Abdelmoutalib Metrane
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Abdelmoutalib Metrane has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Imad Hafidi
Sultan Moulay Slimane University
2 shared publications - Hajar Boualamia
University Sultan Moulay Slimane, Beni Mellal, Morocco
1 shared publication - Mehdi Nasri
Sultan Moulay Slimane University
1 shared publication - Oumaima Mellouli
University Sultan Moulay Slimane, Beni Mellal, Morocco
1 shared publication
Latest Publications
- 7 April 2023
- Published by Springer Nature
- p. 3-14
A New Adaptation Mechanism of the ALNS Algorithm Using Reinforcement Learning
- 7 April 2023
- Published by Springer Nature
- p. 3-14
A New Adaptation Mechanism of the ALNS Algorithm Using Reinforcement Learning
- Open Access
Multithreading Parallel Robust Approach for the VRPTW with Uncertain Service and Travel Times
The objective of this paper is to consider the vehicle routing problem with time windows under two uncertainties: service and travel times. We introduce new resolution approaches for the robust problem and an efficient parallel procedure for the generation of all possible scenarios. The best robust solution of each scenario can be achieved by using a parallel adaptive large neighborhood search metaheuristic. Through our analysis, we expect to find the best compromise between the reduced running time and a best good solution, which leads to four distinct combinations of parallel/sequential approaches. The computational experiments are performed and tested on Solomon’s benchmark and large randomly generated instances. Furthermore, our results can be protected against delay in service time in a reasonable running time especially for large instances.
Open AccessMultithreading Parallel Robust Approach for the VRPTW with Uncertain Service and Travel Times
The objective of this paper is to consider the vehicle routing problem with time windows under two uncertainties: service and travel times. We introduce new resolution approaches for the robust problem and an efficient parallel procedure for the generation of all possible scenarios. The best robust solution of each scenario can be achieved by using a parallel adaptive large neighborhood search metaheuristic. Through our analysis, we expect to find the best compromise between the reduced running time and a best good solution, which leads to four distinct combinations of parallel/sequential approaches. The computational experiments are performed and tested on Solomon’s benchmark and large randomly generated instances. Furthermore, our results can be protected against delay in service time in a reasonable running time especially for large instances.