Browsing by Author "Karsu, Özlem"

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Open Access
A new formulation and an effective matheuristic for the airport gate assignment problem
(Elsevier, 2023-03) Karsu, Özlem; Solyalı, Oğuz
This study considers an airport gate assignment problem where a set of aircraft arriving to an airport are assigned to the fixed gates of the airport terminal or to the apron. The aim is to lexicographically minimize the number of aircraft assigned to the apron, and then the total walking distance by passengers. A new mixed integer linear programming formulation and a matheuristic is proposed for the problem. The proposed formulation is based on the idea of flow of passengers and has smaller size compared to the existing formulations in the literature. The proposed matheuristic, which relies on solving a restricted version of the proposed formulation of the problem, is not only easy to implement but is also very effective. A computational study performed on benchmark instances reveals that the proposed formulation and the matheuristic outperform the existing exact and heuristic algorithms in the literature.
Open Access
Balance in resource allocation problems: a changing reference approach
(Springer, 2020) Karsu, Özlem; Erkan, Hale
Fairness is one of the primary concerns in resource allocation problems, especially in settings which are associated with public welfare. Using a total benefit-maximizing approach may not be applicable while distributing resources among entities, and hence we propose a novel structure for integrating balance into the allocation process. In the proposed approach, imbalance is defined and measured as the deviation from a reference distribution determined by the decision-maker. What is considered balanced by the decision-maker might change with respect to the level of total output distributed. To provide an allocation policy that is in line with this changing structure of balance, we allow the decision-maker to change her reference distribution depending on the total amount of output (benefit). We illustrate our approach using a project portfolio selection problem. We formulate mixed integer mathematical programming models for the problem with maximizing total benefit and minimizing imbalance objectives. The bi-objective models are solved with both the epsilon-constraint method and an interactive algorithm.
Open Access
Bi‐objective optimization of a grid‐connected decentralized energy system
(John Wiley and Sons, 2018) Altıntaş, Onur; Ökten, Büşra; Karsu, Özlem; Kocaman, Ayşe Selin
Motivated by the increasing transition from fossil fuel-based centralized systems to renewable energy-based decentralized systems, we consider a bi-objective investment planning problem of a grid-connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon-intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation-optimization algorithm and then adapt multi-objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi-objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well-known measures from the literature.
Open Access
Capturing preferences for inequality aversion in decision support
(Elsevier, 2018-01-16) Karsu, Özlem; Morton, A.; Argyris, N.
We investigate the situation where there is interest in ranking distributions (of income, of wealth, of health, of service levels) across a population, in which individuals are considered preferentially indistinguishable and where there is some limited information about social preferences. We use a natural dominance relation, generalised Lorenz dominance, used in welfare comparisons in economic theory. In some settings there may be additional information about preferences (for example, if there is policy statement that one distribution is preferred to another) and any dominance relation should respect such preferences. However, characterising this sort of conditional dominance relation (specifically, dominance with respect to the set of all symmetric increasing quasiconcave functions in line with given preference information) turns out to be computationally challenging. This challenge comes about because, through the assumption of symmetry, any one preference statement (“I prefer giving $100 to Jane and $110 to John over giving $150 to Jane and $90 to John”) implies a large number of other preference statements (“I prefer giving $110 to Jane and $100 to John over giving $150 to Jane and $90 to John”; “I prefer giving $100 to Jane and $110 to John over giving $90 to Jane and $150 to John”). We present theoretical results that help deal with these challenges and present tractable linear programming formulations for testing whether dominance holds between any given pair of distributions. We also propose an interactive decision support procedure for ranking a given set of distributions and demonstrate its performance through computational testing.
Open Access
Clean water network design for refugee camps
(Springer, 2021-03) Karsu, Özlem; Yetiş Kara, Bahar; Akkaya, Elif; Ozel, Aysu
Motivated by the recent rise in need for refugee camps, we address one of the key infrastructural problems in the establishment process: The clean water network design problem. We formulate the problem as a biobjective integer programming problem and determine the locations of the water source, water distribution units and the overall network design (pipelines), considering the objectives of minimizing cost (total network length) and maximizing accessibility (total walking distance) simultaneously. We solve the resulting model using exact and heuristic approaches that find the set (or a subset) of Pareto solutions and a set of approximate Pareto solutions, respectively. We demonstrate the applicability of our approach on a real-life problem in Gaziantep refugee camp and provide a detailed comparison of the solution approaches. The novel biobjective approach we propose will help the decision makers to make more informed design decisions in refugee camps, considering the trade-off between the two key criteria of cost and accessibility.
Open Access
Ensuring multidimensional equality in public service
(Elsevier Ltd, 2021-10-23) Akoluk, Damla; Karsu, Özlem
Service planning problems typically involve decisions that lead to the distribution of multiple benefits to multiple users, and hence include equality and efficiency concerns in a multidimensional way. We develop two mathematical modeling-based approaches that incorporate these concerns in such problems. The first formulation aggregates the multidimensional efficiency and equality (equitability) concerns in a biobjective model. The second formulation defines an objective function for each benefit, which maximizes the total social welfare obtained from that specific benefit distribution; this results in an n-objective model, where n is the number of benefits. We illustrate and compare these approaches on an example public service provision problem.
Open Access
Equitable decision making approaches over allocations of multiple benefits to multiple entities
(Elsevier, 2018) Kaynar, N.; Karsu, Özlem
In this study, we develop decision support tools for policy makers that will help them make choices among a set of allocation alternatives. We assume that alternatives are evaluated based on their benefits to different users and that there are multiple benefit (output) types to consider. We assume that the policy maker has both efficiency (maximizing total output) and equity (distributing outputs across different users as fair as possible) concerns. This problem is a multicriteria decision making problem where the alternatives are represented with matrices rather than vectors. We develop interactive algorithms that guide a policy maker to her most preferred solution, which are based on utility additive (UTA) and convex cone methods. Our computational experiments demonstrate the satisfactory performance of the algorithms. We believe that such decision support tools may be of great use in practice and help in moving towards fair and efficient allocation decisions.
Open Access
Eşitlikçi çok amaçlı sırt çantası problemi
(Gazi Üniversitesi Fen Bilimleri Enstitüsü, 2018) Karsu, Özlem
Bu çalışmada, eşitlikçi kaygıların olduğu kaynak dağıtımı problemi için kullanılabilecek, çok amaçlı matematiksel modelleme yaklaşımı geliştirilmiştir. Karar vericinin eşitlikçi tercih ilişkisine sahip olduğu varsayılmış ve eşitlikçi Pareto çözümler bulunması amaçlanmıştır. Eşitlikçi Pareto çözüm kümesinin bulunması için, problemdeki eşitlikçi kaygıları gözönüne alarak tasarlanmış, eşitlikçi Pareto çözümler vermeyecek durum vektörlerini alt ve üst sınırlar kullanarak eleyen, bir dinamik programlama algoritması önerilmiştir. Bu algoritmada, yazında önerilen alt sınırlara ek olarak yeni bir alt sınır mekanizması kullanılmış ve etkililiği gösterilmiştir. Dinamik programlama algoritması, epsilon kısıt yöntemi ile iki amaçlı problemler için karşılaştırılmıştır. Ayrıca, üç amaçlı problemler için epsilon kısıt yöntemi sonuçları verilmiştir.
Open Access
An exact algorithm for biobjective integer programming problems
(Elsevier Ltd, 2021-08) Doğan, Saliha Ferda; Karsu, Özlem; Ulus, Firdevs
We propose an exact algorithm for solving biobjective integer programming problems, which arise in various applications of operations research. The algorithm is based on solving Pascoletti-Serafini scalarizations to search specified regions (boxes) in the objective space and returns the set of nondominated points. We implement the algorithm with different strategies, where the choices of the scalarization model parameters and splitting rule differ. We then derive bounds on the number of scalarization models solved; and demonstrate the performances of the variants through computational experiments both as exact algorithms and as solution approaches under time restriction. The experiments demonstrate that different strategies have advantages in different aspects: while some are quicker in finding the whole set of nondominated solutions, others return good-quality solutions in terms of representativeness when run under time restriction. We also compare the proposed approach with existing algorithms. The results of our experiments show the satisfactory behaviour of our algorithm, especially when run under time limit, as it achieves better coverage of the whole frontier with a smaller number of solutions compared to the existing algorithms.
Open Access
An exact algorithm for the minimum squared load assignment problem
(Elsevier, 2019) Karsu, Özlem; Azizoglu, M.
In this study, we consider an assignment problem with the objective to minimize the sum of squared loads over all agents. We provide mixed integer nonlinear and linear programming formulations of the problem and present a branch and bound algorithm for their solution. The results of our computational experiment have shown the satisfactory behavior of our branch and bound algorithm.
Open Access
Exact and heuristic solution approaches for the airport gate assignment problem
(Elsevier, 2021-01-30) Karsu, Özlem; Azizoğlu, M.; Alanlı, K.
In this study, we consider an airport gate assignment problem that assigns a set of aircraft to a set of gates. The aircraft that cannot be assigned to any gate are directed to an apron. We aim to make aircraft-gate assignments so as to minimize the number of aircraft assigned to apron and among the apron usage minimizing solutions, we aim to minimize total walking distance travelled by all passengers. The problem is formulated as a mixed-integer nonlinear programming model and then it is linearized. A branch and bound algorithm, beam search and filtered beam search algorithms that employ powerful lower and upper bounding mechanisms are developed. The results of the computational experiment have shown the satisfactory performance of the algorithms.
Open Access
Fair allocation of personal protective equipment to health centers during early phases of a pandemic
(Elsevier, 2022-05) Dönmez, Zehranaz; Turhan, S.; Karsu, Özlem; Kara, Bahar Y.; Karaşan, Oya
We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number of infected health workers, the number of infected patients and minimizing a deprivation cost function defined over shortages. We solve the resulting problem using the ε-constraint algorithm so as to obtain the exact Pareto set. We also develop a customized genetic algorithm to obtain an approximate Pareto frontier in reasonable time for larger instances. We provide a comparative analysis of the exact and heuristic methods under various scenarios and give insights on how the suggested allocations outperform the ones obtained through a set of rule-of-thumb policies, policies that are implemented owing to their simplicity and ease-of-implementation. Our comparative analysis shows that as the circumstances get worse, the trade-off between the deprivation cost and the ratio of infections deepens and that the proposed heuristic algorithm gives very close solutions to the exact Pareto frontier, especially under pessimistic scenarios. We also observed that while some rule-of-thumb policies such as a last-in-first-receives type policy work well in terms of deprivation costs in optimistic scenarios, others like split policies perform well in terms of number of infections under neutral or pessimistic settings. While favoring one of the objectives, these policies typically fail to provide good solutions in terms of the other objective; hence if such policies are to be implemented the choice would depend on the problem characteristics and the priorities of the policy makers. Overall, the solutions obtained by the proposed methods imply that more complicated distribution schemes that are not induced by these policies would be needed for best results.
Open Access
Fair resource allocation: Using welfare-based dominance constraints
(Elsevier, 2022-03-01) Argyris, N.; Karsu, Özlem; Yavuz, Mirel
In this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem.
Open Access
Humanitarian facility location under uncertainty: Critical review and future prospects
(Elsevier, 2021-01-08) Dönmez, Zehranaz; Yetiş Kara, Bahar; Karsu, Özlem; Saldanha-da-Gama, F.
This paper provides a comprehensive review of the research done on facility location problems under uncertainty in a humanitarian context. The major goal is to summarize and help structuring this topic, which has increasingly attracted the attention of the scientific community. The literature is reviewed from different perspectives namely, in terms of the type of facilities involved, the decisions to make, the criteria to optimize, the paradigm used for capturing uncertainty, and the solution method adopted. The detailed analysis provided in the manuscript also contributes to identifying the distinguishing features of the problems in the topic. An outcome of the state-of-the-art presented is the identification of the current research trends, expectations and holes in the existing knowledge thus highlighting relevant research directions.
Open Access
In Press, Corrected Proof: Ensuring multidimensional equality in public service
(Elsevier, 2021-10-23) Akoluk, Damla; Karsu, Özlem
Service planning problems typically involve decisions that lead to the distribution of multiple benefits to multiple users, and hence include equality and efficiency concerns in a multidimensional way. We develop two mathematical modeling-based approaches that incorporate these concerns in such problems. The first formulation aggregates the multidimensional efficiency and equality (equitability) concerns in a biobjective model. The second formulation defines an objective function for each benefit, which maximizes the total social welfare obtained from that specific benefit distribution; this results in an n-objective model, where n is the number of benefits. We illustrate and compare these approaches on an example public service provision problem.
Open Access
Operational research: methods and applications
(Taylor and Francis Ltd., 2023-12-27) Petropoulos, F.; Laporte, G.; Aktas, E.; Alumur, S.; Archetti, C.; Ayhan, H.; Battarra, M.; Bennell, J.; Bourjolly, J.; Boylan, J. E.; Breton, M.; Canca, D.; Charlin, L.; Chen, B.; Cicek, C.; Cox, L.; Currie, C. S.M.; Demeulemeester, E.; Ding, L.; Disney, S. M.; Ehrgott, M.; Eppler, M. J.; Erdoğan, G.; Fortz, B.; Franco, L. A.; Frische, J.; Greco, S.; Gregory, A. J.; Hämäläinen, R. P.; Herroelen, W.; Hewitt, M.; Holmström, J.; Hooker, J. N.; Işık, T.; Johnes, J.; Kara, B. Y.; Karsu, Özlem; Kent, K.; Köhler, C.; Kunc, M.; Kuo, Y.; Letchford, A. N.; Leung, J.; Li, D.; Li, H.; Lienert, J.; Ljubić, I.; Lodi, A.; Lozano, S.; Lurkin, V.; Martello, S.; McHale, I. G.; Midgley, G.; Morecroft, J. D.W.; Mutha, A.; Oğuz, C.; Petrovic, S.; Pferschy, U.; Psaraftis, H. N.; Rose, S.
Throughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.
Open Access
Planning sustainable routes: Economic, environmental and welfare concerns
(Elsevier BV, 2021-10-09) Dükkancı, O.; Karsu, Özlem; Kara, Bahar Y.
We introduce a problem called the Sustainable Vehicle Routing Problem (SVRP) in which the sustainability notion is considered in terms of economic, environmental and social impacts. Inspired by real-world problems that large cargo companies face for their delivery decisions, we introduce a new facet to the classical vehicle routing problem by considering the welfare of all three stakeholders of the problem: an environmentally conscious company, the drivers, and the indistinguishable customers, as our setting assumes that all customers belong to the same delivery class. Thus, the proposed problem consists of three objective functions. The first one is to minimize the total fuel consumption and emission to represent the companies’ main economic and environmental concerns. The second one is to maximize total welfare of the drivers through a function that encourages equitable payment across drivers while encouraging low total driver cost and the third one is to maximize total welfare of the customers through a function that encourages fairness in terms of delivery times. The last two objectives are measured using slots for tour lengths and delivery times. We implement an efficient solution approach based on the -constraint scalarization to find the nondominated solutions of our triobjective optimization problem and present computational analysis that provide insights on the trade-off between the objectives. Our experiments demonstrate the potential of the suggested framework under the customer anonymity assumption to help decision makers make effective plans that all parties involved would give consent to.
Open Access
The refugee camp management: a general framework and a unifying decision-making model
(Emerald Group Publishing, 2019) Karsu, Özlem; Kara, Bahar Yetiş; Selvi, B.
Purpose: Motivated by the increasing need to provide support to refugees, which remains as a pressing issue in the agenda of many countries, the purpose of this paper is to consider the refugee camp management problem. Although each of these countries may have different procedures shaped by their own culture, rules and regulations, the main structure of the problem can be modeled utilizing a general framework which will apply to different practices. Design/methodology/approach: In this study, the authors consider the issue with an operations research (OR) perspective and provide such a framework utilizing Turkish Red Crescent (TRC)’s field expertise in many regions of the world. In the proposed framework, the overall refugee camp management problem is first categorized in two main phases: the establishment phase, which consists of one-time decisions like infrastructure design and the administration phase, which focuses on routine decisions that are taken on a periodic basis like aid distribution. Findings: The authors then provide a unifying decision-making model for the establishment phase and detail the administrative phase via subcategories, linking the relevant problems to the OR literature. The proposed framework is general enough to be used by practitioners and to be utilized by the academicians to define new OR problems to the literature. Originality/value: TRC’s know-how is very broad and extensive. Integrating that know-how with OR perspective, the authors provide a general framework that could be of use to practitioners as well as academicians. The proposed framework will constitute an example for countries of asylum and national or international NGOs to manage the refugee camps efficiently. The authors also highlight main challenges and dynamics of the decision-making problems encountered in different parts of the proposed framework, which may constitute many different problems to the OR literature each of which can open new venues for future research.
Open Access
Solution approaches for equitable multiobjective integer programming problems
(Springer, 2022-04) Bashir, Bashir; Karsu, Özlem
We consider multi-objective optimization problems where the decision maker (DM) has equity concerns. We assume that the preference model of the DM satisfies properties related to inequity-aversion, hence we focus on finding nondominated solutions in line with the properties of inequity-averse preferences, namely the equitably nondominated solutions. We discuss two algorithms for finding good subsets of equitably nondominated solutions. The first approach is an extension of an interactive approach developed for finding the most preferred nondominated solution when the utility function is assumed to be quasiconcave. We find the most preferred equitably nondominated solution when the utility function is assumed to be symmetric quasiconcave. In the second approach we generate an evenly distributed subset of the set of equitably nondominated solutions to be considered further by the DM. We show the computational feasibility of the two algorithms on equitable multi-objective knapsack problem, in which projects in different categories are to be funded subject to a limited budget. We perform experiments to show and discuss the performances of the algorithms. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Open Access
Split algorithms for multiobjective integer programming problems
(Elsevier, 2022-04) Karsu, Özlem; Ulus, Firdevs
We consider split algorithms that partition the objective function space into p or p−1 dimensional regions so as to search for nondominated points of multiobjective integer programming problems, where p is the number of objectives. We provide a unified approach that allows different split strategies to be used within the same algorithmic framework with minimum change. We also suggest an effective way of making use of the information on subregions when setting the parameters of the scalarization problems used in the p-split structure. We compare the performances of variants of these algorithms both as exact algorithms and as solution approaches under time restriction, considering the fact that finding the whole set may be computationally infeasible or undesirable in practice. We demonstrate through computational experiments that while the (p−1)-split structure is superior in terms of overall computational time, the p-split structure provides significant advantage under time/cardinality limited settings in terms of representativeness, especially with adaptive parameter setting and/or a suitably chosen order for regions to be explored.