Ziyan Zhao’s research while affiliated with Northeastern University and other places

With the development of robotics and Internet of Things, robot-assisted goods-to-person order picking systems become popular in smart warehouses. Order picking in such systems is a human-robot collaborative process, where robots carry pods to a picking station with human pickers who pick the demanded goods from them to fulfill orders. In it, pod selection, robot scheduling, and manual picking are highly coupled and together influence the efficiency of order picking. Their joint optimization is the key to enhancing operational efficiency but rarely studied in existing work. In order to fill such a research gap and meet high market demand, this work focuses on a novel human-robot collaborative order picking optimization problem. A mixed integer program is formulated to model it and provide an exact solution method for small-scale instances. To provide large-scale problems with efficient solutions in practical application scenarios, we propose an adaptive large-neighborhood-based tabu search algorithm. Specifically, an adaptive large neighborhood search method is designed and embedded into a tabu search algorithm with two tabu mechanisms. Experimental results indicate that the presented algorithm has significant advantages in solving the newly proposed problem. It substantially outperforms: 1) the independent use of adaptive large neighborhood search or tabu search, 2) Gurobi subject to an hour execution time, and 3) several competitive benchmark and newest well-performing algorithms. Its high performance implies its great potential in solving practical order picking optimization problems for Internet-of-Things-enabled robot-assisted smart warehouses.

Path finding in multi-agent systems aims to identify collision-free and cost-optimized paths for all agents with distinct start and goal positions. It poses challenging optimization problems. Existing research typically treats all agents equally, overlooking their differences in practical scenarios where they undertake the tasks of varying importance. In many application scenarios, agents must be differentiated into critical (c-agents) and acritical ones (a-agents) due to premium/general service, no-loaded/full-loaded states, and urgent/non-urgent tasks. Facing this practical need, this work focuses on multi-agent systems in which the different importance of agents must be considered; and tackles a lexicographic dual-objective variant of path-finding problem. The consideration of c-agents makes the concerned problem more useful yet more challenging than basic multi-agent path-finding problems. Different from existing multi-agent path planning methods that minimize the sum-of-costs of all agents, we optimize two objectives with preferences to emphasize the influence of c-agents on the system. The primary one is to minimize the sum-of-costs of c-agents and the secondary one is to minimize that of a-agents. As existing methods are inadequate for this unique challenge, we adapt a conflict-based search framework and design new two-level lexicographic dual-objective optimization methods to deal with it. A high level is responsible for iteratively expanding a search tree and adding constraints to resolve conflicts among agents. A low level is responsible for finding the path of each agent for the node newly expanded in the high level. By conducting numerous computational experiments, we verify the great performance of the presented methods in solving the concerned problem. We further develop a prototype system incorporating our methods and make it public to promote their practical application. This research contributes valuable insights and solutions to pathfinding challenges in multi-agent systems with critical and acritical agents. Note to Practitioners —This work addresses a multi-agent path finding problem in multi-agent systems involving both critical and acritical agents. The former are more important than the latter since they are assigned to perform more important tasks. This is a common scenario in manufacturing and service environments. We propose a two-level lexicographic dual-objective optimization framework to deal with the problem and underscore the importance of c-agents in the path finding problem. Three solution approaches are designed for practitioners to select based on their practical application needs. The computational experimental results and statistical analyses highlight the exceptional performance of our proposed approaches. In order to facilitate the practical application, we further provide a prototype system with our proposed approaches embedded, which is openly accessible for practitioners to realize their specific applications.

An oversampling technique balances a dataset by increasing the number of minority samples. It is a common and effective method in imbalanced learning. However, most oversampling methods have randomness in generating minority samples, which would have negative impacts on the prediction performance of subsequent classifiers. This study treats the prediction made by classifiers as a black-box optimization problem. The optimization objective is to improve the classification accuracy of subsequent classifiers for minority samples. The solution of this optimization problem can be regarded as a minority sample that can be and added to the imbalanced dataset. The minority samples are iteratively generated by Bayesian optimization (BO). We determine two valuable intervals for each 1-D continuous variable feature. One is the interval with the densest minority samples. The other is that with the sparsest majority samples distributed among the minority samples. By adjusting the proportion of samples generated in the two areas, the presented algorithm can be flexibly applied to different datasets. In order to reduce the noise that may be caused by the exploration phase of BO, a sample selection procedure is carried out to eliminate the samples that are worse than those generated at the previous iteration. The samples generated in this way are based on the principle of improving the performance of the classifier, thus avoiding the negative effects of randomness. Experimental results via twenty open imbalanced datasets show that the proposed method obtains better results than existing state-of-the-art oversampling models, thus well advancing the important field of imbalanced learning.