Template Design Problem Research Articles

Enhanced and Efficient Guiding Template Design for Lamellar DSA With Graph Monomorphism

Lamellar directed self-assembly (DSA) technology in combination with the self-aligned via (SAV) process has emerged as a novel and promising choice for via/contact layer fabrication, where a via between Metal-x and Metal-(x+1) is generated at the intersection of a wire segment on Metal-(x+1) and a rectangular and perpendicular guiding template. Compared to the highly investigated cylindrical DSA, lamellar DSA does not suffer from the overlay error of generated holes and thus can have better yield, and it also benefits from the better flexibility of various via pitches a single template can produce. A state-of-the-art work has addressed the guiding template design problem for the new process, and an integer linear programming (ILP) formulation that can optimally solve the problem as well as a simple heuristic approach are proposed. However, solving the ILP formulation is time-consuming, and the heuristic method suffers from unignorable degradation in solution quality due to several algorithm deficiencies. In this paper, we propose an enhanced and efficient template design algorithm flow for the lamellar DSA and SAV process by using graph monomorphism for pattern conflict check in multiple patterning lithography. Experimental results show that the proposed flow can greatly reduce the numbers of conflicts and short templates and is almost as efficient as the existing heuristic approach.

Minimum Cost Reconfigurable Network Template Design With Guaranteed QoS

Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.

Tactical berth and yard template design at container transshipment terminals: A column generation based approach

Quay-side berthing congestion is an emerging challenging issue that arises in busy container transshipment terminals and calls for effective management of terminal operations. This paper tackles the berthing congestion problem by introducing a proactive management strategy from the terminal’s perspective that adjusts the calling schedule of feeder vessels in such a way that the quay-side workload distribution in the temporal dimension can be balanced. Such a schedule template design problem is considered simultaneously with another two tactical level decision problems, berth template design (i.e., determining preferred berthing positions for vessels) and yard template design (i.e., allocating storage yard space to transshipment flows). This highly integrated problem is formulated as a set covering model. Heuristic methods based on column generation are developed to obtain near-optimal solutions in an efficient way. Computational experiments on real-world sized test instances demonstrate the efficiency and effectiveness of the proposed approach.

A method for effective yard template design in container terminals

Effective storage space planning is important and complex, especially for container terminals with congested storage yards. The technique of yard template design, which normally specifies a set of storage locations in a group of yard blocks for export containers of various vessel services, is particularly useful for allocating workloads evenly among yard blocks to smooth the landside operations of such container terminals. This research studies the yard template design problem with periodical vessel calling schedules. In order to control the workload and make efficient use of yard storage space, we specify two planning phases in a hierarchical solution approach and formulate the problems as integer programming models. Numerical experiments indicate that the problems cannot be efficiently solved by means of an exact integer programming approach and therefore a heuristic is designed to specify the exact shape and location for stacking containers in a storage plan template. Further experiments show that our solution method generates good storage templates for most test cases. It contributes to efficient stacking and handling for export containers in yards.

A Novel Extremal Optimization Approach for the Template Design Problem

This paper presents a novel algorithm based on extremal dynamics for tackling the template design problem, a constrained optimization problem that originated in the printing industry. The template design problem involves printing several variations of a design onto one or more stencil sheets, where the aims are to minimize the number of stencils as well as the overproduction of prints of a particular design. In this paper, the authors introduce several search operators to be used in conjunction with the proposed algorithm. Different combinations of these search operators are tested via extensive numerical experiments. The solutions indicate that the algorithm is a feasible approach for template design optimization. In particular, hybridizing it with a deterministic local search has proven to be very effective.

Yard planning for vessel services with a cyclical calling pattern

In a land-scarce container terminal, congestions in the terminal yard due to highly concentrated workload often lead to unsatisfactory terminal productivity. Currently, yard planners use their experience to design a yard template for determining the storage locations of export containers to be loaded onto vessels deployed to services with a cyclical calling pattern. This article studies the problem of designing a yard template that balances the workload in an export yard. The template design problem is formulated as an integer program. It is found that the computational time required to optimally solve realistic sizes of the template design problem is unacceptably long. This article proposes a simpler integer program as an approximate model. On the basis of the analysis on the approximate model, a heuristic is developed to solve the template design problem. Results of computational experiments show that the heuristic can find effective solutions for the template design problem.

Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach

This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point. The current study characterizes the template design problem as a standard LMI problem and the optimization parameters of the templates are carried out by PSO. Finally, the examples are given to illustrate the effectiveness of the proposed method.

Template design under demand uncertainty by integer linear local search

Production planning under uncertain demands leads to optimization problems that are hard both to model and to solve. We describe an integer linear model for a template design problem under uncertainty, and investigate its solution by a general-purpose local search algorithm for integer linear programs. Several such algorithms have previously been proposed as tools for solving large combinatorial optimization problems, and ours is based on a recent Boolean Satisfiability algorithm. In experiments it was slower than other methods on small instances, but rapidly outstripped them as the problem size and number of templates increased. It also found near-optimal solutions to all instances much more quickly. A general-purpose local search algorithm provides a rapid and convenient way of finding high-quality solutions to complex production problems.

Berth management in container terminal: the template design problem

One of the foremost planning problems in container transshipment operation concerns the allocation of home berth (preferred berthing location) to a set of vessels scheduled to call at the terminal on a weekly basis. The home berth location is subsequently used as a key input to yard storage, personnel, and equipment deployment planning. For instance, the yard planners use the home berth template to plan for the storage locations of transshipment containers within the terminal. These decisions (yard storage plan) are in turn used as inputs in actual berthing operations, when the vessels call at the terminal. In this paper, we study the economical impact of the home berth template design problem on container terminal operations. In particular, we show that it involves a delicate trade-off between the service (waiting time for vessels) and cost (movement of containers between berth and yard) dimension of operations in the terminal. The problem is further exacerbated by the fact that the actual arrival time of the vessels often deviates from the scheduled arrival time, resulting in last-minute scrambling and change of plans in the terminal operations. Practitioners on the ground deal with this issue by building (capacity) buffers in the operational plan and to scramble for additional resources if needs be. We propose a framework to address the home berth design problem. We model this as a rectangle packing problem on a cylinder and use a sequence pair based simulated annealing algorithm to solve the problem. The sequence pair approach allows us to optimize over a large class of packing efficiently and decomposes the home berth problem with data uncertainty into two smaller subproblems that can be readily handled using techniques from stochastic project scheduling. To evaluate the quality of a template, we use a dynamic berth allocation package developed recently by Dai et al. (unpublished manuscript, 2004) to obtain various berthing statistics associated with the template. Extensive computational results show that the proposed model is able to construct efficient and robust template for transshipment hub operations.

Integer Linear Programming and Constraint Programming Approaches to a Template Design Problem

We describe a design problem arising in the color printing industry and discuss a number of integer linear programming and constraint programming approaches to its solution. Despite the apparent simplicity of the problem it presents a challenge for both approaches. We present results for three typical cases and show that the constraint programming approach provides better results, although in some cases after considerable handcrafting. We also show that the results obtained by constraint programming can be improved by a simple goal programming model.