The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-sum game played between an optimizing player and an adversary, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player. Existing minmax regret models consider only deterministic solutions/strategies, and minmax regret...

This paper addresses a version of the single-facility Maximal Covering Location Problem on network where demand is: (i) distributed along edges and (ii) uncertain with only known interval estimation. To deal this problem, we propose minmax regret model service facility can be located anywhere network. problem is called Minmax Regret (MMR-EMCLP). Furthermore, present two polynomial algorithms fo...

We formulate minmax flow problems as a DC. optimization problem. We then apply a DC. primal-dual algorithm to solve the resulting problem. The obtained computational results show that the proposed algorithm is efficient thanks to particular structures of the minmax flow problems.

This paper deals with mainly establishing numerous sets of generalized second-order parametric sufficient optimality conditions for a semiinfinite discrete minmax fractional programming problem, while the results on semiinfinite discrete minmax fractional programming problem are achieved based on some partitioning schemes under various types of generalized second-order (F ,β, φ, ρ, θ, m)-univex...

A number of methods for multiple-objective optimization problems (MOP) give as solution to MOP the set of optimal solutions for some single-objective optimization problems associated with it. Well-known examples of these single-objective optimization problems are the minsum and the minmax. In this note, we propose a new parametric single-objective optimization problem associated with MOP by mea...

In the stabilizing consensus problem each agent of a networked system has an input value and is repeatedly writing output value; it required that eventually all values stabilize to same which, moreover, must be one values. We study this for synchronous model with identical anonymous agents are connected by time-varying topology may join at any time (asynchronous start). Our main result generic ...

We study repeated Bayesian games with communication and observable actions in which the players’ privately-known payoffs evolve according to an irreducible Markov chain whose transitions are independent across players. Our main result implies that, generically, any Pareto-efficient payoff vector above a stationary minmax value can be approximated arbitrarily closely in a perfect Bayesian equili...