Abstract
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 problem are known to be NP-hard, and therefore, many efforts have been devoted to the development of approximation algorithms for solving them. In this paper, we design a parallel randomized approximation algorithm for the non-preemptive single machine scheduling problem with release dates and delivery times (1|rj,qj|Cmax), where the objective is to minimize the completion time of all jobs (i.e., makespan). To evaluate the performance of the proposed algorithm, we carry out a comprehensive experimental analysis on several instances of the problem. The results indicate that the proposed parallel algorithm can efficiently solve large instances achieving significant speedup on parallel systems with multiple cores.
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