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2710. Benders decomposition for robust and congested partial set covering location
Invited abstract in session TA-52: Mixed Integer Optimization I, stream Combinatorial Optimization.
Tuesday, 8:30-10:00Room: 8003 (building: 202)
Authors (first author is the speaker)
| 1. | Alice Calamita
|
| Modelite | |
| 2. | Ivana Ljubic
|
| IDS, ESSEC Business School of Paris | |
| 3. | Laura Palagi
|
| Department of Computer, Control, and Management Engineering A. Ruberti, Sapienza University of Rome |
Abstract
We present the congested facility location problem with partial coverage under the assumption of uncertain customer demand. First, we formulate the deterministic problem and its robust counterpart as mixed-integer quadratic problems. Second, since the size of the robust counterpart grows with the number of customers, which could be significant in real-world contexts, we investigate the use of Benders decomposition to effectively reduce the number of variables by projecting out of the master problem all the variables dependent on the number of customers. We discuss single-tree and multi-tree Benders approaches and introduce a perturbation technique to deal with the degeneracy of the Benders subproblem efficiently. Our tailored Benders approaches outperform the state-of-the-art solver Gurobi on adapted instances from the literature.
Keywords
- Robust Optimization
- Algorithms
- Location
Status: accepted
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