Hadi Charkhgard

Sasan Mahmoudinazlou, Abhay Sobhanan, Hadi Charkhgard et al.

Order picking is a pivotal operation in warehouses that directly impacts overall efficiency and profitability. This study addresses the dynamic order picking problem, a significant concern in modern warehouse management, where real-time adaptation to fluctuating order arrivals and efficient picker routing are crucial. Traditional methods, which often depend on static optimization algorithms designed around fixed order sets for the picker routing, fall short in addressing the challenges of this dynamic environment. To overcome these challenges, we propose a Deep Reinforcement Learning (DRL) framework tailored for single-block warehouses equipped with an autonomous picking device. By dynamically optimizing picker routes, our approach significantly reduces order throughput times and unfulfilled orders, particularly under high order arrival rates. We benchmark our DRL model against established algorithms, utilizing instances generated based on standard practices in the order picking literature. Experimental results demonstrate the superiority of our DRL model over benchmark algorithms. For example, at a high order arrival rate of 0.09 (i.e., 9 orders per 100 units of time on average), our approach achieves an order fulfillment rate of approximately 98%, compared to the 82% fulfillment rate observed with benchmarking algorithms. We further investigate the integration of a hyperparameter in the reward function that allows for flexible balancing between distance traveled and order completion time. Finally, we demonstrate the robustness of our DRL model on out-of-sample test instances.

George Dunn, Hadi Charkhgard, Ali Eshragh et al.

Order Picker Routing is a critical issue in Warehouse Operations Management. Due to the complexity of the problem and the need for quick solutions, suboptimal algorithms are frequently employed in practice. However, Reinforcement Learning offers an appealing alternative to traditional heuristics, potentially outperforming existing methods in terms of speed and accuracy. We introduce an attention based neural network for modeling picker tours, which is trained using Reinforcement Learning. Our method is evaluated against existing heuristics across a range of problem parameters to demonstrate its efficacy. A key advantage of our proposed method is its ability to offer an option to reduce the perceived complexity of routes.

Alvaro Sierra-Altamiranda, Hadi Charkhgard, Iman Dayarian et al.

In this paper, we investigate the possibility of improving the performance of multi-objective optimization solution approaches using machine learning techniques. Specifically, we focus on multi-objective binary linear programs and employ one of the most effective and recently developed criterion space search algorithms, the so-called KSA, during our study. This algorithm computes all nondominated points of a problem with p objectives by searching on a projected criterion space, i.e., a (p-1)-dimensional criterion apace. We present an effective and fast learning approach to identify on which projected space the KSA should work. We also present several generic features/variables that can be used in machine learning techniques for identifying the best projected space. Finally, we present an effective bi-objective optimization based heuristic for selecting the best subset of the features to overcome the issue of overfitting in learning. Through an extensive computational study over 2000 instances of tri-objective Knapsack and Assignment problems, we demonstrate that an improvement of up to 12% in time can be achieved by the proposed learning method compared to a random selection of the projected space.