A bi-objective model for robust local container drayage problem
RAIRO. Operations Research, Tome 55 (2021), pp. S625-S646

Local Container Drayage Problem (LCDP) refers to the optimization of the process of planning and scheduling container trucks between a terminal and customers to offer door-to-door service to customers in a local area. The time required for (un)packing containers at customers’ sites are often relatively long and uncertain due to the current (un)packing work level and unexpected deviations in operational situations, which has a significant influence on the planning and scheduling of the container transportation process. This paper examines the LCDP with Separable tractors and trailers, and additionally with consideration of (un)packing time Uncertainties (LCDPSU). A proactive strategy is employed to tackle the uncertainty by proposing a “model robust” bi-objective optimization model to balance the tradeoff between operational cost, which includes traveling cost and tractor deployment setup cost, and robustness, which is represented as an exponential expression of the time buffer between two stages of each individual task. The deterministic version of our problem is proved to be NP hard, and an Ant Colony Optimization (ACO) scheme is therefore proposed to search for feasible solutions in which the Zoutendijk feasible direction algorithm is embedded in order to tackle the nonlinearity brought in by the robustness of the model. Numerical experiments are conducted to validate the efficiencies and effectivenesses of the proposed models and methods, and managerial implications are drawn from the numerical results.

DOI : 10.1051/ro/2019063
Classification : 90B06
Keywords: Local Container Drayage Problem, (un)packing time uncertainties, time buffer insertion, Ant Colony Optimization, Zoutendijk feasible direction algorithm
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     title = {A bi-objective model for robust local container drayage problem},
     journal = {RAIRO. Operations Research},
     pages = {S625--S646},
     year = {2021},
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You, Jintao; Zhang, Canrong; Xue, Zhaojie; Miao, Lixin; Ye, Bin. A bi-objective model for robust local container drayage problem. RAIRO. Operations Research, Tome 55 (2021), pp. S625-S646. doi: 10.1051/ro/2019063

M. A. Al-Fawzan and M. Haouari, A bi-objective model for robust resource-constrained project scheduling. Int. J. Prod. Econ. 96 (2005) 175–187. | DOI

K. Braekers, A. Caris and G. K. Janssens, Bi-objective optimization of drayage operations in the service area of intermodal terminals. Transp. Res. Part E-logist. Transp. Rev. 65 (2014) 50–69. | DOI

L. Coslovich, R. Pesenti and W. Ukovich, Minimizing fleet operating costs for a container transportation company. Eur. J. Oper. Res. 171 (2006) 776–786. | Zbl | DOI

M. Dorigo, V. Maniezzo and A. Colorni, Ant system: Optimization by a colony of cooperating agents. Syst. Man Cybern. 26 (1996) 29–41. | DOI

A. Ghezelsoflu, M. D. Francesco, A. Frangioni and P. Zuddas, A set-covering formulation for a drayage problem with single and double container loads. J. Ind. Eng. Int. 14 (2018) 665–676. | DOI

C. E. Gounaris, W. Wiesemann and C. A. Floudas, The robust capacitated vehicle routing problem under demand uncertainty. Oper. Res. 61 (2013) 677–693. | MR | Zbl | DOI

J. Han, C. Lee and S. Park, A robust scenario approach for the vehicle routing problem with uncertain travel times. Transp. Sci. 48 (2014) 373–390. | DOI

X. Han, Z. Lu and L. Xi, A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time. Eur. J. Oper. Res. 207 (2010) 1327–1340. | Zbl | DOI

W. Herroelen and R. Leus, Project scheduling under uncertainty: Survey and research potentials. Eur. J. Oper. Res. 165 (2005) 289–306. | Zbl | DOI

A. Imai, E. Nishimura and J. R. Current, A lagrangian relaxation-based heuristic for the vehicle routing with full container load. Eur. J. Oper. Res. 176 (2007) 87–105. | MR | Zbl | DOI

H. Jula, M. M. Dessouky, P. A. Ioannou and A. Chassiakos, Container movement by trucks in metropolitan networks: Modeling and optimization. Transp. Res. Part E-logist. Transp. Rev. 41 (2005) 235–259. | DOI

O. Lambrechts, E. Demeulemeester and W. Herroelen, Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. J. Sched. 11 (2008) 121–136. | MR | Zbl | DOI

C. Lee, K. Lee and S. Park, Robust vehicle routing problem with deadlines and travel time/demand uncertainty. J. Oper. Res. Soc. 63 (2012) 1294–1306. | DOI

C. Liu, C. Zhang and L. Zheng, A bi-objective model for robust yard allocation scheduling for outbound containers. Eng. Optim. 49 (2017) 113–135. | MR | DOI

J. M. Mulvey, R. J. Vanderbei and S. A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. | MR | Zbl | DOI

S. Shiri and N. Huynh, Optimization of drayage operations with time-window constraints. Int. J. Prod. Econ. 176 (2016) 7–20. | DOI

Y. Song, Z. Jiantong, L. Zhe and Y. Chunming, An exact algorithm for the container drayage problem under a separation mode. Transp. Res. Part E-logist. Transp. Rev. 106 (2017) 231–254. | DOI

T. Stutzle and H. H. Hoos, Max-min ant system. Future Gener. Comput. Syst. 16 (2000) 889–914. | DOI

I. Sungur, F. Ordóñez and M. M. Dessouky, A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty. Iie Trans. 40 (2008) 509–523. | DOI

D. Tjokroamidjojo, E. Kutanoglu and G. D. Taylor, Quantifying the value of advance load information in truckload trucking. Transp. Res. Part E-logist. Transp. Rev. 42 340–357.

S. Wang and Q. Meng, Robust schedule design for liner shipping services. Transp. Res. Part E-logist. Transp. Rev. 48 (2012) 1093–1106.

X. Wang and A. C. Regan, Local truckload pickup and delivery with hard time window constraints. Transp. Res. Part B- methodol. 36 (2002) 97–112.

Z. Xue, C. Zhang, W. H. Lin, L. Miao and P. Yang, A tabu search heuristic for the local container drayage problem under a new operation mode. Transp. Res. Part E-logist. Transp. Rev. 62 136–150.

Z. Xue, C. Zhang, P. Yang and L. Miao, A combinatorial benders’ cuts algorithm for the local container drayage problem. Math. Probl. Eng. 2015 (2015) 1–7.

R. Zhang, W. Y. Yun and H. Kopfer, Heuristic-based truck scheduling for inland container transportation. OR Spectr. 32 (2010) 787–808. | Zbl

R. Zhang, W. Y. Yun and K. I. Moon, Modeling and optimization of a container drayage problem with resource constraints. Int. J. Prod. Econ. 133 (2011) 351–359.

L. Zhen and D. F. Chang, A bi-objective model for robust berth allocation scheduling. Comput. Ind. Eng. 63 (2012) 262–273.

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