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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Now showing 1 - 5 of 5
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    PublicationOpen Access
    Purchasing, production, and sales strategies for a production system with limited capacity, fluctuating sales and purchasing prices
    (Taylor _ Francis, 2019) N/A; Department of Business Administration; Karabağ, Oktay; Tan, Barış; Resercher; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600
    In many industries, the revenue and cost structures of manufacturers are directly affected by the volatility of purchasing and sales prices in the markets. We analyze the purchasing, production, and sales policies for a continuous-review discrete material flow production/inventory system with fluctuating and correlated purchasing and sales prices, exponentially distributed raw material and demand inter-arrival times, and processing time. The sales and purchasing prices are driven by the random environmental changes that evolve according to a discrete state space continuous-time Markov process. We model the system as an infinite-horizon Markov decision process under the average reward criterion and prove that the optimal purchasing, production, and sales strategies are state-dependent threshold policies. We propose a linear programming formulation to compute the optimal threshold levels. We examine the effects of the sales price variation, purchasing price variation, correlation between sales and purchasing prices, customer arrival rate and limited inventory capacities on the system performance measures, through a range of numerical experiments. We also examine under which circumstances the use of the optimal policy notably improves the system profit compared to the use of the buy low and sell high naive policy. We show that using the optimal purchasing, production, and sales policies allow manufacturers to improve their profits when the purchasing and sales prices fluctuate.
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    PublicationOpen Access
    Inequity-averse shelter location for disaster preparedness
    (Taylor _ Francis, 2019) Gutjahr, Walter J.; Department of Industrial Engineering; Salman, Fatma Sibel; Hashemian, Mohammadmahdi; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838; N/A
    We study the problem of selecting a set of shelter locations in preparation for natural disasters. Shelters provide victims of a disaster both a safe place to stay and relief necessities such as food, water and medical support. Individuals from the affected population living in a set of population points go to, or are transported to the assigned open shelters. We aim to take both efficiency and inequity into account, thus we minimize a linear combination of: (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them; and (ii) Gini's Mean Absolute Difference of these distances. We develop a stochastic programming model with a set of scenarios that consider uncertain demand and disruptions in the transportation network. A chance constraint is defined on the total cost of opening the shelters and their capacity expansion. In this stochastic context, a weighted mean of the so-called ex ante and ex post versions of the inequity-averse objective function under uncertainty is optimized. Since the model can be solved to optimality only for small instances, we develop a tailored Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem to solve this problem heuristically for larger instances. We compare the performance of the mathematical program and the GA via benchmark instances where the model can be solved to optimality or near optimality. It turns out that the GA yields small optimality gaps in much shorter time for these instances. We run the GA also on Istanbul data to drive insights to guide decision-makers for preparation.
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    PublicationOpen Access
    GoNDEF: an exact method to generate all non-dominated points of multi-objective mixed-integer linear programs
    (Springer, 2019) Department of Industrial Engineering; N/A; Türkay, Metin; Rasmi, Seyyed Amir Babak; Faculty Member; PhD Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A
    Most real-world problems involve multiple conflicting criteria. These problems are called multi-criteria/multi-objective optimization problems (MOOP). The main task in solving MOOPs is to find the non-dominated (ND) points in the objective space or efficient solutions in the decision space. A ND point is a point in the objective space with objective function values that cannot be improved without worsening another objective function. In this paper, we present a new method that generates the set of ND points for a multi-objective mixed-integer linear program (MOMILP). The Generator of ND and Efficient Frontier (GoNDEF) for MOMILPs finds that the ND points represented as points, line segments, and facets consist of every type of ND point. First, the GoNDEF sets integer variables to the values that result in ND points. Fixing integer variables to specific values results in a multi-objective linear program (MOLP). This MOLP has its own set of ND points. A subset of this set establishes a subset of the ND points set of the MOMILP. In this paper, we present an extensive theoretical analysis of the GoNDEF and illustrate its effectiveness on a set of instance problems.
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    PublicationOpen Access
    Recent advances in operations research in computational biology, bioinformatics and medicine
    (EDP Sciences, 2014) Felici, Giovanni; Szachniuk, Marta; Lukasiak, Piotr; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956
    The EURO Working Group on Operations Research in Computational Biology, Bioinformatics and Medicine held its fourth conference in Poznan-Biedrusko, Poland, June 26-28, 2014. The editorial board of RAIRO-OR invited submissions of papers to a special issue on Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine. This special issue includes nine papers that were selected among forty presentations and included in this special issue after two rounds of reviewing.
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    PublicationOpen Access
    Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6
    (EDP Sciences, 2016) Department of Industrial Engineering; N/A; Department of Chemical and Biological Engineering; N/A; Tardu, Mehmet; Rahim, Fatih; Kavaklı, İbrahim Halil; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 40319; 24956
    Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low-and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature.