In the course of the 11th International Conference on Variable Neighborhood Search (ICVNS 2025), Angelo Sifaleras presented the paper entitled “Variable Neighborhood Programming for Job Shop Scheduling Problems”.
The publication was authored by Triantoglou, M.- A., Sifaleras, A. and Benmasour, R.
The Job Shop Scheduling Problem is a classic combinatorial optimization problem and one of the most well-studied scheduling problems. Several methodologies, both exact and metaheuristic, have already been proposed for the solution of this computationally difficult problem. This work presents for the first time a solution approach based on Variable Neighborhood Programming for the Job Shop Scheduling Problem. Variable Neighborhood Programming is a recent methodology which constitutes a combination of Genetic Programming and Variable Neighborhood Search. In addition, some encouraging comparative computational results are also shown against the state-of-the-art Gurobi optimization solver using medium- and large-scale benchmark instances. The findings of this work have a plethora of modern applications in Manufacturing-as-a-Service online platforms. All experimental evaluations were performed on the Google Cloud Platform.
You can find the publication in this link.