In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or Cmax. This is a type of assignment problem that has been freque...

Let there be m parallel machines and n jobs, where a job j can be scheduled on machine i to take pi, j ∈ Z+ time units. The makespan Cmax is the completion time of a machine that finishes last. The goal is to produce a schedule with all n jobs that has minimum makespan. This is the makespan problem on unrelated parallel machines, denoted as R||Cmax. Assume p,q ∈ Z+ are constants, let A(p,q) = {...

Classes of scheduling problems can be specified in terms of the three-field classification α|β|γ where • α specifies the machine environment, – P parallel identical – Q uniform machines: each machine has a given speed speed i, and all jobs have a size sizej, the processing time is given by sizej/speed i – R unrelated machines: the processing time of job j on machine i is given by pi,j, without ...

This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help u...

<p style='text-indent:20px;'>We consider parallel-machine scheduling in the context of shared manufacturing where each job has a machine set to which it can be assigned for processing. Such is called processing set. In setting, not only certain machines processing, but also processed on remaining at cost. Compared with traditional rejection, model under study embraces notion sustainable m...

Untranslated words still constitute a major problem for Statistical Machine Translation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called “low density” languages. But pivoting requires additional parallel texts. We...