International intermodal choices via chance-constrained goal programming

doi:10.1016/0191-2607(91)90013-G

Transportation Research Part A: General

Volume 25, Issue 6, November 1991, Pages 351-362

https://doi.org/10.1016/0191-2607(91)90013-G Get rights and content

Abstract

Over the years, an increasing interdependence of the world economy has led to the considerable growth of international trade. Due to the lengthy distribution channel, international trade is often characterized by intermodal shipment which moves products across national boundaries via more than one mode of transportation. Consequently, the intermodal choice is of vital importance to the success of international trade. The intermodal choice, however, has never been a simple matter for any distribution manager because it can be affected by the multitude of conflicting factors such as cost, on-time service, and risk. This article develops a chance-constrained goal programming model to aid the distribution manager in choosing the most effective intermodal mix that not only minimizes cost and risk, but also satisfies various on-time service requirements.

References (21)

D.L. Anderson
International logistics strategies for the eighties
P.S. Bender
Logistics system design
P.S. Bender
The international dimension of physical distribution management
A. Charnes et al.
Deterministic equivalents for optimizing and satisficing under chance constraints
Ops Res.
(1963)
J.L. Cohon
Multiobjective Programming and Planning
(1978)
R.M. Cox et al.
The role of packaging in physical distribution
J.J. Coyle et al.
The Management of Business Logistics
(1988)
T.A. Foster
Domestic intermodalism: Radical change, rapid progress
Distribution
(1981)
L.H. Harrington
Plugging into world market
Traffic Mgmnt.
(1983)
How PM's are going global
Purchasing World
(July 1987)

There are more references available in the full text version of this article.

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International intermodal choices via chance-constrained goal programming

Abstract

International logistics strategies for the eighties

Logistics system design

The international dimension of physical distribution management

Deterministic equivalents for optimizing and satisficing under chance constraints

Ops Res.

Multiobjective Programming and Planning

The role of packaging in physical distribution

The Management of Business Logistics

Domestic intermodalism: Radical change, rapid progress

Distribution

Plugging into world market

Traffic Mgmnt.

How PM's are going global

Purchasing World