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This paper presents a general kind of flow shop scheduling problem in a manufacturing supply chain where a group of jobs can be processed on a machine simultaneously.

According to the technology and the kind of machines used in a work environment and variety of products, setup times can be dependent on both machines and the sequence of jobs that should be processed on a machine.

Constraint (13) guarantees that each part is assigned to be processed on a machine in a period t with a worker w at a location l and in a cell k.

We consider an open shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine.

Noori-Darvish and Tavakkoli-Moghaddam (2012) proposed a novel bi-objective mathematical programming for an open-shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine.

Similar(55)

Mahout is scalable and it can support massive datasets that are too large to be processed on a single machine.

A set of jobs is to be processed on a single machine and each job belongs to a specific group.

Formally, there is a set of n jobs with identical processing time, J = { j 1, …, j n } = { 1, …, n }, to be processed on a single machine.

If the data can fit into the system memory, then clusters are usually not required and the entire data can be processed on a single machine.

In the order scheduling problem, every job (order) consists of several tasks (product items), each of which will be processed on a dedicated machine.

The main result is a bound of e/(e−1) for the on-line problem with objective minimizing the sum of completion times of jobs that arrive over time at their release times and are to be processed on a single machine.

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