Network Design for Express Shipment Delivery
Computational Optimization and Applications
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A Study of Demand Stochasticity in Service Network Design
Transportation Science
Hub location for time definite transportation
Computers and Operations Research
Large-Scale, Less-than-Truckload Service Network Design
Operations Research
Computers and Operations Research
A metaheuristic for stochastic service network design
Journal of Heuristics
Branch and Price for Service Network Design with Asset Management Constraints
Transportation Science
Branch and Price for Service Network Design with Asset Management Constraints
Transportation Science
Fleet Management for Vehicle Sharing Operations
Transportation Science
Tabu assisted guided local search approaches for freight service network design
Information Sciences: an International Journal
A GRASP with adaptive large neighborhood search for pickup and delivery problems with transshipment
Computers and Operations Research
Logistics service network design for time-critical delivery
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
LTL logistics networks with differentiated services
Computers and Operations Research
Network design formulations for scheduling U.S. Air Force channel route missions
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.00 |
The focus of this research is to model and solve a large-scale service network design problem involving express package delivery. The objective is to find the cost minimizing movement of packages from their origins to their destinations, given very tight service windows, limited package sort capacity, and a finite number of ground vehicles and aircraft. We have developed a model for large scale transportation service network design problems with time windows. With the use of route-based decision variables, we capture complex cost structures and operating regulations and policies. The poor linear programming bounds limit our ability to solve the problem, so we strengthen our linear programming relaxation by adding valid inequalities. By exploiting problem structure using a specialized network representation and applying a series of novel problem reduction methods, we achieve dramatic decreases in problem size without compromising optimality of the model. Our solution optimization approach synthesizes column and row generation optimization techniques and heuristics to generate solutions to an express package delivery application containing hundreds of thousands of constraints and billions of variables, using only a small fraction of the constraint matrix. The results are potential savings in annual operating costs of tens of millions of dollars, reductions in the fleet size required, dramatic decreases in the time required to develop operating plans, and scenario analysis capabilities for planners and analysts. Through this and additional computational experiments, we conclude that, although state-of-the-art integer programming methods can work well for relatively small, uncongested service network design problems, they must be used in concert with heuristics to be effective for large-scale, congested problems encountered in practice.