Two-stage conflict robust optimization models for cross-dock truck scheduling problem under uncertainty

This paper addresses a class of two-stage robust optimization models with an exponential number of scenarios given implicitly. We apply Dantzig–Wolfe decomposition to exploit the structure of these models and show that the original problem reduces to a single-stage robust problem. We propose a Benders algorithm for the reformulated single-stage problem. We also develop a heuristic algorithm that dualizes the linear programming relaxation of the inner maximization problem in the reformulated model and iteratively generates cuts to shape the convex hull of the uncertainty set. We combine this heuristic with the Benders algorithm to create a more effective hybrid Benders algorithm. Because the master problem and subproblem in the Benders algorithm are mixed-integer programs, it is computationally demanding to solve them optimally at each iteration of the algorithm. Therefore, we develop novel stopping conditions for these mixed-integer programs and provide the relevant convergence proofs. Extensive computational experiments on a nurse planning problem and a two-echelon supply chain problem are performed to evaluate the efficiency of the proposed algorithms.

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The Hot Strip Mill Scheduling Problem With Uncertainty: Robust Optimization Models and Solution Approaches

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2021.3135539 ◽

2022 ◽

pp. 1-15

Author(s):

Rui Zhang ◽

Shiji Song ◽

Cheng Wu

Keyword(s):

Robust Optimization ◽

Optimization Models ◽

Hot Strip Mill ◽

Scheduling Problem ◽

Hot Strip

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Robust Two-Stage and Multistage Optimization

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch002 ◽

2016 ◽

pp. 54-94

Author(s):

Michel Andre Minoux

Keyword(s):

Robust Optimization ◽

Optimization Problems ◽

Uncertain Data ◽

Optimal Strategies ◽

Optimization Models ◽

Special Focus ◽

Planning Problem ◽

Two Stage ◽

Production Planning Problem ◽

The Right

This chapter is intended as an overview of robust optimization models related to optimization problems subject to uncertain data, with special focus on the case when uncertainty impacts the right-hand side coefficients in the constraints. Two-stage as well as multistage models are addressed, emphasizing links with applications and computational complexity issues. A class of multistage robust optimization problems for which exact optimal strategies can be efficiently computed (via a robust dynamic programming recursion) is discussed. An application to a multiperiod energy production planning problem is presented into detail, and computational results are reported.

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