A capacity improvement lower bound for fixed charge network design problems
Operations Research
A Heuristic for Moment-Matching Scenario Generation
Computational Optimization and Applications
Constructing Railroad Blocking Plans to Minimize Handling Costs
Transportation Science
A Survey of Optimization Models for Train Routing and Scheduling
Transportation Science
Multimodal Express Package Delivery: a Service Network Design Application
Transportation Science
Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use?
Operations Research
A Hybrid Tabu Search/Branch-and-Bound Algorithm for the Direct Flight Network Design Problem
Transportation Science
Composite Variable Formulations for Express Shipment Service Network Design
Transportation Science
The Design of a Letter-Mail Transportation Network by Intelligent Techniques
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Generating Scenario Trees for Multistage Decision Problems
Management Science
Ship Routing and Scheduling: Status and Perspectives
Transportation Science
Online Stochastic Combinatorial Optimization
Online Stochastic Combinatorial Optimization
A metaheuristic for stochastic service network design
Journal of Heuristics
Twenty-Five Years of Hub Location Research
Transportation Science
Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
Computers and Operations Research
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The objective of this paper is to investigate the importance of introducing stochastic elements into service network design formulations. To offer insights into this issue, we take a basic version of the problem in which periodic schedules are built for a number of vehicles and where only the demand may vary stochastically. We study how solutions based on uncertain demand differ from solutions based on deterministic demand and provide qualitative descriptions of the structural differences. Some of these structural differences provide a hedge against uncertainty by using consolidation. This way we get consolidation as output from the model rather than as an a priori required property. Service networks with such properties are robust, as seen by the customers, by providing operational flexibility.