Multiobjective Path Finding in Stochastic Dynamic Networks, with Application to Routing Hazardous Materials Shipments

Authors:
Tsung-Sheng Chang;Linda K. Nozick;Mark A. Turnquist
Affiliations:
Institute of Global Operations Strategy and Logistics Management, National Dong Hwa University, Hualien, Taiwan;School of Civil and Environmental Engineering, Cornell University, Hollister Hall, Ithaca, New York 14853;School of Civil and Environmental Engineering, Cornell University, Hollister Hall, Ithaca, New York 14853
Venue:
Transportation Science
Year:
2005

Citing 4
Cited 2

Modeling of Transport Risk for Hazardous Materials

Operations Research
Catastrophe Avoidance Models for Hazardous Materials Route Planning

Transportation Science
Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks

Transportation Science
Path planning under time-dependent uncertainty

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

A container multimodal transportation scheduling approach based on immune affinity model for emergency relief

Expert Systems with Applications: An International Journal
Bi-Objective Bus Routing: An Application to School Buses in Rural Areas

Transportation Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a method for finding nondominated paths for multiple routing objectives in networks where the routing attributes are uncertain, and the probability distributions that describe those attributes vary by time of day. This problem is particularly important in routing and scheduling of shipments of very hazardous materials. Our method extends and integrates the work of several previous authors, resulting in a new algorithm that propagates means and variances of the uncertain attributes along paths and compares partial paths that arrive at a given node within a user-specified time window. The comparison uses an approximate stochastic dominance criterion. We illustrate the effects of changing primary parameters of the algorithm using a small test network, and we show how the nondominated solution set achieved is larger than the set that would be identified if the uncertainty in routing attributes were ignored. We then demonstrate how the algorithm creates an effective solution set in a case study using a large network.