TY - JOUR
T1 - Solution methods for vehicle-based inventory routing problems
AU - Dong, Yachao
AU - Maravelias, Christos T.
AU - Pinto, Jose M.
AU - Sundaramoorthy, Arul
N1 - Funding Information:
The authors would like to acknowledge financial support from the US National Science Foundation under grant CBET-1264096.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017
Y1 - 2017
N2 - A novel method for solving vehicle-based inventory routing problems (IRPs) under realistic constraints is presented. First, we propose a preprocessing algorithm that reduces the problem size by eliminating customers and network arcs that are irrelevant for the current horizon. Second, we develop a decomposition method that divides the problem into two subproblems. The upper level subproblem considers a simplified vehicle routing problem to minimize the distribution cost while satisfying minimum demands, which are calculated based on consumption rate, initial inventory and safety stock. In the lower level, a detailed schedule with drivers is acquired using a continuous-time MILP model, by adopting the routes selected from the upper level. Finally, an iterative approach based on the upper and lower levels is presented, including the addition of different types of integer cuts and parameter updates. Different options of implementing this iterative approach are discussed, and computational results are presented.
AB - A novel method for solving vehicle-based inventory routing problems (IRPs) under realistic constraints is presented. First, we propose a preprocessing algorithm that reduces the problem size by eliminating customers and network arcs that are irrelevant for the current horizon. Second, we develop a decomposition method that divides the problem into two subproblems. The upper level subproblem considers a simplified vehicle routing problem to minimize the distribution cost while satisfying minimum demands, which are calculated based on consumption rate, initial inventory and safety stock. In the lower level, a detailed schedule with drivers is acquired using a continuous-time MILP model, by adopting the routes selected from the upper level. Finally, an iterative approach based on the upper and lower levels is presented, including the addition of different types of integer cuts and parameter updates. Different options of implementing this iterative approach are discussed, and computational results are presented.
KW - Decomposition method
KW - Mixed-integer programming
KW - Network reduction algorithm
KW - Vendor managed inventory
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U2 - 10.1016/j.compchemeng.2017.02.036
DO - 10.1016/j.compchemeng.2017.02.036
M3 - Article
AN - SCOPUS:85015671458
SN - 0098-1354
VL - 101
SP - 259
EP - 278
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
ER -