In automated production systems like flexible manufacturing systems (FMSs), an important issue is to find an adequate workload for each machine for each time period. Many integer linear programming (ILP) models have b...
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This paper presents an integer linear programming (ILP) model for the scheduling problem in high level synthesis. In addition to time-constrained scheduling and resource-constrained scheduling, a new scheduling proble...
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This paper presents an integer linear programming (ILP) model for the scheduling problem in high level synthesis. In addition to time-constrained scheduling and resource-constrained scheduling, a new scheduling problem called feasible scheduling is constructed, which provides a paradigm for exploring the solution space. Extensive consideration is given to the following applications: scheduling with;chaining;multicycle operations by nonpipelined function units;multicycle operations by pipelined function units;functional pipelining;loop folding;mutually exclusive operations;scheduling under bus constraint;minimizing lifetimes of variables. The complexity of the number of variables in the formulation is O (s . n) where s and n are the number of control steps and operations, respectively. Since we use the as soon as possible (ASAP), as late as possible (ALAP), and list scheduling techniques to reduce the solution space, the formulation becomes very efficient. A solution to a practical problem, such as the fifth-order filter, can be found optimally in a few seconds.
This paper presents a novel analog computational network for solving NP-complete constraint satisfaction problems, i.e., job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based o...
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This paper presents a novel analog computational network for solving NP-complete constraint satisfaction problems, i.e., job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, we propose to use linear cost functions. As a result, the network complexity (number of interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linearprogramming network described by Tank and Hopfield, which also uses a linear cost function for a simple optimization problem. In this paper we show how to map a difficult constraint satisfaction problem (job-shop scheduling) onto a simple neural net, where the number of neural processors equals the number of subjobs (operations), and the number of interconnections grows linearly with the total number of operations. Simulations show that our approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the TSP-type Hopfield approach and integer linear programming approach of Foo and Takefuji in terms of the quality of the solution and the network complexity, i.e., the number of neurons and the number of resistive interconnections.
We propose a new approach for reducing the density of a channel by routing some nets (or subnets) over the cells (i.e., outside the channel). Previous research assumed that the more nets being routed over the cells th...
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We propose a new approach for reducing the density of a channel by routing some nets (or subnets) over the cells (i.e., outside the channel). Previous research assumed that the more nets being routed over the cells the greater the reduction in the channel density. We show that only the removal of critical nets contributes to the reduction in the channel density. We divide channel into zones where each zone has a zone density and the removal of any net from a zone will reduce its density by one. In order to reduce the channel density, only certain critical zones need to have their nets routed over the cells. A bipartite graph is used to represent the relationship between nets and zones. The problem is transformed into a constrained covering problem and formulated as an integer linear programming problem. In comparison with previous research, our approach reduces more channel densities while using fewer tracks over the cells. For Deutsch's difficult channel, a previous approach needs 15 tracks over the cells to reduce the channel density by 3 while we need only 5 tracks to achieve the same result.
The authors consider the problem of scheduling a set of simultaneously available jobs on several parallel machines. Specifically, the minimization of the time to finish all the jobs assigned to all machines of schedul...
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The authors consider the problem of scheduling a set of simultaneously available jobs on several parallel machines. Specifically, the minimization of the time to finish all the jobs assigned to all machines of scheduling sequence under job deadline constraints for the n jobs, m machines problem is formulated. The simulated annealing and fast simulated annealing algorithms are reviewed and adopted for the scheduling problem. A large number of simulations were carried out which provides an empirical basis for comparing the application of classical simulated annealing and fast simulated annealing algorithms to the scheduling problem.< >
An integer linear programming (ILP) model is presented for the design of optimal parallel testing, which minimizes the index number of the maximal complete subgraph (MCS) in the parallel testing graph (PTG), therefore...
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ISBN:
(纸本)081862275X
An integer linear programming (ILP) model is presented for the design of optimal parallel testing, which minimizes the index number of the maximal complete subgraph (MCS) in the parallel testing graph (PTG), therefore, making the index coloring number of the PTG minimal. The whole test time of a partitioned circuit is thus minimal. An optimal testing scheduling algorithm of a partitioned circuit is also presented in polynomial time. An existent ILP program is used to prove the correctness of this model. Sufficient experimental results corresponding to this ILP model are offered.< >
A novel design methodology for data path synthesis using multiport memories is presented which can be applied to scheduled algorithms or to already synthesized data paths as a postprocessor to reduce the design space....
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A novel design methodology for data path synthesis using multiport memories is presented which can be applied to scheduled algorithms or to already synthesized data paths as a postprocessor to reduce the design space. Based on simple and clear but powerful principles, the proposed technique not only groups variables to a minimum number of multiport memories depending on their ports and taking into consideration the variables' access requirements, but also minimizes their interconnection hardware (such as buses, multiplexers, and tri-state buffers) to functional units. The system, MAP, supports the synthesis of architecture in both linear topology and random topology for the application-specific design. The minimization problems have been formulated as 0-1 integer linear programming problems. Experiments on benchmarks show very promising results and the CPU time for all the benchmarks is less than 1.4 s.< >
This paper reports the investigation of the neural network solution to the zero-one optimal covering problem via computer simulation. The key idea used in this exploration is that for every covering problem there exis...
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This paper reports the investigation of the neural network solution to the zero-one optimal covering problem via computer simulation. The key idea used in this exploration is that for every covering problem there exists an equivalent integer linear programming problem which can be solved by modifying the linearprogramming neural net circuit proposed by Tank and Hopfield. Simulation results indicate that this method works very well.< >
A novel approach is presented to the operation scheduling problem in a data path synthesis. After obtaining the start time and the require time of each operation by the ASAP (as soon as possible) and ALAP (as late as ...
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A novel approach is presented to the operation scheduling problem in a data path synthesis. After obtaining the start time and the require time of each operation by the ASAP (as soon as possible) and ALAP (as late as possible) methods, respectively, an integer linear programming (ILP) formulation is formed to solve the scheduling problem. The objective is to fully utilize the hardware resources, i.e. to minimize the requirement of function units under a given timing constraint. The formulation can be generalized to support multicycle operations, multiple operations per cycle, pipelined data paths, mutually exclusive operations, and variables' lifetime consideration in a data path. A fifth-order filter containing 26 addition and 8 multiplication operations can be scheduled optimally for the cases from 17 cycles to 21 cycles per minute on a VAX-11/8800.< >
Presents a sea-of-gates layout system able to design medium-size logic circuits in a true channelless fashion. The methodology relies on flexible leaf cell generation, systematic cell terminal abutment, a global routi...
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Presents a sea-of-gates layout system able to design medium-size logic circuits in a true channelless fashion. The methodology relies on flexible leaf cell generation, systematic cell terminal abutment, a global routine scheme using integer linear programming methods, and a step-wise compaction-rerouting refinement. Modules up to several hundred transistors have been laid out compactly with more than 80% transistor utilization with two layers of metal. With top-down hierarchy, those modules can be used as macrocells.< >
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