A Simulation Model for LTL Trucking Network

A Simulation Model for LTL Trucking Network

Fubin Qian, Yue Xu
DOI: 10.4018/jisscm.2012070105
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Service network design (SND) is a part of tactical planning activities of transportation companies. Less-than-truckload (LTL) trucking industry has been steadily expanding the market share in the past decades, due to its operational flexibility and high efficiency. In order to provide flexible and robust service schedule for LTL carriers, stochasticity is explicitly taken into account when formulating the SND problem. Service schedules derived from the stochastic model show structural difference with its deterministic counterparts. This research project develops a simulation model of an LTL network, in order to evaluate the system performance of LTL network with the stochastic schedule. A set of experiments shows that the stochastic solution performs very well when it is confronted with random customer demands. Furthermore, the stochastic schedule is much better than the deterministic one in terms of the proportion of undelivered commodities.
Article Preview
Top

Service Network Design For Ltl Trucking Network

Service network design aims at the determination of the routes and types of service to operate, service schedules, vehicle and traffic routing, repositioning of the fleet for future use, which are usually part of tactical planning activities. The selected services and the schedule constitute a load plan. See Crainic (2003) for an overview of the service network design problems applied in the context of freight transportation.

It may be advisable to take uncertainty into account when designing a service network for LTL carriers in order to conduct robust and flexible schedule for truck and freight flow. We say a schedule is robust if it can withstand random changes, while it is flexible if it is capable of accommodating to them. Here we present some primary concepts and perspectives related to service network design and uncertainty inherent in service networks.

Although there is quite a significant body of literature on the service network design problem, stochastic factors are rarely incorporated into service network design model explicitly. The literature and the various software systems implemented at various carriers assume complete knowledge, which means these formulations are deterministic. The deterministic models formulate service network design problem mostly based on point forecast of the future demand, i.e., the expected value. This is not to say the researchers and transportation professionals ignore the uncertainty inherent in actual operations. The load plan is conducted through the deterministic model, while in practical implementation the plan is modified according to the real demands.

For LTL carriers, a variety of key factors may involve randomness. Customer demand may vary from day to day. Transportation times between terminals may be different on each day since vehicle speed may be affected by weather and road condition. Sorting/consolidation times are not fixed since congestion and equipment breakdown are unpredictable at terminal. Other random delays may happen in the network due to truck accident, traffic congestion, etc. The stochastic nature of the transportation system must be explicitly considered in order to have insight into the properties of real-life transportation activities since we know that stochasticity is an inherent characteristic of transportation systems.

Complete Article List

Search this Journal:
Reset
Volume 17: 1 Issue (2024)
Volume 16: 1 Issue (2023)
Volume 15: 7 Issues (2022): 6 Released, 1 Forthcoming
Volume 14: 4 Issues (2021)
Volume 13: 4 Issues (2020)
Volume 12: 4 Issues (2019)
Volume 11: 4 Issues (2018)
Volume 10: 4 Issues (2017)
Volume 9: 4 Issues (2016)
Volume 8: 4 Issues (2015)
Volume 7: 4 Issues (2014)
Volume 6: 4 Issues (2013)
Volume 5: 4 Issues (2012)
Volume 4: 4 Issues (2011)
Volume 3: 4 Issues (2010)
Volume 2: 4 Issues (2009)
Volume 1: 4 Issues (2008)
View Complete Journal Contents Listing