Maryam Karimi-Mamaghan

TL;DR: This paper provides a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation.

TL;DR: This paper models a single allocation multi-commodity hub-and-spoke network problem through a bi-objective mathematical model, considering the congestion in both hubs and connection links, and proposes a novel aggregation model based on a general GI/G/c queuing system.

TL;DR: In this article , a novel iterated greedy algorithm based on reinforcement learning is proposed for solving combinatorial optimization problems, which incorporates Q-learning to select appropriate perturbation operators during the search process.

TL;DR: This paper presents an agile integrated inspection-operation planning model wherein inspection actions are planned alongside the machining operations to make the production process agile, and addresses the uncertainty of manufacturing parameters and equipment disruptions.

TL;DR: A novel aggregation model based on a queueing network approach, so-called queue-based aggregation (QAG) model, to estimate the cycle time of a job-shop production system that consists of several processing workstations, and in which products are transferred via an Automated Material Handling System (AMHS).