Doctor of Philosophy (PhD), Ohio University, 2024, Industrial and Systems Engineering (Engineering and Technology)

With the onset of technology-driven solutions, the warehousing and logistics sectors are witnessing transformative advancements, one of which is the Robotic Compact Storage and Retrieval System (RCSRS). This research presents a comprehensive examination of RCSRS through three interrelated chapters. The first chapter provides an exhaustive literature review, presenting existing findings and gaps in different types of automated storage systems that have been studied, comparing their characteristics, similarities, and differences. The second chapter pioneers the study of robot travel time within RCSRS, introducing an innovative Mixed-Integer Non-Linear Programming (MINLP) model optimized using a Genetic Algorithm (GA) approach. This investigation primarily provides insights like the optimal placement of the Input/Output (I/O) point and the significance of digging time as a critical bottleneck, while also setting the stage for future research directions. Lastly, the third chapter studies the performance of three optimization algorithms in the RCSRS context: Genetic Algorithm (GA), Simulated Annealing (SA), and a novel Greedy Heuristic. This study aims to minimize robot bin moves, recognizing its direct impact on time and energy utilization. Remarkable findings such as the Greedy Heuristic's efficiency for moderate-sized order lists and the SA's aptness for larger order lists have been detailed. Together, these chapters offer an expansive view into RCSRS's potential and the strategies to harness it, contributing valuable insights and methodologies for the warehousing and logistics sectors. The research anticipates fostering advanced RCSRS designs, optimizing operations, and guiding future research in this transformative domain.

Committee: Tao Yuan Dr. (Committee Chair); Dale Masel Dr. (Committee Co-Chair); Vardges Melkonian Dr. (Committee Member); Ashley Metcalf Dr. (Committee Member); Aros-Vera Felipe Dr. (Committee Member) Subjects: Engineering; Robotics

Doctor of Business Administration, Cleveland State University, 2017, Monte Ahuja College of Business

Organizations often begin knowledge management (KM) efforts by building knowledge repositories to store organizational knowledge to ensure that it may be later retrieved to reuse, share with, and transfer to knowledge workers. The use of such storage/retrieval systems (S/RS) are particularly relevant in preserving and restoring internal organizational knowledge; such implementations support reduced costs associated with knowledge reacquisition, recreation, and reinvention, thus increasing the efficiency of knowledge transfer. Additionally, there is an increased interest in newer uses of S/RS to support large-scale knowledge-bases and knowledge sharing communities. Therefore, it is important for organizations to understand the factors that influence success in S/RS, as generally, KM systems (KMS) initiatives have failed to realize promised results. This study focuses on knowledge flow from the knowledge repository to the knowledge consumer to facilitate and enable knowledge transfer (FEKT). Because of the strong relationship between S/RS processes and technologies and IS/IT, DeLone and McLean's (2003) IS success model serves as the foundation for the S/RS success model, which is modified here to include the complexities inherent in an S/RS. This empirical study presents a model of S/RS success in FEKT and identifies, prioritizes, and weights both the constructs that define S/RS success and the critical success factors (CSF) that influence these success constructs. In addition to informing KM practitioners, this research also addresses a research gap in the KM literature in respect to storage/retrieval systems in facilitating knowledge transfer. Moreover, while prior KMS research has generally assumed an independence in factors and constructs when empirically testing KMS success, this study embraces the notion that real-world factors and constructs are interrelated, intertwined, and interdependent; thus, the analytic network process (ANP) is used as an analytic method (open full item for complete abstract)

Committee: Radha Appan Ph.D. (Committee Chair); Oya Tukel Ph.D. (Committee Member); Timothy Arndt Ph.D. (Committee Member); Birsen Karpak Ph.D. (Committee Member) Subjects: Information Systems

Master of Science (MS), Ohio University, 1983, Industrial and Manufacturing Systems Engineering (Engineering)

Design of an automated warehouse teaching system

Committee: B Khoshnevis (Advisor) Subjects: Engineering, Industrial