2008
DOI: 10.1016/j.tre.2007.07.004
|View full text |Cite
|
Sign up to set email alerts
|

Planning local container drayage operations given a port access appointment system

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
37
0
1

Year Published

2011
2011
2019
2019

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 117 publications
(38 citation statements)
references
References 10 publications
0
37
0
1
Order By: Relevance
“…Next, summation of the average queue lengths of all lanes in calculation period of each solution could be obtained from the simulation model. Then, the total costs of the container terminal gate system corresponding to each solution can be calculated through Expression (1).…”
Section: Results Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Next, summation of the average queue lengths of all lanes in calculation period of each solution could be obtained from the simulation model. Then, the total costs of the container terminal gate system corresponding to each solution can be calculated through Expression (1).…”
Section: Results Analysismentioning
confidence: 99%
“…Then, for the management of container trucks, Namboothiri and Erera [1] studied the management of a fleet of trucks providing container pickup and delivery service (drayage) to a terminal with an appointment-based access control system; Chen et al [2] proposed an analytical point-wise stationary approximation model to analyze timedependent truck queuing processes with stochastic service time distributions at gates and yards of a terminal; Chen et al [3] proposed a method called vessel-dependent time windows to control truck arrivals, involving partitioning truck entries into groups and assigning different time windows to the groups. Later, Chen et al [4] developed a biobjective 2 Journal of Advanced Transportation model to minimize both truck waiting times and truck arrival pattern change, so that the emissions from idling truck engines at marine container terminals can be reduced; Yang et al [5] presented an integrated planning model and a sequential planning model to coordinate the major terminal planning activities and developed a heuristic-based genetic algorithm to solve the models; Phan and Kim [6] addressed a negotiation process for smoothing truck arrivals in peak hours among multiple trucking companies and a terminal; Azab and Eltawil [7] developed a discrete event simulation model to study the effect of various truck arrival patterns on length of stay of trucks in container terminals; Ambrosino and Peirano [8] solved a mixed integer linear programming model based on the network flow theory to determine the number of appointments offered by each time window to trucks in the shortest time as possible; Chen and Jiang [9] proposed a solution of managing truck arrivals with time windows based on the truck-vessel service relationship, where trucks delivering containers for the same vessel share one common time window.…”
Section: Introductionmentioning
confidence: 99%
“…In a subsequent work, a deterministic annealing algorithm is proposed to solve the problem . The effect of the introduction of an appointment-based access control system at a port on full truckload drayage operations with time windows is studied by Namboothiri and Erera (2008). Mes et al (2007; propose an agent-based approach for a dynamic version of the FT-PDPTW.…”
Section: Full Truckload Routingmentioning
confidence: 99%
“…In a subsequent work, a deterministic annealing algorithm is proposed . The effect of the introduction of an appointment-based access control system at a port on full truckload drayage operations with time windows is studied by Namboothiri and Erera (2008). Mes et al (2007, 2010 propose an agent-based approach for a dynamic version of the FT-PDPTW.…”
Section: Related Literaturementioning
confidence: 99%