-sequencing problem, is considered. The traditional test-sequencing problem is generalized here to include asymmetrical tests. In general, the next test to execute depends on the results of previous tests...

In this paper, we design a new heuristic for an important extension of the minimum power multicasting problem in ad hoc wireless networks 20,21 . Assuming that each transmission takes a fixed amount of time, we impose constraints on the number of hops allowed to reach the destination nodes in the multicasting application. This setting would be appl...

Multiple instance learning (MIL) is a growing field of research under machine learning. A typical MIL problem consists of multiple sets of observations, where each set is referred to as a bag. Each bag belongs to a positive or a negative class. MIL is mainly associated with the classification of the positive and negative bags. In this paper, we fir...