Dynamic speed optimization in supply chains with stochastic demand
(2016) In Transportation Science 50(3). p.1114-1127- Abstract
In this paper, we analyze how to continuously adjust the speed in a supply chain with stochastic demand. For each unit (e.g., truckload, shipping container) in the chain, one must decide at which speed it should be moved downstream, given the state of the system, to minimize total supply chain costs. We decompose the problem into a set of one-dimensional subproblems that can be easily solved and characterize the optimal variable speed policy: under some assumptions, we show that it is optimal to set a speed that is first increasing in the distance to the market, and then decreasing. As a result, at optimality a given unit will experience an accelerating speed and then it will be slowed down, unless a demand occurs, in which case, the... (More)
In this paper, we analyze how to continuously adjust the speed in a supply chain with stochastic demand. For each unit (e.g., truckload, shipping container) in the chain, one must decide at which speed it should be moved downstream, given the state of the system, to minimize total supply chain costs. We decompose the problem into a set of one-dimensional subproblems that can be easily solved and characterize the optimal variable speed policy: under some assumptions, we show that it is optimal to set a speed that is first increasing in the distance to the market, and then decreasing. As a result, at optimality a given unit will experience an accelerating speed and then it will be slowed down, unless a demand occurs, in which case, the speed will be adjusted upward. We finally provide a transportation case study where we estimate the benefits of a variable-speed compared to a fixed-speed policy and show them to be significant both financially and from a CO2-emissions perspective.
(Less)
- author
- Berling, Peter LU and Martínez-De-Albéniz, Victor
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Environmental impact, Expediting, Variable speed
- in
- Transportation Science
- volume
- 50
- issue
- 3
- pages
- 14 pages
- publisher
- INFORMS Inst.for Operations Res.and the Management Sciences
- external identifiers
-
- scopus:84984903736
- wos:000381289900021
- ISSN
- 0041-1655
- DOI
- 10.1287/trsc.2014.0561
- language
- English
- LU publication?
- yes
- id
- 21cfb231-eaa8-4af0-b236-de6024ee2f28
- date added to LUP
- 2017-02-22 14:02:33
- date last changed
- 2024-06-10 13:51:23
@article{21cfb231-eaa8-4af0-b236-de6024ee2f28,
abstract = {{<p>In this paper, we analyze how to continuously adjust the speed in a supply chain with stochastic demand. For each unit (e.g., truckload, shipping container) in the chain, one must decide at which speed it should be moved downstream, given the state of the system, to minimize total supply chain costs. We decompose the problem into a set of one-dimensional subproblems that can be easily solved and characterize the optimal variable speed policy: under some assumptions, we show that it is optimal to set a speed that is first increasing in the distance to the market, and then decreasing. As a result, at optimality a given unit will experience an accelerating speed and then it will be slowed down, unless a demand occurs, in which case, the speed will be adjusted upward. We finally provide a transportation case study where we estimate the benefits of a variable-speed compared to a fixed-speed policy and show them to be significant both financially and from a CO<sub>2</sub>-emissions perspective.</p>}},
author = {{Berling, Peter and Martínez-De-Albéniz, Victor}},
issn = {{0041-1655}},
keywords = {{Environmental impact; Expediting; Variable speed}},
language = {{eng}},
number = {{3}},
pages = {{1114--1127}},
publisher = {{INFORMS Inst.for Operations Res.and the Management Sciences}},
series = {{Transportation Science}},
title = {{Dynamic speed optimization in supply chains with stochastic demand}},
url = {{http://dx.doi.org/10.1287/trsc.2014.0561}},
doi = {{10.1287/trsc.2014.0561}},
volume = {{50}},
year = {{2016}},
}