This paper aims at proposing tractable algorithms to find effectively good solutions to large size chance-constrained combinatorial problems. A new robust model is introduced to deal with uncertainty in mixed-integer linear problems. It is shown to be strongly related to chance-constrained programming when considering pure 0–1 problems. Furthermore, its tractability is highlighted. Then, an optimization algorithm is designed to provide possibly good solutions to chance-constrained combinatorial problems. This approach is numerically tested on knapsack and multi-dimensional knapsack problems. The results obtained outperform many methods based on earlier literature.

This paper considers large shift scheduling problems with different shiftstart times and lengths, fractionable breaks and work stretch durationrestrictions. Two solution approaches are proposed to solve the problemsover a multiple-day planning horizon. The first approach is based on alocal branching strategy and the second one is based on a temporaldecomposition of the problem. Local branching is veryefficient in finding good feasible solutions when compared to a classicalbranch-and-bound procedure. However, the decomposition approach has theadvantage of yielding feasible solutions in short computing times, even for difficult instances.