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Access Type

WSU Access

Date of Award

January 2019

Degree Type


Degree Name



Computer Science

First Advisor

Daniel Grosu


In this thesis, we address two important scheduling problems, single machine scheduling and flow shop scheduling problem.

Single machine scheduling is a very fundamental scheduling problem with extensive applications in various areas ranging from computer science to manufacturing. Also, this problem is the building block of different decomposition-based algorithms for shop scheduling problems.

Most variants of the single machine scheduling

problems are known to be NP-hard, and therefore, many efforts have been

devoted to the development of approximation algorithms for solving them.

We design a parallel randomized approximation algorithm for

the non-preemptive single machine scheduling problem with release dates and delivery times~($1|r_j, q_j|C_{max}$), where the objective is to minimize the completion time of all jobs~(i.e., makespan).

We also address the flow shop scheduling problem as an application of the proposed algorithm for single machine scheduling problem. We design efficient parallel algorithms for

solving large-size flow shop scheduling problems by

leveraging the huge amount of computing power of the current multi-core computing systems.

We design two parallel algorithms based on the Shifting Bottleneck heuristic.

The first one is a coarse-grained parallel algorithm that is suitable for execution

on multi-core systems with a small number of cores, while the second one is a

fine-grained parallel algorithm suitable for multi-core systems with a

large number of cores.

We perform extensive experimental analyses to evaluate the performance of the proposed algorithms for instances of various sizes.

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