Integrating truck arrival management into tactical operation planning at container terminals

Abstract

As an intermodal interface, marine container terminals serve vessels on the sea side and trucks/trains on the land side. Operating a container terminal involves many different decisions and they often interact with each other. Due to the multi-criteria nature, the complexity of operations, and the size of the operations management problem, it is extremely difficult to make the optimal decisions for the entire terminal system (Zhang et al., 2003). Traditionally, the whole system is decomposed into a set of sub-planning problems of manageable complexity. The sub-problems may be solved in a sequential fashion, in which the output of one sub-problem is treated as the input of another sub-problem. This sequential solution enables a clear hierarchy of decision making, but on the other hand, ignores the interrelations between the sub-problems and often leads to plans of poor overall quality (Bierwirth and Meisel, 2010). In order to find better planning decisions, it is necessary to integrate some of the sub-planning problems and optimize them simultaneously at a reasonable planning level, as mentioned by Stahlbock and Voss (2008) that "improved terminal performance cannot necessarily be obtained by solving isolated problems but by an integration of various operations connected to each other."Many container terminals in Asia have a typical layout as shown in Fig. 1, which consists of three parts: the seaside area, the yard storage area and the landside area. The seaside area is the place where vessels are berthed and operated by quay cranes. The landside area, also called gate house, is the entrance and exit place for external trucks (XTs). Between the seaside and the landside areas is the storage yard, which stores inbound (I/B) and outbound (O/B) containers temporarily because there are time differences between vessel arrivals and land-carrier arrivals (Meisel, 2009

Performance analysis of time-dependent queueing systems: Survey and classification

“…The same approximation method is used by Kimber et al [121], Kimber and Hollis [120], and Kimber and Daly [119] to analyze the performance of three-arm major/minor priority junctions. Kimber et al [121] and Kimber and Hollis [120] [237], analyze truck handling facilities at seaports. Based on these analyses, several optimization techniques are proposed to optimize the time-dependent truck arrival process.…”

“…The number of active nodes of two different classes in a peer-to-peer (P2P) internet telephony system is modeled with an MðtÞ=M=1 system and analyzed via the SIPP and explicit solutions by Kuraya et al [134,135]. McCalla and Whitt [162] evaluate the volume of lines in [29] CTT Kimber et al [121] CTT Kimber and Hollis [120] CTT Kimber and Daly [119] x CTT Brilon and Wu [25] x D T A Griffiths et al [92] CTT Kuwahara [136] x FLUID Griffiths et al [93] x BOT Viti and van Zuylen [223] DTA, FLUID Viti and van Zuylen [224] DTA, FLUID Blumberg-Nitzani and Bar-Gera [20] DTA Car and truck handling facilities Curry et al [45] x SIPP Deng et al [58] x x SIPP Chen and Yang [35] x FLUID Chen et al [37] x PSFFA Chen et al [32] x x PSFFA Chen et al [33] x x PSFFA Chen et al [34] x PSFFA Yang et al [237] x PSFFA Chen and Yang [36] x FLUID, PSA, PSFFA Selinka et al [196] x SBC…”

A Simheuristic Method for the Reversible Lanes Allocation and Scheduling Problem at Smart Container Terminal Gate

“…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.…”