S3 sets, an extension of the Beale-Tomlin special ordered sets
Mathematical Programming: Series A and B - Mathematical Models and Their Solutions
Valid inequalities for 0–1 knapsacks and mips with generalised upper bound constraints
Selected papers on First international colloquium on pseudo-boolean optimization and related topics
Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
L-shaped decomposition of two-stage stochastic programs with integer recourse
Mathematical Programming: Series A and B
Computational solution of capacity planning models under uncertainty
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
Constraint-Based Scheduling
A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty
Journal of Global Optimization
Journal of Global Optimization
The Air Traffic Flow Management Problem with Enroute Capacities
Operations Research
Risk Aversion via Excess Probabilities in Stochastic Programs with Mixed-Integer Recourse
SIAM Journal on Optimization
On Bridging the Gap Between Stochastic Integer Programming and MIP Solver Technologies
INFORMS Journal on Computing
Introduction to Stochastic Programming
Introduction to Stochastic Programming
The integer L-shaped method for stochastic integer programs with complete recourse
Operations Research Letters
Dual decomposition in stochastic integer programming
Operations Research Letters
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
Computers and Operations Research
Fix-and-Relax-Coordination for a multi-period location-allocation problem under uncertainty
Computers and Operations Research
Integrated production scheduling and maintenance policy for robustness in a single machine
Computers and Operations Research
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We present a framework for solving multistage pure 0-1 programs for a widely used sequencing and scheduling problem with uncertainty in the objective function coefficients, the constraint matrix and the right-hand side. The problem has the following form: given a set of operations to be executed along a time horizon, find a schedule to minimize a function included by the expected operations cost over the scenarios under consideration, subject to a set of constraints. Typical elements are: limited availability of the resources, multiperiod operations, subsets of operations with exclusivity and implicative constraints, precedence relationships in the execution of the operations, etc. The stochasticity is in the resources' consumption by the operations, their availability and the operations cost along the time horizon. A multistage scenario analysis with complete recourse is used. Given the high dimensions of the problem and its combinatorial nature, it is not realistic to obtain the optimal solution for the problem. Instead, we present the so-called Fix-and-Relax Coordination algorithmic framework to exploit the characteristics of the non-anticipativity constraints for each scenario group in the stochastic model. This exploitation basically consists of selectively exploring the nodes of the branching trees in which the branch-and-bound tree is decomposed while the non-anticipativity constraints are relaxed. The algorithm is specifically designed for coordinating and reinforcing the node pruning, and the branching node and variable selection at each branching tree, such that the non-anticipativity constraints are satisfied. Some computational experience is reported.