Two approaches for fair resource allocation
| buir.advisor | Karsu, Özlem | |
| dc.contributor.author | Yavuz, Mirel | |
| dc.date.accessioned | 2018-06-05T09:09:20Z | |
| dc.date.available | 2018-06-05T09:09:20Z | |
| dc.date.copyright | 2018-05 | |
| dc.date.issued | 2018-05 | |
| dc.date.submitted | 2018-06-04 | |
| dc.department | Department of Industrial Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2018. | en_US |
| dc.description | Includes bibliographical references (leaves 53-54). | en_US |
| dc.description.abstract | Fairness has become one of the primary concerns in several Operational Research (OR) problems, especially in resource allocation problems. It is crucial to ensure a fair distribution of the resources across the entities so that the proposed solutions will be both applicable and acceptable. In many real-life systems, the most e cient solution will not be the most fair solution, which creates a trade-o between e ciency and fairness. We propose two approaches in order to help the decision makers (DM) to nd an e cient solution which take fairness of the distribution of resources into account. First approach we propose is optimizing a speci c subset of the set of Schur-concave functions, namely ordered weighted averaging (OWA) functions, which are able to re ect both e ciency and fairness concerns. We do not assume that the weights of the DM to be used in OWA functions are readily available. We explore a wide range of weight vectors and report results for these di erent choices of weights. We illustrate the approach using a workload allocation problem and a knapsack problem and visualize the trade-o between fairness and e ciency. In some applications, the DM may provide a reference point such that the aim would be nding an e cient solution which is more preferable than this reference in terms of fairness. For such cases we propose a second approach that maximizes e ciency while controlling fairness concerns via a constraint. Similar to the rst approach, fairness concerns are re ected using OWA function forms. However, the resulting formulation yields to non-linearity. Thus, a hybrid interactive algorithm is presented that tackles this nonlinearity using an enumerative approach. The algorithm nds an e cient solution which OWA dominates the reference point by interacting with the DM. The algorithm is tested on knapsack problems and shows successful performance. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | by Mirel Yavuz. | en_US |
| dc.format.extent | ix, 60 leaves : charts ; 30 cm | en_US |
| dc.identifier.itemid | B158449 | |
| dc.identifier.uri | http://hdl.handle.net/11693/46987 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Resource Allocation Problem | en_US |
| dc.subject | Fairness | en_US |
| dc.subject | Knapsack Problem | en_US |
| dc.subject | Interactive Algorithm | en_US |
| dc.subject | Ordered Weighted Averaging | en_US |
| dc.title | Two approaches for fair resource allocation | en_US |
| dc.title.alternative | Eşitlikçi kaynak dağıtımına iki yaklaşım | en_US |
| dc.type | Thesis | en_US |