A copula-based heuristic for scenario generation

A copula-based heuristic for scenario generation

In most practical applications of stochastic programming, we have to approximate the probability distribution of the stochastic parameters by a discrete distribution, i.e. a list of realizations (scenarios) and their probabilities. The process of generating this discrete distribution is usually referred to as ‘scenario generation’. As with any other approximation, the quality of the scenarios i...

Erratum to: A copula-based heuristic for scenario generation

In this section, we present a heuristic for the bivariate case of the average-deviation problem. Just like the MIP formulation (9) from the previous section, the heuristic works on the ranks of the copula and uses the difference between the cdfs as a measure of distance between the sample and the target. It is based on the observation from Eq. (2) that the rank cdf Cr (i, j) depends only on poi...

A Heuristic for Moment-Matching Scenario Generation

Copula-Based Approach to Synthetic Population Generation

Generating synthetic baseline populations is a fundamental step of agent-based modeling and simulation, which is growing fast in a wide range of socio-economic areas including transportation planning research. Traditionally, in many commercial and non-commercial microsimulation systems, the iterative proportional fitting (IPF) procedure has been used for creating the joint distribution of indiv...

A Conflict-Based Path-Generation Heuristic for Evacuation Planning

5 Evacuation planning and scheduling is a critical aspect of disaster management and national security applications. This paper proposes a conflict-based path-generation approach for evacuation planning. Its key idea is to decompose the evacuation planning problem into a master and a subproblem. The subproblem generates new evacuation paths for each evacuated area, while the master problem opti...