ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 29 Jan 2020 09:16:17 GMT2020-01-29T09:16:17Z5051- An optimisation framework for yard planning in a container terminal: Case with automated rail-mounted gantry craneshttps://scholarbank.nus.edu.sg/handle/10635/51854Title: An optimisation framework for yard planning in a container terminal: Case with automated rail-mounted gantry cranes
Authors: Ku, L.P.; Lee, L.H.; Chew, E.P.; Tan, K.C.
Abstract: Different terminals, with their unique combinations of liner services, yard layouts and equipment configurations, may find that different yard planning strategies work better for their scenarios. While an optimum yard plan can be found for each yard planning strategy, it is interesting to know which strategy gives the best plan. In designing an IT-based search engine to discover the best yard planning strategy and/or scenario, having a generic specification and solver is important, so that the whole solution space could be represented and searched. We design a generic problem specification with parameterised scenarios and yard planning strategies, and formulate a generic mathematical model that solves for the optimum weekly yard plan template for that given problem. A good run time of this generic model is extremely important as the model will be executed hundreds of times in the search engine. Experiments are conducted with the model. An interesting discovery is that re-modelling a set of integer variables into multiple binary variables improve the run time tremendously, and in some cases, outperform the relaxed original model. We also find that the strategy which allows sharing of yard space between services yield better utilization for yard space and rail mounted gantry handling capacity. © 2010 Springer-Verlag.
Thu, 01 Jul 2010 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/518542010-07-01T00:00:00Z
- A novel approach to yard planning under vessel arrival uncertaintyhttps://scholarbank.nus.edu.sg/handle/10635/51847Title: A novel approach to yard planning under vessel arrival uncertainty
Authors: Ku, L.P.; Chew, E.P.; Lee, L.H.; Tan, K.C.
Abstract: Many container terminals in the world adopt the consolidated yard planning strategy, where containers to be loaded into the same vessel are stacked in groups. This has been a good strategy because when a vessel is loading, yard cranes will be stationed at these locations, and the trucks shuttle between the quay cranes and the yard cranes almost in a conveyor belt fashion. These locations are optimally chosen such that no two groups of containers are stacked in close vicinity if they are to be loaded simultaneously. However, when there is a change in vessel arrival schedule, it may cause congestion of trucks at yard locations where groups of containers in near vicinity are loading simultaneously. While the Robust Optimisation community may suggest having a robust plan - a plan that is immune to uncertainty, in this paper, we will like to find a solution that allows us to change easily when uncertainty reveals - a plan that is nimble. While the optimum solution for the nimble plan could be intractable, we explore various heuristics that enable us to find good solutions. © Springer Science+Business Media, LLC 2011.
Sat, 01 Sep 2012 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/518472012-09-01T00:00:00Z
- Discovering interesting holes in datahttps://scholarbank.nus.edu.sg/handle/10635/99500Title: Discovering interesting holes in data
Authors: Liu, B.; Ku, L.-P.; Hsu, W.
Abstract: Current machine learning and discovery techniques focus on discovering rules or regularities that exist in data. An important aspect of the research that has been ignored in the past is the learning or discovering of interesting holes in the database. If we view each case in the database as a point in a it-dimensional space, then a hole is simply a region in the space that contains no data point. Clearly, not every hole is interesting. Some holes are obvious because it is known that certain value combinations are not possible. Some holes exist because there are insufficient cases in the database. However, in some situations, empty regions do carry important information. For instance, they could warn us about some missing value combinations that are either not known before or are unexpected. Knowing these missing value combinations may lead to significant discoveries. In this paper, we propose an algorithm to discover holes in databases.
Wed, 01 Jan 1997 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/995001997-01-01T00:00:00Z
- Note on optimal tile partition for space region of integrated-circuit geometryhttps://scholarbank.nus.edu.sg/handle/10635/99349Title: Note on optimal tile partition for space region of integrated-circuit geometry
Authors: Ku, L.-P.; Leong, H.W.
Abstract: An OTP algorithm for solving the optimal tile-partitioning problem has recently been published. The OTP algorithm makes use of an elimination algorithm to find a maximum set of nonintersecting critical partition edges. It is shown that this elimination algorithm is flawed. © IEE, 1996.
Mon, 01 Jan 1996 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/993491996-01-01T00:00:00Z
- Optimum partitioning problem for rectilinear VLSI layouthttps://scholarbank.nus.edu.sg/handle/10635/99369Title: Optimum partitioning problem for rectilinear VLSI layout
Authors: Ku, L.-P.; Leong, H.W.
Abstract: The authors consider the optimum partitioning problem defined as follows: given a rectilinear layout £consisting of n rectangles, it is desirable to partition (decompose) the remaining free space into rectangular free blocks using horizontal and/or vertical partition edges such that the number of free blocks is minimised. The authors give a new formula for counting the number of free blocks in any partition of a given layout. Based on the new formula, they show that the optimum partitioning problem reduces to the problem of finding a maximum independent vertex set (MIS) in a bipartite graph. They then give an O(/z2-5) optimum partitioning algorithm (OPA) for computing an optimum partition. This optimum partitioning algorithm can be used to improve the space and time complexities of many applications where space partitioning is encountered. One example is the corner stitching data structure used for design rule checker (DRC) in the layout system Magic. In the experiments, the space complexity of the corner stitching data structure can be improved by an average of 13% by using the proposed optimum partitioning. Other applications are also presented. © lEE, 1997.
Wed, 01 Jan 1997 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/993691997-01-01T00:00:00Z