TY - JOUR
T1 - Real-time optimization of containers and flatcars for intermodal operations
AU - Powell, Warren Buckler
AU - Carvalho, Tassio A.
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 1998/5
Y1 - 1998/5
N2 - We propose a dynamic model for optimizing the flows of flatcars that considers explicitly the broad range of complex constraints that govern the assignment of trailers and containers to a flatcar. The problem is formulated as a logistics queueing network which can handle a wide range of equipment types and complex operating rules. The complexity of the problem prevents a practical implementation of a global network optimization model. Instead, we formulate a global model with the specific goal of providing network information to local decision makers, regardless of whether they are using optimization models at the yard level. Thus, our approach should be relatively easy to implement given current rail operations. Initial experiments suggest that a flatcar fleet that is managed locally, without the benefit of our network information, can achieve the same demand coverage as a fleet that is 10 percent smaller, but is managed locally with our network information.
AB - We propose a dynamic model for optimizing the flows of flatcars that considers explicitly the broad range of complex constraints that govern the assignment of trailers and containers to a flatcar. The problem is formulated as a logistics queueing network which can handle a wide range of equipment types and complex operating rules. The complexity of the problem prevents a practical implementation of a global network optimization model. Instead, we formulate a global model with the specific goal of providing network information to local decision makers, regardless of whether they are using optimization models at the yard level. Thus, our approach should be relatively easy to implement given current rail operations. Initial experiments suggest that a flatcar fleet that is managed locally, without the benefit of our network information, can achieve the same demand coverage as a fleet that is 10 percent smaller, but is managed locally with our network information.
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U2 - 10.1287/trsc.32.2.110
DO - 10.1287/trsc.32.2.110
M3 - Article
AN - SCOPUS:0032069551
VL - 32
SP - 110
EP - 126
JO - Transportation Science
JF - Transportation Science
SN - 0041-1655
IS - 2
ER -