Browsing by Subject "Equitable efficiency"

Now showing 1 - 3 of 3

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
Inequity averse optimization in operational research
(Elsevier, 2015) Karsu, Ö.; Morton, A.
There are many applications across a broad range of business problem domains in which equity is a concern and many well-known operational research (OR) problems such as knapsack, scheduling or assignment problems have been considered from an equity perspective. This shows that equity is both a technically interesting concept and a substantial practical concern. In this paper we review the operational research literature on inequity averse optimization. We focus on the cases where there is a tradeoff between efficiency and equity. We discuss two equity related concerns, namely equitability and balance. Equitability concerns are distinguished from balance concerns depending on whether an underlying anonymity assumption holds. From a modeling point of view, we classify three main approaches to handle equitability concerns: the first approach is based on a Rawlsian principle. The second approach uses an explicit inequality index in the mathematical model. The third approach uses equitable aggregation functions that can represent the DM's preferences, which take into account both efficiency and equity concerns. We also discuss the two main approaches to handle balance: the first approach is based on imbalance indicators, which measure deviation from a reference balanced solution. The second approach is based on scaling the distributions such that balance concerns turn into equitability concerns in the resulting distributions and then one of the approaches to handle equitability concerns can be applied. We briefly describe these approaches and provide a discussion of their advantages and disadvantages. We discuss future research directions focussing on decision support and robustness.
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.