MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results

By Dimitri J. Papageorgiou, George L. Nemhauser, J. Sokol, Myun-Seok Cheon, Ahmet B. Keha. 2014

123 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Mathematical Optimization

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Shabbir Ahmed
2. George L. Nemhauser
3. Le Song
4. Bistra N. Dilkina
5. Elias Boutros Khalil
6. Alexander Shapiro
7. Guanghui Lan
8. Yongpei Guan

In Mathematical Optimization

1. Bistra N. Dilkina
2. Elias Boutros Khalil
3. Le Song
4. Yuyu Zhang
5. Shabbir Ahmed
6. Hanjun Dai
7. Andrea Lodi

Citations in the corpus

• Learning Combinatorial Optimization Algorithms over Graphs (2017, background): “common practice to solve the same operational optimization problem repeatedly on slightly different data. One prominent example of such a process is the “Maritime Inventory Routing Problems” library (Papageorgiou et al., 2014) of large-scale transportation problems solved regularly by major corporat”
• Learning to Run Heuristics in Tree Search (2017, methodology): “For the 28 MIRP instances, the solver Gurobi, with default settings (including parallel processing) and no warm-starting, is not capable of finding any feasible solutions for any of the instances in 24 hours [Papageorgiou et al., 2014].”