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Solving Job Shop Scheduling with Parallel Population-Based Optimization and Apache Spark

  • Piotr Jedrzejowicz
  • Izabela WierzbowskaEmail author
Conference paper
  • 46 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 193)

Abstract

The paper proposes an architecture for the population-based optimization in which Apache Spark is used as a platform enabling parallelization of the process of search for the best solution. The suggested architecture, based on the A-Team concept, is used to solve the Job Shop Scheduling Problem (JSP) instances. Computational experiment is carried out to compare the results from solving a benchmark set of the problem instances obtained using the proposed approach with other, recently reported, results.

Keywords

optimization Apache spark Job shop scheduling 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Information SystemsGdynia Maritime UniversityGdyniaPoland

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