A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

TL;DR

This paper introduces a novel meta-heuristic algorithm, GLNSA, combined with tabu search, to improve solutions for the flexible job shop scheduling problem, demonstrating competitive performance on benchmark instances.

Contribution

The paper proposes a new global-local neighborhood search algorithm for FJSP, integrating cellular automaton concepts with tabu search to enhance optimization capabilities.

Findings

Outperforms recent algorithms on benchmark problems

Achieves better solutions in 2 out of 86 test cases

Shows satisfactory performance across diverse instances

Abstract

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new meta-heuristic algorithm called GLNSA (Global-local neighborhood search algorithm), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called "smart_cells" generates and shares information that helps to optimize instances of FJSP. The GLNSA algorithm is complemented with a tabu search that implements a simplified version of the Nopt1 neighborhood defined in [1] to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms and widely cited in the specialized bibliography, using 86 test problems, improving the optimal result reported in previous works in two of them.