The routing of the rolling stock depends strongly on the rolling stock assignment to di erent opera- tions and the shunting schedule. Therefore, the integration of these decision making is justi ed and is appropriate to introduce robustness in the model. We propose a new approach to obtain better circula- tions of the rolling stock material, solving … Read more
We address rapid transit network design problems characterized by uncertainty in the input data. Network design has a determinant impact on the future e ective- ness of the system. Design decisions are made with a great degree of uncertainty about the conditions under which the system will be required to operate. The de- mand is one … Read more
We study dynamic operational decision problems where risky cash flows are being resolved over a finite planning horizon. Financing decisions via lending and borrowing are available to smooth out consumptions over time with the goal of achieving some prescribed consumption targets. Our target-oriented decision criterion is based on the aggregation of Aumann and Serrano (2008) … Read more
In this paper we discuss representations of law invariant coherent risk measures in a form of integrals of the Average Value-at-Risk measures. We show that such integral representation exists iff the dual set of the considered risk measure is generated by one of its elements, and this representation is uniquely defined. On the other hand, … Read more
Liquefied Natural Gas contracts offer cancellation options that make their pricing difficult, especially if many gas storages need to be taken into account. We develop a valuation mechanism for such contracts from the buyer’s perspective, a large gas company whose main interest in these contracts is to provide a reliable supply of gas to its … Read more
In this work we consider the symmetric network equilibrium problem formulated as convex minimization problem whose variables are the path flows. In order to take into account the difficulties related to the large dimension of real network problems we adopt a column generation strategy and we employ a gradient projection method within an inexact decomposition … Read more
In this paper, we present a general framework for designing approximation schemes for combinatorial optimization problems in which the objective function is a combination of more than one function. Examples of such problems include those in which the objective function is a product or ratio of two linear functions, parallel machine scheduling problems with the … Read more
In this paper, we consider a job scheduling problem with random local generation, in which some jobs must be scheduled day-ahead while the others can be scheduled in a real time fashion. To capture the randomness of the local distributed generation, we develop a two-stage robust optimization model by assuming an uncertainty set without probability … Read more
We consider the problem of optimal decision making under uncertainty but assume that the decision maker’s utility function is not completely known. Instead, we consider all the utilities that meet some criteria, such as preferring certain lotteries over certain other lotteries and being risk averse, s-shaped, or prudent. This extends the notion of stochastic dominance. … Read more
We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical study, we compare approximate dynamic programming with a global search for parameters of simple control policies. We propose two value function … Read more