Interactive approaches for biobjective problems with progressively changing solution sets

In this study, we develop interactive approaches to find a satisfactory alternative of a decision maker (DM) having a quasiconvex preference function where the alternative set changes progressively. In this environment, we keep searching the available set of alternatives and estimating the preference function of the DM. As new alternatives emerge, we make better use of the available preference information and eventually converge to a preferred alternative of the DM. We test our approaches on biobjective, multi-item, multi-round auction problems. The results show that our approaches work well in terms of both the preference function value of the obtained solution and the amount of preference information required.


Interactive evolutionary approaches to multiobjective feature selection
ÖZMEN, müberra; Karakaya, Gülşah; KÖKSALAN, MUSTAFA MURAT (Wiley, 2018-05-01)
In feature selection problems, the aim is to select a subset of features to characterize an output of interest. In characterizing an output, we may want to consider multiple objectives such as maximizing classification performance, minimizing number of selected features or cost, etc. We develop a preference-based approach for multiobjective feature selection problems. Finding all Pareto-optimal subsets may turn out to be a computationally demanding problem and we still would need to select a solution. There...
Interactive algorithms for a broad underlying family of preference functions
Karakaya, Gülşah; AHİPAŞAOĞLU, Selin Damla (Elsevier BV, 2018-02-16)
In multi-criteria decision making approaches it is typical to consider an underlying preference function that is assumed to represent the decision maker's preferences. In this paper we introduce a broad family of preference functions that can represent a wide variety of preference structures. We develop the necessary theory and interactive algorithms for both the general family of the preference functions and for its special cases. The algorithms guarantee to find the most preferred solution (point) of the ...
KIRCA, O; KOKTEN, M (Elsevier BV, 1994-06-09)
In this paper a framework for a new heuristic approach for solving the single level multi-item capacitated dynamic lot sizing problem is presented. The approach uses an iterative item-by-item strategy for generating solutions to the problem. In each iteration a set of items are scheduled over the planning horizon and the procedure terminates when all items are scheduled. An algorithm that implements this approach is developed in which in each iteration a single item is selected and scheduled over the planni...
Optimal lot-sizing/vehicle-dispatching policies under stochastic lead times and stepwise fixed costs
Alp, O; Erkip, NK; Gullu, R (Institute for Operations Research and the Management Sciences (INFORMS), 2003-01-01)
We characterize optimal policies of a dynamic lot-sizing/vehicle-dispatching problem under dynamic deterministic demands and stochastic lead times. An essential feature of the problem is the structure of the ordering cost, where a fixed cost is incurred every time a batch is initiated (or a vehicle is hired) regardless of the portion of the batch (or vehicle) utilized. Moreover, for every unit of demand not satisfied on time, holding and backorder costs are incurred. Under mild assumptions we show that the ...
Interactive and nonparametric modeling of preferences on an ordinal scale using small data
Erişkin, Levent; Köksal, Gülser (2016-12-15)
In this study, we consider learning preference structure of a Decision Maker (DM). Many preference modeling problems in a variety of fields such as marketing, quality control and economics involve possibly interacting criteria, and an ordinal scale is used to express preference of objects. In these cases, typically underlying preference structure of the DM and distribution of criteria values are not known, and only a few data can be collected about the preferences of the DM.
Citation Formats
G. Karakaya, “Interactive approaches for biobjective problems with progressively changing solution sets,” INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, pp. 356–375, 2021, Accessed: 00, 2020. [Online]. Available: