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1131. Cross-dock Door Design Problem, CDDP, under uncertainty
Invited abstract in session MD-35: Cross-dock Door Problems, stream Stochastic, Robust and Distributionally Robust Optimization.
Monday, 14:30-16:00Room: 44 (building: 303A)
Authors (first author is the speaker)
1. | M. Araceli Garin
|
Quantitative Methods, UPV/EHU | |
2. | Laureano F. Escudero
|
Estadística e Investigación Operativa, Universidad Rey Juan Carlos | |
3. | Aitziber Unzueta
|
Applied Mathematics, UPV/EHU |
Abstract
The Cross-dock Door Design Problem (CDDP) consists of deciding on the number and capacity of inbound and outbound doors for receiving product pallets from origin nodes and exiting them to destination nodes. The uncertainty, realized in scenarios, lies in the occurrence of these nodes, the number and cost of the pallets, and the capacity’s disruption of the doors. The CDDP is represented using a stochastic two-stage binary quadratic model (BQM). The first stage decisions are related to the cross-dock infrastructure design, and the second stage ones to the node-to-door’s assignments. This is the first time, as far as we know, that a stochastic two-stage BQM has been presented for minimizing the construction cost of the infrastructure and its exploitation expected cost in the scenarios. Given the difficulty of solving this combinatorial problem, a mathematically equivalent MILP formulation is introduced. However, searching an optimal solution is still impractical for commercial solvers. Thus, a scenario cluster decomposition-based matheuristic is introduced to obtain feasible solutions with small optimality gap and reasonable computational effort. A broad study to validate the proposal gives solutions with a much smaller gap than the ones provided by a state-of-the-art general solver. In fact, the proposal provides solutions with a 1 to 5% optimality gap, while the solver does it with up to a 12% gap, if any, and requires a wall time two orders of magnitude higher.
Keywords
- Combinatorial Optimization
- Programming, Quadratic
- Stochastic Optimization
Status: accepted
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