TY - JOUR
T1 - Multiobjective Optimization Models for Locating Vehicle Inspection Stations Subject to Stochastic Demand, Varying Velocity and Regional Constraints
AU - Tian, Guangdong
AU - Zhou, Meng Chu
AU - Li, Peigen
AU - Zhang, Chaoyong
AU - Jia, Hongfei
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 51405075, Grant 51275190, and Grant 51575211, by the Funds for International Cooperation Exchange of the National Natural Science Foundation of China under Grant 51561125002, and by the Postdoctoral Science Foundation Project of Heilongjiang Province of China under Grants LBH-Z13005 and LBH-TZ0501.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/7
Y1 - 2016/7
N2 - Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.
AB - Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.
KW - Transportation
KW - facility location
KW - modeling and simulation
KW - optimization algorithm
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U2 - 10.1109/TITS.2016.2514277
DO - 10.1109/TITS.2016.2514277
M3 - Article
AN - SCOPUS:84960193005
SN - 1524-9050
VL - 17
SP - 1978
EP - 1987
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
M1 - 7423738
ER -