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

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 metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), 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 the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) 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, using four benchmark sets and 101 test problems.

Automated summary

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that has been extensively studied to model and optimize more complex situations reflecting the current needs of the industry. This work introduces a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), which utilizes the concepts of a cellular automaton to generate and share information among a set of leading solutions called smart-cells. Experimental results demonstrate the satisfactory performance of the GLNSA algorithm when compared with recent algorithms, using four benchmark sets and 101 test problems.