Pages that link to "Item:Q486397"

The following pages link to An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem (Q486397):

Displaying 16 items.

• The minmax regret robust shortest path problem in a finite multi-scenario model (Q279255) (← links)
• A new bound for the midpoint solution in minmax regret optimization with an application to the robust shortest path problem (Q319447) (← links)
• Reduction approaches for robust shortest path problems (Q716356) (← links)
• A minmax regret version of the time-dependent shortest path problem (Q724002) (← links)
• A minimum expected regret model for the shortest path problem with solution-dependent probability distributions (Q1652010) (← links)
• A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs (Q1652219) (← links)
• Erratum to: ``An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem'' (Q1675565) (← links)
• Formulation and algorithms for the robust maximal covering location problem (Q1742214) (← links)
• Dynamic preprocessing for the minmax regret robust shortest path problem with finite multi-scenarios (Q1751178) (← links)
• A utility theory based interactive approach to robustness in linear optimization (Q1753136) (← links)
• A MILP-based VND for the min-max regret shortest path tree problem with interval costs (Q1791906) (← links)
• Robust min-max regret covering problems (Q2162527) (← links)
• Algorithms for the minmax regret path problem with interval data (Q2198244) (← links)
• On the Finite Optimal Convergence of Logic-Based Benders’ Decomposition in Solving 0–1 Min-Max Regret Optimization Problems with Interval Costs (Q2835657) (← links)
• An enhanced exact procedure for the absolute robust shortest path problem (Q3563623) (← links)
• A fix‐and‐optimize heuristic for the minmax regret shortest path arborescence problem under interval uncertainty (Q6079879) (← links)