We begin this paper by identifying a class of stochastic mixed-integer programs that have column-oriented formulations suitable for solution by a branch-and-price algorithm (B&P). We then survey a number of examples, and use a stochastic facility-location problem (SFLP) for a detailed demonstration of the relevant modeling and solution techniques. Computational results with a scenario represent...

In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in ...

We consider a class of two-stage stochastic integer programs with binary variables in the first stage and general integer variables in the second stage. We develop decomposition algorithms akin to the L-shaped or Benders’ methods by utilizing Gomory cuts to obtain iteratively tighter approximations of the second-stage integer programs. We show that the proposed methodology is flexible in that i...

Supplier selection has a strategic importance for every company. Hybrid integer data is one of the models in data envelopment analysis (DEA). In many real world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained hybrid integer d...

DoD’s transportation activities incur USD 11+Billion expenditure anually. Optimal resource allocation under uncertainty provides huge opportunity for improvement and commensurate cost savings. Stochastic optimization techniques are used to incorporate uncertainty in the data to arrive at robust resource allocations. The application of stochastic optimization extends to a broad range of areas ra...

ddsip.vSD is a C-implementation of a number of scenario decomposition algorithms for stochastic linear programs with firstor second-order stochastic dominance constraints induced by mixed-integer linear recourse. The program is based on a previous implementation of scenario decomposition algorithms for mean-risk models of A. Märkert [20]. Main idea of the decomposition algorithms is the Lagrang...

DoD’s transportation activities incur USD 11+Billion expenditure anually. Optimal resource allocation under uncertainty provides huge opportunity for improvement and commensurate cost savings. Stochastic optimization techniques are used to incorporate uncertainty in the data to arrive at robust resource allocations. The application of stochastic optimization extends to a broad range of areas ra...

We develop a two-stage stochastic programming model with integer first-stage and mixed-integer recourse for solving the unit commitment problem in power generation in the presence of uncertainty of load profiles. The solution methodology rests on a novel scenario decomposition method for stochastic integer programming. This method combines Lagrangian relaxation of non-anticipativity constraints...

The scheduling of hydro stations has stochastic, integer, non-linear, and continuous time aspects, with all the approaches described to date making some simplifying assumption about one or other of these aspects. The (integer) unit commitment decision has received relatively little attention, partly because the resulting problem was deemed intractable given the potential gains in efficiency. Ho...

We consider two-stage quadratic integer programs with stochastic right-hand sides, and present an equivalent reformulation using value functions. We first derive some basic properties of value functions of quadratic integer programs. We then propose a two-phase solution approach. The first phase constructs the value functions of quadratic integer programs in both stages. The second phase solves...