Shafipour, "Design of an efficient genetic algorithm for resource-constrained unrelated

parallel machine scheduling problem with machine eligibility restrictions," Journal of Intelligent Manufacturing, 2015.

The second topic of related works is concerned with the

parallel machine scheduling (PMS).

Literature Review on the Hybrid Flow Shop Scheduling Problem with Unrelated

Parallel MachinesTo address these challenges, we first take advantages of the

parallel machine learning to reduce the execution time and achieve the tradeoff between detection accuracy and time consumption.

At least one of the processes has multiple

parallel machines. The problem to be solved is to determine the allocation of

parallel machines, as well as the processing sequence of jobs on the same machine.

In this section, mixed integer programming (MIP) mathematical models are formulated to find optimal solutions for the unrelated

parallel machine scheduling problem with sequence dependent setup times and job splitting.

Akhshabi [17] found a uniform

parallel machine scheduling problem which is to minimize the maximum lateness.

Scheduling of Non-Related

Parallel Machines with Sequence Dependent Setup Times and Dynamic Entry using Genetic Algorithms

Consequently, a program decomposition that is efficient on one

parallel machine might become inefficient on another

parallel machine.

The kinematics of both machines is based upon the same 3-CPU topology but the joints are differently assembled so as to obtain a translating

parallel machine (TPM) with one mechanism and a spherical

parallel machine (SPM) with the other.

Lokesh and Jain (2011) developed a three-phased methodology to decide RMS configuration in the desired period considering various features such as multiproduct line, number of stages, selection of machine type, machine configuration, number of same type

parallel machine in a stage, machine selection for all stages, allocation of stage functionality blocks in each stage, etc., and various constraints and performance measures satisfaction, but existing configuration consideration is also important to select RMS configuration in the desired period.

The Linux cluster model designed is a collection of Personal Computers (PCs) networked together as a single resources in order to share their processors and other resources for computations and analysis that could be performed on any

parallel machine. The cluster consists of a PC deisgnated as the master while the other PCs on the network are the computational nodes.