Buffer capacity allocation in unreliable production lines: An adaptive large neighborhood search approach

The design of a production line directly affects the system performance which is usually measured by its throughput. The problem involving determination of the optimal capacity and location of the buffers in a production line is known as the buffer allocation problem (BAP). Due to the difficulties such as the NP hard structure of the problem and not being able to be defined the throughput of the line in terms of the buffer capacities algebraically, meta-heuristic search algorithms are widely used to solve the BAP. In this study, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve the BAP for throughput maximization in unreliable production lines. Different from the literature, for the first time, ALNS algorithm is employed to solve the problem of designing a production line. For this purpose, two different removal-insertion operator pairs are proposed and employed in an adaptive way by considering the nature of the problem. Moreover, a new initialization procedure based on the well-known storage bowl phenomenon concept is proposed to reduce the search effort. Performance of the proposed algorithm was tested on the existing benchmark instances. A computational study demonstrated the benefits of not only the adaptive mechanism embedded into the proposed algorithm but also the proposed initialization procedure. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

Automated summary

This study introduces an adaptive large neighborhood search (ALNS) algorithm to solve the problem of buffer allocation in production lines for maximizing throughput. The algorithm's adaptive mechanism and initialization procedure have shown significant benefits in reducing search efforts and improving performance.