A methodology for the cross-dock door design problem under uncertainty
The Cross-Dock door Design Problem (CDDP) consists of deciding on the number and capacity of inbound and outbound doors for receiving commodities from origin nodes and sending them to destination nodes. The uncertainty, realized in scenarios, lies in the sets of nodes that must be dealt with, the vo...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:dnet:addi________::b939c0b0774b86d0a6c835c1c84730c5 |
| Acceso en línea: | http://hdl.handle.net/10810/80030 |
| Access Level: | acceso abierto |
| Palabra clave: | cross-dock door design two-stage stochastic quadratic combinatorial optimization linearized mixed-integer programming constructive matheuristic |
| Sumario: | The Cross-Dock door Design Problem (CDDP) consists of deciding on the number and capacity of inbound and outbound doors for receiving commodities from origin nodes and sending them to destination nodes. The uncertainty, realized in scenarios, lies in the sets of nodes that must be dealt with, the volume of commodities handled and the operational cost as well as the doors’ capacity disruption. The CDDP is represented using a stochastic two-stage binary quadratic (BQ) model. The first stage decisions are related to design of the cross-dock infrastructure, and the second stage decisions are related to the assignments of nodes to doors. This is the first time, as far as we know, that a stochastic two-stage BQ model has been presented for minimizing the cost of building the platform’s infrastructure and the expected cost of its use in the scenarios. Given the difficulty of solving this combinatorial problem, a mathematically equivalent MILP formulation is introduced. However, searching for an optimal solution is still impractical for commercial solvers. Thus, a scenario cluster decomposition-based matheuristic algorithm is introduced to obtain feasible solutions with only a small optimality gap and reasonable computational effort.Abroad 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 1.31% to 8.33% optimality gap, while the solver does it with a gap of up to 12.45%, if any, and requires a wall time twice as high for the largest instances, at least. |
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