Solving the \(p\)-hub median problem under intentional disruptions using simulated annealing
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Publication:264277
DOI10.1007/s11067-013-9189-3zbMath1332.90140OpenAlexW2008321972MaRDI QIDQ264277
F. Blanchet-Sadri, M. Dambrine
Publication date: 31 March 2016
Published in: Networks and Spatial Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11067-013-9189-3
Taguchi methodsimulated annealingbi-level programming\(p\)-hub median problemdisruptionmultiple allocation
Programming involving graphs or networks (90C35) Mixed integer programming (90C11) Hierarchical games (including Stackelberg games) (91A65) Discrete location and assignment (90B80)
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Uses Software
Cites Work
- Unnamed Item
- Optimization by Simulated Annealing
- Allocation strategies in hub networks
- Optimizing system resilience: a facility protection model with recovery time
- Hub location-allocation in intermodal logistic networks
- Scheduling open shops with parallel machines to minimize total completion time
- Designing robust coverage networks to hedge against worst-case facility losses
- Location and allocation for distribution systems with transshipments and transportion economies of scale
- Adapting polyhedral properties from facility to hub location problems
- Analysis of facility protection strategies against an uncertain number of attacks: the stochastic R-interdiction median problem with fortification
- Uncapacitated Euclidean hub location: strengthened formulation, new facets and a relax-and-cut algorithm
- New formulations for the uncapacitated multiple allocation hub location problem
- Hub location for time definite transportation
- Solving the uncapacitated multiple allocation hub location problem by means of a dual-ascent technique
- Benders decomposition for the uncapacitated multiple allocation hub location problem
- Routing traffic at hub facilities
- Application of particle swarm optimization algorithm for solving bi-level linear programming problem
- Improved immune algorithm for global numerical optimization and job-shop scheduling problems
- Strategic network restoration
- A simple tabu search method to solve the mixed-integer linear bilevel programming problem
- Tight linear programming relaxations of uncapacitated \(p\)-hub median problems
- Exact and heuristic algorithms for the uncapacitated multiple allocation \(p\)-hub median problem
- Efficient solution procedure and reduced size formulations for \(p\)-hub location problems
- Orthogonal arrays. Theory and applications
- Integer programming formulations of discrete hub location problems
- Locating service facilities whose reliability is distance dependent.
- Preprocessing and cutting for multiple allocation hub location problems.
- Incorporating the threat of terrorist attacks in the design of public service facility networks
- A bilevel fixed charge location model for facilities under imminent attack
- Hub-and-spoke network design with congestion
- New formulation and a branch-and-cut algorithm for the multiple allocation p-hub median problem
- Linear bilevel programming solution by genetic algorithm
- The stochastic interdiction median problem with disruption intensity levels
- Resolution method for mixed integer bi-level linear problems based on decomposition technique
- A bilevel mixed-integer program for critical infrastructure protection planning
- A hybrid neural network approach to bilevel programming problems
- Network-based accessibility measures for vulnerability analysis of degradable transportation networks
- Network hub location problems: The state of the art
- On a bi-level formulation to protect uncapacitated p-median systems with facility recovery time and frequent disruptions
- Exponential distribution-based genetic algorithm for solving mixed-integer bilevel programming problems
- Reliable Facility Location Design Under the Risk of Disruptions
- Facility Reliability Issues in Network p-Median Problems: Strategic Centralization and Co-Location Effects
- A facility reliability problem: Formulation, properties, and algorithm
- A defensive maximal covering problem on a network
- Convergence of an annealing algorithm
- Heuristic Solution Methods for Two Location Problems with Unreliable Facilities
- Efficient algorithms for the uncapacitated single allocation p-hub median problem
- A dual algorithm for the uncapacitated hub location problem
- Hub Location and the p-Hub Median Problem
- An Exact Solution Approach Based on Shortest-Paths for p-Hub Median Problems
- Genetic algorithm based approach to bi-level linear programming
- Equation of State Calculations by Fast Computing Machines
- On solving unreliable planar location problems
- HubLocator: An exact solution method for the multiple allocation hub location problem
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