9 citations · 24 across the 12 of their papers we have counts for
5 papers · 1 filter
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits
Aneta Neumann, Yue Xie, Frank Neumann
Evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack…
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems
Yue Xie, Aneta Neumann, Frank Neumann
The Stockpile blending problem is an important component of mine production scheduling, where stockpiles are used to store and blend raw material. The goal of blending material fro…
Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints
Yue Xie, Aneta Neumann, Frank Neumann
Heuristic algorithms have shown a good ability to solve a variety of optimization problems. Stockpile blending problem as an important component of the mine scheduling problem is a…
Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
Yue Xie, Aneta Neumann, Frank Neumann
The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is…
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives
Hirad Assimi, Oscar Harper, Yue Xie +2
Real-world combinatorial optimization problems are often stochastic and dynamic. Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In this…