In an uncertain and competitive environment, product portfolio management (PPM) becomes more challenging for manufacturers to decide what to make and establish the most beneficial product portfolio. In this paper, a novel approach in PPM is proposed in which the environment uncertainty, competitors’ behavior and customer’s satisfaction are simultaneously considered as the most important criteri...

We consider the problem of controlling a Linear Quadratic Regulator (LQR) system over finite horizon $T$ with fixed and known cost matrices $Q,R$, but unknown non-stationary dynamics $\{A_t, B_t\}$. The sequence can be arbitrary, total variation, $V_T$, assumed to $o(T)$ controller. Under assumption that stabilizing, potentially sub-optimal controllers is available for all $t$, we present an al...

A major challenge to develop optimal strategies for allocation of flexible demand toward the smart grid paradigm is uncertainty associated with real-time price and electricity demand. This article presents a regret-based model novel iterative algorithm which solves minimax regret optimization problem. algorithms exhibits low computational burden compared traditional linear programming methods a...

In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows user to apply classic RRM model introduced in Chorus (2010, European Journal Transport and Infrastructure Research 10: 181–196), generalized (2014, Transportation Research, Part B 68: 224–238), also µRRM pure models, both van Cranenburgh, Guevara, (2015,...

We analyze competition among newsvendors when the only information competitors possess about the nature of future demand realizations is the support of demand distributions. In such a setting, traditional expectation-based optimization criteria may not be adequate. In our analysis, we focus on several alternative criteria used in the robust optimization literature, such as relative and absolute...

We introduce a new algorithm and a new analysis technique that is applicable to a variety of online optimization scenarios, including regret minimization for Lipschitz regret functions, universal portfolio management, online convex optimization and online utility maximization. In addition to being more efficient and deterministic, our algorithm applies to a more general setting (e.g. when the p...

We study different types of aggregation operators and the decision making process with minimization of regret. We analyze the original work developed by Savage and the recent work developed by Yager that generalizes the MMR method creating a parameterized family of minimal regret methods by using the ordered weighted averaging (OWA) operator. We suggest a new method that uses different types of...

We study the regret of optimal strategies for online convex optimization games. Using von Neumann’s minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary’s action sequence, of the difference betwe...