TY - JOUR
T1 - Improving fleet utilization for carriers by interval scheduling
AU - Lee, Soonhui
AU - Turner, Jonathan
AU - Daskin, Mark S.
AU - Homem-De-Mello, Tito
AU - Smilowitz, Karen
N1 - Funding Information:
This research is partially supported by Carry Transit, Inc. The authors thank the associate editor and referees for their comments and suggestions.
PY - 2012/4/1
Y1 - 2012/4/1
N2 - Carriers are under increasing pressure to offset rising fuel charges with cost cutting or revenue generating schemes. One opportunity for cost reduction lies in asset management. This paper presents resource allocation scheduling models that can be used to assign truck loads to delivery times and trucks when delivery times are flexible. The paper makes two main contributions. First, we formulate the problem as a multi-objective optimization model - minimizing the number of trucks needed as well as the costs associated with tardiness or earliness - and demonstrate how improvements in fleet usage translate into savings which carriers can use as incentives to promote flexible delivery times for customers. Second, we show that a two-phase model with a polynomial algorithm in the second phase is able to produce optimal schedules in a reasonable time.
AB - Carriers are under increasing pressure to offset rising fuel charges with cost cutting or revenue generating schemes. One opportunity for cost reduction lies in asset management. This paper presents resource allocation scheduling models that can be used to assign truck loads to delivery times and trucks when delivery times are flexible. The paper makes two main contributions. First, we formulate the problem as a multi-objective optimization model - minimizing the number of trucks needed as well as the costs associated with tardiness or earliness - and demonstrate how improvements in fleet usage translate into savings which carriers can use as incentives to promote flexible delivery times for customers. Second, we show that a two-phase model with a polynomial algorithm in the second phase is able to produce optimal schedules in a reasonable time.
KW - Fleet size reduction
KW - Flexible delivery times
KW - Multi-machine scheduling
KW - Multiobjective programming
KW - Penalty costs
UR - http://www.scopus.com/inward/record.url?scp=83955161183&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2011.10.019
DO - 10.1016/j.ejor.2011.10.019
M3 - Article
AN - SCOPUS:83955161183
SN - 0377-2217
VL - 218
SP - 261
EP - 269
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -