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About 6,008 papers for “machine assignment”

The operator-machine assignment problem

Edward F. Stafford · European J of Industrial Engineering · 2007

This paper describes three approaches to solving the operator-machine assignment problem. Difference equations are used to prove the optimality of the third approach, a three-step simple and quick solution technique. Techniques for performing sensitivity analyses on the operator and machine operating cost values are presented and illustrated with an example. Four additional objective functions, in addition to the classical one of minimising per unit manufacturing cost, are presented and discussed. [Received 29 November 2006; Revised 5 April 2007; Accepted 27 May 2007]

Cited by 1

Spectrum Assignment Algorithm for Cognitive Machine-to-Machine Networks

Soheil Rostami, Sajad Alabadi, Soheir Noori et al. · Mobile Information Systems · 2016

A novel aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M) networks is proposed. The introduced algorithm takes practical constraints including interference to the Licensed Users (LUs), co-channel interference (CCI) among CM2M devices, and Maximum Aggregation Span (MAS) into consideration. Simulation results show clearly that the proposed algorithm outperforms State-Of-The-Art (SOTA) algorithms in terms of spectrum utilisation and network capacity. Furthermore, the convergence analysis of the proposed algorithm verifies its high convergence rate.

Cited by 1Open access

Static single assignment form for machine code

Allen Leung, Lal George · ACM SIGPLAN Notices · 1999

Static Single Assignment (SSA) is an effective intermediate representation in optimizing compilers. However, traditional SSA form and optimizations are not applicable to programs represented as native machine instructions because the use of dedicated registers imposed by calling conventions, the runtime system, and target architecture must be made explicit. We present a simple scheme for converting between programs in machine code and in SSA, such that references to dedicated physical registers in machine code are preserved. Our scheme ignores all output- and anti-dependences imposed by physical registers while a program is in SSA form, but inserts compensation code during machine code reconstruction if any naming requirements have been violated. By resolving all mismatches between the two representations in separate phases, we are able to utilize existing SSA algorithms unaltered to perform machine code optimizations.

Cited by 6

Comparative analysis on Virtual Machine assignment algorithms

Priyanka C.P, Sankari Subbiah · 2017

The resource allocation is an important concept in cloud computing. It is an emerging technology in modern computing systems, it provides an on demand services because it offers dynamic allocation of resources to provide reliable and high available services to the users. To manage the actual hardware resource of the underlying Physical Machine (PM), many number of jobs (user requests) are executed on a virtual machine. Virtual Machine (VM) placement algorithm deals about placing different VMs onto the existing physical machines in an efficient manner so that the load is balanced optimally among all the hardware resources available. The Random Resource Allocation (RRA) algorithm means that the jobs are placed into the VM randomly and allocating VM into the PM randomly. This survey paper gives an overview of the existing virtual machine placement techniques and the proposed random resource allocation algorithm reduces resource wastage and power consumption and also provides load balancing in servers.

Cited by 8

Static single assignment form for machine code

Allen Leung, Lal George · 1999

Static Single Assignment (SSA) is an effective intermediate representation in optimizing compilers. However, traditional SSA form and optimizations are not applicable to programs represented as native machine instructions because the use of dedicated registers imposed by calling conventions, the runtime system, and target architecture must be made explicit. We present a simple scheme for converting between programs in machine code and in SSA, such that references to dedicated physical registers in machine code are preserved. Our scheme ignores all output- and anti-dependences imposed by physical registers while a program is in SSA form, but inserts compensation code during machine code reconstruction if any naming requirements have been violated. By resolving all mismatches between the two representations in separate phases, we are able to utilize existing SSA algorithms unaltered to perform machine code optimizations.

Cited by 32Open access

Parallel machine scheduling with job assignment restrictions

Celia A. Glass, Hans Kellerer · Naval Research Logistics (NRL) · 2006

Abstract In the classical multiprocessor scheduling problem independent jobs must be assigned to parallel, identical machines with the objective of minimizing the makespan. This article explores the effect of assignment restrictions on the jobs for multiprocessor scheduling problems. This means that each job can only be processed on a specific subset of the machines. Particular attention is given to the case of processing times restricted to one of two values, 1 and λ, differing by at most 2. A matching based polynomial time ε‐approximation algorithm is developed that has a performance ratio tending to $2-{1 \over 1+\lambda}$ . This algorithm is shown to have the best possible performance, tending to 3/2, for processing times 1 and 2. For the special case of nested processing sets, i.e., when the sets of machines upon which individual jobs may be assigned are non‐overlapping, the behavior of list scheduling algorithms is explored. Finally, for assignment restrictions determined by just one characteristic of the machines, such as disc storage or memory constraint in the case of high performance computing, we contribute an algorithm that provides a 3/2 worst case bound and runs in time linear in the number of jobs. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Cited by 81

State Assignment for Finite State Machine Synthesis

Meng Yang · Journal of Computers · 2013

This paper proposes simulated annealing based algorithm for the synthesis of a finite state machine to determine the optimal state assignment with less area and power dissipation. The algorithm has two annealing stages. In the first rough annealing stage it tries to search in global scope by the proposed rough search method. In the second focusing annealing stage it tries to search in local scope by using proposed focusing search methods intending changing solution slightly. In both stages, the experience of past solution is utilised by combing the best solution in the past and the current solution. The experiments performed on a large suite of benchmarks have established the fact that the proposed method outperforms the published GA-based algorithms. The results have shown the effectiveness of the proposed method in achieving optimal state assignment for finite state machine.

Cited by 12

Multitasking parallel-machine scheduling with machine-dependent slack due-window assignment

Min Ji, Wenya Zhang, Lijuan Liao et al. · International Journal of Production Research · 2018

We consider the problem of parallel-machine scheduling with machine-dependent slack (SLK) due-window assignment in the multitasking environment, which exists in various application domains such as Internet services, project management, and manufacturing. Motivated by practical observations, we extend the original model of multitasking to a more general model where each job’s interruption proportion depends on the job itself and its processing position. In the light of individualised service, we consider SLK due-window assignment. Our objective is to minimise the total cost that comprises the earliness, tardiness, and due-window-related costs. Finding that an optimal schedule exists when each machine is occupied by at least one job, we show that the problem is polynomially solvable. We provide a more efficient solution algorithm for a special case of the problem. Finally, we present numerical examples to illustrate the application of the theoretical results and working of the solution algorithms.

Cited by 37

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment

Yuanqing Wang, Iván Pulido, Kenichiro Takaba et al. · The Journal of Physical Chemistry A · 2024

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

Cited by 24Open access

When to Use Machine Learning: A Course Assignment

Kayhan, Varol · Journal of the Association for Information Systems · 2022

The number of institutions that offer machine learning courses continues to increase. However, supplementary materials that help instructors teach these courses fail to address an important step in the machine learning process; that is, conceptualizing a problem using a valid input-output relationship. To address this issue, I first review frameworks in extant work before proposing a decision flow. After discussing steps in the decision flow, I present a course assignment that reinforces the concepts in the decision flow. I conclude by discussing the lessons learned after using this assignment in a graduate course at a university in the United States.

Cited by 0

Towards Effective Issue Assignment using Online Machine Learning

Athanasios Michailoudis, Themistoklis Diamantopoulos, Antonios Favvas et al. · 2025

Efficient issue assignment in software development relates to faster resolution time, resources optimization, and reduced development effort. To this end, numerous systems have been developed to automate issue assignment, including AI and machine learning approaches. Most of them, however, often solely focus on a posteriori analyses of textual features (e.g. issue titles, descriptions), disregarding the temporal characteristics of software development. Thus, they fail to adapt as projects and teams evolve, such cases of team evolution, or project phase shifts (e.g. from development to maintenance). To incorporate such cases in the issue assignment process, we propose an Online Machine Learning methodology that adapts to the evolving characteristics of software projects. Our system processes issues as a data stream, dynamically learning from new data and adjusting in real time to changes in team composition and project requirements. We incorporate metadata such as issue descriptions, components and labels and leverage adaptive drift detection mechanisms to identify when model re-evaluation is necessary. Upon assessing our methodology on a set of software projects, we conclude that it can be effective on issue assignment, while meeting the evolving needs of software teams.

Cited by 1Open access

The Design of an Automatic Machine Assignment System

M. W. S. Barlow · Journal of the SMPTE · 1975

Certain features of a switching system used to assign videotape machines to recording and playback functions lend themselves to automation control. A suitable system was built and installed at the Canadian Broadcasting Corp., Toronto, to control operations of eight Presentation Area VTRs. Some features of the design are discussed, including modifications introduced after some operational use in order to give greater reliability and better operational features. Although based on a particular VTR assignment system, the comments are applicable to systems using any type of machinery requiring unattended automatic switching.

Cited by 0

A note on parallel-machine due-window assignment

Dan Xu, Chung‐Lun Li, Qiaoming Han · Journal of the Operational Research Society · 2011

We consider a due-window assignment problem on identical parallel machines, where the jobs have equal processing times and job-dependent earliness-tardiness costs. We would like to determine a ‘due window’ during which the jobs can be completed at no cost and to obtain a job schedule in which the jobs are penalized if they finish before or after the due window. The objective is to minimize the total earliness and tardiness job penalty, plus the cost associated with the size of the due window. We present an algorithm that can solve this problem in O(n3) time, which is an improvement of the O(n4) solution procedure developed by Mosheiov and Sarig.

Cited by 1