S.Z. Alparslan-Gok

Cooperation under Interval Uncertainty

In this paper, the classical theory of two-person cooperative games is extended to two-person cooperative games with interval uncertainty. The core, balancedness, superadditivity and related topics are studied. Solutions are introduced and characterizations are given.

Cores and Stable Sets for Interval-Valued Games

In this paper, interval-type solution concepts for interval-valued cooperative games like the interval core, the interval dominance core and stable sets are introduced and studied. The notion of I-balancedness is introduced, and it is proved that the interval core of an interval-valued cooperative game is nonempty if and only if the game is I-balanced. Relations between the interval core, the dominance core and stable sets of an interval-valued game are established.

Sequencing Interval Situations and Related Games

In this paper we consider one-machine sequencing situations with interval data. We present different possible scenarioes and extend classical results on well known rules and on sequencing games to the interval setting.

Some Characterizations of Convex Interval Games

This paper focuses on new characterizations of convex interval games using the notions of exactness and superadditivity. We also relate big boss interval games with concave interval games and obtain characterizations of big boss interval games in terms of exactness and subadditivity.

Cooperative Interval Games Arising from Airport Situations with Interval Data

This paper deals with the research area of cooperative interval games arising from airport situations with interval data. We also extend to airport interval games some results from classical theory.

Connection Situations Under Uncertainty

This paper deals with cost allocation problems arising from connection situations where edge costs are closed intervals of real numbers. To solve such problems, we extend classical solutions from the theory of minimum cost spanning tree games. We study the properties of such solutions and suggest how to use them under uncertainty conditions.

Inverse Problems in Complex Multi-Modal Regulatory Networks Based on Uncertain Clustered Data

Complex regulatory networks effected by noise and data uncertainty occur in many OR applications. The complexity is compounded by the unknown interactions between the system variables that have to be revealed from unprecise measurement data. The concept of target-environment networks provides a generic framework for the analysis of complex regulatory systems under uncertainty. Data mining methods like clustering and classification can be applied for an identification of functionally related groups of targets and environmental factors. The effects of the intricate connections between target and environmental clusters on single entities are determined by a parameterized time-discrete model. A crisp regression problem is introduced for parameter estimation and in case of uncertain data, ellipsoids are used to describe the clusters and error sets what refers to particular robust counterpart programs.