A robotic cell - manufacturing system widely used in industry - contains two or more robot-served machines, repetitively producing a number of part types. In this paper, we consider scheduling of operations in a bufferless dual-gripper robotic cell processing multiple part types. The processing constraints specify the cell to be a flowshop. The objective is to determine the robot move sequence and the sequence in which parts are to be processed so as to maximize the long-run average throughput rate for repetitive production of parts. We provide a framework to study the problem, and address the issues of problem complexity and solvability. Focusing on a particular class of robot move sequences, we identify all potentially optimal robot move sequences for the part-sequencing problem in a two-machine dual-gripper robot cell. In the case when the gripper switching time is sufficiently small, we specify the best robot move sequence in the class. We prove the problem of finding an optimal part sequence to be strongly NP-hard, even when the robot move sequence is specified. We provide a heuristic approach to solve the general two-machine problem and evaluate its performance on the set of randomly generated problem instances. We perform computations to estimate the productivity gain of using a dual-gripper robot in place of a single-gripper robot. Finally, we extend our results for the two-machine cell to solve an m-machine problem.

This article assesses the benefits of implementing a dual-arm robot in a flow shop manufacturing cell. Such a robot has the ability to tend (unload or load) to two adjacent machines simultaneously. This significantly changes the analysis required to find sequences of robot actions that maximize a cell's throughput. For cells processing identical parts, optimal sequences are identified for two- and three-machine cells and also structural results are derived for cells with an arbitrary number of machines. Cells processing different part-types are fully analyzed for the case of two-machine cells. For each case the productivity of single-arm and dual-arm robotic cells is compared.

Throughput Optimization In Robotic Cells provides practitioners, researchers, and students with up-to-date algorithmic results on sequencing of robot moves and scheduling of parts in robotic cells. It brings together the structural results developed over the last 25 years for the various realistic models of robotic cells. This book is ideally suited for use in a graduate course or a research seminar on robotic cells.

Artificial intelligence (AI) is influencing the future of almost every sector and human being. AI has been the primary driving force behind emerging technologies such as big data, blockchain, robots, and the internet of things (IoT), and it will continue to be a technological innovator for the foreseeable future. New algorithms in AI are changing business processes and deploying AI-based applications in various sectors. The Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence is a comprehensive reference that presents cases and best practices of AI and knowledge engineering applications on business intelligence. Covering topics such as deep learning methods, face recognition, and sentiment analysis, this major reference work is a dynamic resource for business leaders and executives, IT managers, AI scientists, students and educators of higher education, librarians, researchers, and academicians.

Although several monographs and edited volumes have discussed scheduling in general, most of these works survey the field by contributing a single chapter to production systems like flow shops. Flow Shop Scheduling: Theoretical Results, Algorithms, and Applications is solely dedicated to bringing together a huge body of knowledge on the subject, along distinct design features, in order to help scholars and practitioners easily identify problems of interest. This monograph has been organized into ten distinct flow shop systems and explores their connections. The chapters cover flow shop systems including two-machine, flexible, stochastic, and more. Outside of the traditional flow shops that require a job never revisits any stage, this book also examines the reentrant flow shop, in which a job may cycle back and be reprocessed at the same station or sequence of stations, multiple times. The authors have made the material accessible to a broad readership, using simplified notation and revealing unifying concepts. The results unique to flow shop research should provide the seed for research in other areas of scheduling and in optimization in general.

Manufacturing systems, regardless of their size, have to work with scarce resources in dynamic environments. Effective Resource Management in Manufacturing Systems aims to provide methods for achieving effective resource allocation and to solve related problems that occur daily and often generate cost overruns. This book will be bought by postgraduate students of business, engineering and computer science as well as researchers in these fields. It will also be of interest to practitioners in manufacturing systems and operations managers in industry.

This handbook provides full coverage of the most recent and advanced topics in scheduling, assembling researchers from all relevant disciplines to facilitate new insights. Presented in six parts, these experts provides introductory material, complete with tutorials and algorithms, then examine classical scheduling problems. Part 3 explores scheduling models that originate in areas such as computer science, operations research. The following section examines scheduling problems that arise in real-time systems. Part 5 discusses stochastic scheduling and queueing networks, and the final section discusses a range of applications in a variety of areas, from airlines to hospitals.