This paper proposes a learning-based framework for resolving preference conflicts in priority assignment by reverse-engineering task priorities from historical scheduling data. The framework integrates four modules: Conditional Generative Adversarial Network(CTGAN)-based data augmentation, Bayesian networks (BNs) for interpretable preference modeling, a novel Generative Adversarial Network(GAN)-based approach for priority learning, and constraint-relaxation optimization. This architecture systematically transforms fragmented subproblems into a integrated priority learning framework. The methodology employs differentiable proxies to extract implicit priority structures directly from sparse historical allocation patterns. umerical experiments demonstrate consistent improvements over baseline methods in scheduling accuracy. In a surgical scheduling case study, the framework attained 85.2% operating room assignment accuracy and 72.3% time slot accuracy, outperforming historical schedules by 6.17% in total reward. The results validate the framework’s effectiveness in extracting conflicting preferences and priorities from data, offering significant practical value in complex scheduling domains with resource conflicts.

Accurate prediction of surgery duration is vital for efficient operating room (OR) scheduling but remains difficult due to the compositional and variable nature of surgical procedures. Current practices are usually based on historical averages and existing models rely on coded procedures and structured variables, overlooking multiple operations and unstructured narratives, which lead to inaccurate predictions, affecting operating room efficiency, patient safety and surgery completion. This study proposes a multi-operation modeling framework integrating medical Named Entity Recognition with probabilistic deep learning. Free-text operative notes are transformed into structured triplets of procedure type, body site, and surgical approach. These operation-level representations are aggregated by an attention mechanism and passed to a Mixture Density Network for distributional prediction. Experiments on 40,436 surgical cases from a tertiary hospital show consistent gains over regression, tree-based, and neural baselines, reducing performance indicators by up to 17% and 20% respectively with well-calibrated uncertainty. Attention weights highlight influential operations, enabling interpretable and reliable predictions. Such a framework provides a compact and practical solution for interpretable, uncertainty-aware, and data-driven operating room management.

Reducing energy consumption plays a critical role to implement the carbon peak and carbon neutral strategies in manufacturing processes with energy-intensive setups and operations. In this paper, a serial production line model with setups is introduced, where both productivity and energy consumption performances are considered. First, exact solutions of both measures are derived for two-machine lines. Next, using the results in two-machine lines as a building block, an aggregation and interpolation based approximation method is presented to evaluate system production rate and energy usage for longer lines. Using this method, system properties and sensitivities are investigated. Extensions to more complex scenarios with multiple downtimes are also discussed.Note to Practitioners—Achieving the carbon peak and carbon neutral goals has been the national strategy in response to climate change. With such a strategy, reducing energy consumption in manufacturing processes is of critical importance. Many manufacturing systems include energy-intensive setups and operations, which complicates the study of such systems and proposes new challenges in modeling and analysis of system performance. To solve this problem, a system-theoretic method is presented in this paper. At first, a two-machine line model is introduced and an exact solution can be derived to calculate the line production rate and energy consumption. Then, for longer lines, the evaluation method in two-machine lines is utilized as a building block to update system performance repeatedly. Specifically, by aggregating the setup states into down states, the model is simplified. Then, using backward and forward aggregations, an iterative method is introduced to overcome the curse of dimensionality. In addition, through interpolation based on coefficient of variation (CV), the estimates of system performance can be obtained. Moreover, by examining the performances, system-theoretic properties, such as monotonicity and reversibility, as well as sensitivity are investigated. Furthermore, it is shown that the proposed model can be extended to analyze more complex cases with multiple sequential downtimes. Such a model provides an effective method to design, analyze, and improve productivity and energy efficiency in production systems.

Collaboration between humans and robots has great promise in manufacturing systems. The utilization of cobots in a manufacturing system can improve both productivity and ergonomics. In this paper, we study the problem of how to allocate limited cobot/robots to manufacturing systems with multiple workstations so that an integrated performance measure, considering both productivity and ergonomics is optimized. Previous work on cobot/robot allocation in manufacturing systems focus on the decomposition of tasks for a single workstation into multiple work elements, and then split them between human and robots, rather than studying multi-machine systems. To bridge this gap, we consider the allocation of cobots/robots to a multi-stage manufacturing system. Specifically, we establish an integrated performance measure and formulate cobot/robot allocation into a constraint integer programming problem. With this formulation, we obtain the optimal allocation of one available cobot/robot in simulated production systems, based on the integrated performance measure of productivity and ergonomics. Furthermore, the allocation problems of production systems with multiple cobots/robots is considered and solved with a scalable algorithm.Note to Practitioners—Collaborative robots and independent robots are increasingly applied to manufacturing production systems. However, how to optimally allocate both types of robots considering both productivity and ergonomics influence has not been well studied. In this article, we established a practical optimization method to allocate cobots/robots to different workstations and split the work between cobot and human in one workstation when there are multiple workstations and a limited number of available cobots/robots in the manufacturing systems. Based on various real-world scenarios, we inferred useful insights for the robot/cobot allocation problem. To deal with the computational load when the number of workstations is large, a scalable optimization algorithm is also adopted. The case study results demonstrated the effectiveness of the proposed approach.

Patients suffering from chronic diseases often receive interventions after they are discharged from hospitals for their health management. The intervention decisions can be made sequentially during the course of disease progression and treatment process, and timely and proper interventions can lead to better health condition and quality of life and reduce the rate of re-hospitalization. Moreover, to determine effective intervention plans, personalized care should be considered. However, in many cases, intervention plans are not tailored to patients’ health conditions or dynamically adjusted based on their status changes due to the lack of adequate analytical methods. To address this issue, we introduce a causal Bayesian network-based Markov decision process (CNMDP) model to dynamically evaluate the health status and effectiveness of interventions for each patient and update the corresponding intervention strategy. In such a model, a causal Bayesian network is integrated in order to predict the effect of interventions in each patient’s health status. The resulting risk at each decision point is considered in a Markov decision process model by examining diverse factors of a patient to make a personalized optimal decision on intervention planning to minimize the risk of health deterioration or readmission. To illustrate the applicability of the model and develop a specific formulation, an intervention decision model for chronic obstructive pulmonary disease (COPD) patients is presented using the proposed method. Such a model can be applied to other diseases as well to design personalized, prompt, and effective intervention plans. Note to Practitioners—Substantial efforts have been devoted to treatment and care management of chronic diseases. Particularly, there has been an initiative to design appropriate follow-up plans and interventions for postdischarge care to reduce hospital readmissions. However, many intervention plans are not patient specific and do not change with patient conditions. To mitigate such an limitation, based on identified risk factors and causal relationships, a causal Bayesian network model is introduced to predict readmission risk and evaluate intervention effectiveness. The results are then embedded into a Markov decision process to derive optimal intervention plan to minimize readmission risk. Such a process is repeated dynamically to evaluate health status and update intervention strategies. Using chronic obstructive pulmonary disease (COPD) as an example of chronic disease, a case study at a community hospital is presented to illustrate the applicability of the model.

Improving workflow efficacy in operating rooms (ORs) is of significant importance for hospital management. Safe delivery of surgical procedures, better utilization of staff and medical resources, as well as enhanced efficiency and satisfactory patient outcome are expected. To achieve this, quick and accurate analysis of surgery workflow becomes a critical enabler. Although extensive studies have been carried out in OR scheduling, efficient methods for detailed workflow analysis in ORs are missing in the current literature. To bridge the gap, a system-theoretic approach is introduced to study surgical workflow in ORs. Specifically, a Markov chain-based analytical model is presented to evaluate the performance of surgical processes. To mitigate the curse of dimensionality, an iterative algorithm is introduced for large surgical centers. Using this model, system properties, such as monotonicities, are investigated. Numerical experiments indicate that such a method can provide efficient analysis with acceptable accuracy. In addition, a case study in a large public hospital illustrates the applicability of the method.Note to Practitioners—Operating room is one of the most expensive and critical units in a hospital, and the associated surgical processes are complex and random. Therefore, accurate and efficient analysis of surgery workflow is needed to better plan and schedule surgery activities for improved quality of care and patient outcomes. This paper presents a system-theoretic approach to study OR workflow, which can quickly and effectively evaluate OR performance, such as throughput, length of OR stay, staff and OR utilizations, and investigate the impacts of changes in settings and demands. To avoid state space explosion in Markov chain analysis, an iterative method is presented to significantly reduce computation intensity. Finally, an application in a public hospital illustrates that such a method provides an efficient quantitative tool for OR management.

The COVID-19 pandemic has not only posed a significant threat to health, life, economy, and the whole society but also led to numerous new theoretical and practical challenges for automation science and engineering. The goal of this Special Issue is to bring together researchers and practitioners into a forum to show the state-of-the-art research and applications in responding to the challenges and opportunities of automation science and engineering to the pandemic, by presenting efficient scientific and engineering solutions, addressing the needs and difficulties for integration of new automation methodologies and technologies, and providing visions for future research and development.

Production systems include both discrete part and batch operations, where an individual part is manufactured in a discrete operation, and a group of parts are processed simultaneously, i.e., in a batch, on one machine for a batch operation. Many manufacturing industries, such as battery, aircraft, and automotive, consist of mixed batch and discrete part operations, referred to as batch-discrete lines. Although such operations are widely encountered, analytical studies of these systems are limited in current literature. In this paper, a systems approach is presented to model and analyze batch-discrete lines. First, a Bernoulli machine reliability model for a two-machine batch-discrete system is introduced. Using a virtual buffer to represent the batch processing feature, performance evaluation formulae are derived and system properties are investigated. Using them, improvement analyses and bottleneck identification are presented. Then, the model is extended to systems with a quality inspection device under different control policies. To illustrate the applicability of the model, a case study in a composite part production process is described. Such a work delivers a quantitative tool for production engineers and managers to design, analyze, and improve batch-discrete manufacturing systems.Note to Practitioners—Many manufacturing systems in aircraft, automotive, battery, medical device, and defense industries include both batch operation and discrete part processing machines. In a batch operation, multiple parts are manufactured simultaneously on a batch machine, while a single part is made in a discrete part machine. Production lines with mixed batch and discrete part operations are named as batch-discrete systems. Analysis and improvement of such systems are critical to ensure high productivity and quality. However, accurate modeling and analysis of batch-discrete systems are lacking in current literature. To bridge this gap, a novel methodology is presented in this paper. Using a Bernoulli reliability machine model with a virtual buffer concept, performance measures are derived for batch-discrete two-machine lines, as well as the reversed discrete-batch lines. Then system properties, such as monotonicity, interchangeability, and reversibility, are investigated, followed by improvement analysis under constraints and bottleneck analysis. By extending to systems with quality inspections, two quality control policies, to scrap either the current batch or the whole inventory after detecting a degraded part, are studied. In addition, a case study of heating (batch) and trimming (discrete) operations in composite panel production lines is presented, and improvement strategies are investigated, to illustrate how to apply the model and analysis in practice.

The 15th annual IEEE International Conference on Automation Science and Engineering (CASE 2019) was held on August 22–26, 2019, at The University of British Columbia, Vancouver, BC, Canada. IEEE CASE represents the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners.

Performance analysis of flexible manufacturing systems has received substantial research attention. Most of the studies focus on steady-state behavior. The transient performance, which is critical for system operation, control, and improvement, is less investigated. No analytical method is available to evaluate the transients in flexible lines with unreliable machines, finite buffers, and setup times. In this article, an analytical model is introduced to study the transient performance of multiproduct serial lines with Bernoulli reliability machines and nonnegligible setups during changeovers. To reduce the state-space dimension, an approximation method is introduced to evaluate the performance in two-machine lines, where the sequence of multiple part types in the inventory is approximated by the ratio of product mix. Such an approximation method leads to high precision of production rate estimation. Then, for longer lines, the analysis approach in two-machine lines is used as a building block in an iterative aggregation procedure. Numerical experiments show that the iteration procedure is convergent and results in an acceptable accuracy in performance evaluation. In addition, system properties, such as monotonicity and reversibility, are investigated.

Opioid misuse and overdose have become a public health hazard and caused drug addiction and death in the United States due to rapid increase in prescribed and non-prescribed opioid usage. The misuse and overdose are highly related to opioid over-prescription for chronic and acute pain treatment, where a one-size-fits-all prescription plan is often adopted but can lead to substantial leftovers for patients who only consume a few. To reduce over-prescription and opioid overdose, each patient's opioid usage pattern should be taken into account. As opioids are often prescribed for patients after total joint replacement surgeries, this study introduces a machine learning model to predict each patient's opioid usage level in the first 2 weeks after discharge. Specifically, the electronic health records, patient prescription history, and consumption survey data are collected to investigate the level of short-term opioid usage after joint replacement surgeries. However, there are a considerable number of answers missing in the surveys, which degrades data quality. To overcome this difficulty, a semi-supervised learning model that assigns pseudo labels via Bayesian regression is proposed. Using this model, the missing survey answers of opioids amount taken by the patients are predicted first. Then, based on the prediction, pseudo labels are assigned to those patients to improve classification performance. Extensive experiments indicate that such a semi-supervised learning model has shown a better performance in the resulting patients classification. It is expected that by using such a model the providers can adjust the amount of prescribed opioids to meet each patient's actual need, which can benefit the management of opioid prescription and pain intervention.

A patient's stay at a hospital may encompass various departments or units. Since many critical and complex problems occur at the interfaces of healthcare delivery systems, safe and efficient transitions between the departments within a hospital has significant importance. This paper presents a Markov chain-based model to study patient transitions between emergency department, intensive or critical care unit, and hospital ward in small and medium-sized community hospitals. To make the analytical study tractable, an iteration method is introduced to approximate the system performance during transitions, including direct transferring probabilities without waiting, average patient occupancy in each department, and average patient length of stay. In addition, system properties, such as monotonicity and sensitivity, are analyzed. It is shown that such a method has a high accuracy in performance estimation and can be used to study and improve patient transitions in small or medium-sized hospitals.

Primary care is the backbone of the U.S. healthcare delivery system. In primary care clinics, teams led by providers play a central role in care delivery. A care service task typically requires joint efforts among the care providers and support staff in the team. Thus, communications within a team are critical to ensure effective collaboration and coordination between team members to provide high quality of care. Due to such an importance, team communication and collaboration have received a considerable amount of research attention. However, most research is qualitative or based on empirical studies. This paper introduces an analytical framework of modeling, analysis, and improvement of team communication and collaboration process in primary care clinics. Specifically, using a queueing network model, the physicians, nurses, and medical assistants are modeled as servers and the communication and collaboration tasks are viewed as customers. The team members interact with each other multiple times to accomplish a task. The efficiency of the team communication and collaboration process is characterized by task throughput, task completion time, and the number of iterations to finish a task. Analytical formulas to evaluate such performances are derived and system properties are investigated. In addition, bottleneck analysis is carried out to identify the constraint that impedes the system performance in the strongest manner. Finally, a case study at a primary care clinic is presented to illustrate the applicability of the model.

This paper introduces a system-theoretic approach to improve inpatient discharge process at the University of Wisconsin (UW) Hospital. The complex hospital discharge process is modeled by a stochastic process with parallel subprocesses, splits, merges, and reworks. Then, a stochastic analysis method is introduced to evaluate the performance of discharge. Specifically, the waiting and service times are characterized by gamma distributions, and an efficient algorithm is presented to aggregate the multiple interacting subprocesses and calculate the mean, variability, and discharge-time performance, i.e., the probability to discharge a patient within a desired or given time interval. High accuracy in performance evaluation is obtained by using such a method. To improve the discharge process at UW Hospital, bottleneck and what-if analyses are carried out and improvement recommendations are discussed.

Safe and efficient patient transitions are of critical importance to ensure patient safety and care quality. To study patient transitions, this paper presents a queueing network model-based iteration method to model and analyze transitions between emergency department, intensive care unit, and general ward within a hospital. Routings with feedback flows are considered under general arrival and service processes, and the effects of blocking on performance measures are presented for both the mean and variability. It is shown that the iteration procedure is convergent and leads to acceptable accuracy of estimation, for both small- and large-sized hospitals. In addition, the impacts of bed capacity, admission rate, as well as arrival and service time variabilities are discussed. Such a method provides an efficient way to study patient transitions within hospitals.

This paper is devoted to modeling and analysis of intervention process to reduce hospital readmissions of patients with chronic obstructive pulmonary disease (COPD). As compliance is a major issue among COPD patients, and economic burden and social support can be important factors affecting compliance and readmission, we propose to hospital management to reimburse out-of-pocket and transportation costs for COPD patients visiting primary care physicians and rehab centers, which are used as incentives to encourage them complying with patient-specific intervention plan. Then, we introduce an optimization model to minimize COPD readmission rate under incentive budget constraint and patients' readmission risks. Solving the problem, the minimal readmission rates are evaluated and the conditions to achieve the optimal solution are derived, which can provide a guideline for hospital management to plan appropriate incentive budget and benchmark the desired readmission rate. A case study at a community hospital is presented to illustrate the method. Finally, cost-effective analysis, sensitivity studies, and implementation discussions are carried out.

There has been growing interest in healthcare delivery systems worldwide coupled with a recent influx of funding into the area. Due to rapid development in information and network technology, smartness and interconnectivity have become a central issue in healthcare delivery. Automation is important for healthcare delivery systems engineering. In recent years, the significant changes in healthcare delivery and the rapid development in data analytics, artificial intelligence, robotics, and wearable devices have generated numerous opportunities for innovation in automation for smart and interconnected healthcare delivery systems. In addition, many new challenges have emerged in order to apply and implement these innovations. Such opportunities and challenges have significantly expanded the scopes of traditional automation science and engineering. Therefore, to show the state-of-the-art research and applications in the general area of healthcare delivery systems automation and to address the needs and challenges for the integration of new automation technologies in healthcare delivery, this Special Issue serves as a forum to bring together researchers, clinicians, and healthcare practitioners to present efficient scientific and engineering solutions and to provide visions for future research and development.

This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis.

Early diagnosis and treatment of lung cancer are of significant importance. In this paper, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients who receive surgical resections. The complex care delivery process is decomposed into a collection of serial processes, each consisting of combinations of various tests and procedures. Closed formulas are derived to estimate the mean and coefficient of variation of waiting time during the diagnosis-to-surgery process. Simple indicators based on the data collected on the clinic/hospital floor are derived to identify the bottlenecks, i.e., the waiting times that impede the whole delivery process in the strongest manner. In addition, by approximating waiting times using Gamma distributions, an algorithm is introduced to evaluate the waiting-time performance, i.e., the probability to finish the diagnosis-to-surgery process within a desired or given time interval. Finally, a case study at Baptist Memorial Health System is introduced to illustrate the applicability of the method and provide recommendations for improvement.

Selective assembly has been employed to obtain high-precision assemblies of two mating parts. Most studies only consider the case where machines are reliable and the buffer capacity is infinite. However, unreliable machines and finite buffers are commonly observed in many assembly systems, such as battery pack assemblies and powertrain production lines in the automotive industry. This paper studies a selective assembly system with two component machines, two finite buffers, and one assembly machine. Each component can exhibit different quality behaviors. Bernoulli machine reliability models are assumed. Analytical methods based on a two-level decomposition procedure are developed to evaluate the system performance efficiently. Numerical experiments suggest that the iteration always converges and can deliver high estimation accuracy. Extension to larger systems is also discussed.

Manufacturing systems are becoming more and more flexible to confront uncertainty in demands of multiple product types. In this paper, we study the properties of flexible manufacturing systems from the perspective of demand shortfall and service level as functions of setup time, nonhomogeneous capacities, and demand distributions. Closed-form bounds as well as an accurate estimate of the service level are obtained. Sensitivity analyses of service level with respect to reductions in setup time, downtime, and cycle time are carried out, and the impact of new product launch and product correlation on system flexibility is investigated.

Primary care, the backbone of the nation's healthcare system, is at the risk of collapse. Patients are dissatisfied due to poor access to care, and physicians are unhappy and burning out with an enormous amount of tasks. To improve the primary care access, many healthcare organizations have introduced electronic visits (or e-visits) to provide patient-physician communications through securing messages. In this paper, we introduce an analytical model to study e-visits in primary care clinics. Analytical formulas to evaluate the mean and variance of the patient length of visit in primary care clinics with e-visits are derived. System properties are investigated. In addition, comparisons of different scheduling policies between the office and the e-visits are carried out. The first come first serve, preemptive-resume, and non-preemptive policies are studied and the results show that the first come first serve policy typically leads to the best performance.

This paper introduces a case study at Dean East Clinic to model patient flow with joint visits by provider and medical assistant (MA). To reduce the state space dimension, a convergent iterative procedure based on Markov chain model of patient flow is proposed. The study is extended to non-Markovian case by introducing an empirical formula based on the mean and coefficient of variation (CV) of service times. The results have been validated with good accuracy using both collected and randomly generated data.

Battery management system has attracted mounting research attention recently, within which cell equalization plays a key role. Although many research and practices have been devoted to developing various structures of cell equalizers, there are still substantial opportunities for performance improvement yet to investigate. In particular, mathematical modeling and systematic analysis of equalizer systems are limited. In this paper, the performance analysis of the modularized global equalizer system for Lithium-ion battery cell equalization is conducted analytically. Specifically, a mathematical model is developed to emulate the equalization dynamics by considering both charging/discharging and energy loss. Analytical formulas are derived to evaluate the performance of the global equalizer. The introduced model is also compared with the state-of-the-art structures in terms of equalization speed and energy loss. Numerical studies show that the modularized global equalization outperforms others by its substantial reduction on energy loss with similar equalization performance and much less equalizers. In addition, a module segmentation guide is provided to facilitate the equalization system design. Lithium-based battery technology offers performance advantages over traditional battery technologies, which makes it promising in application such as automobiles, portable devices, power grid, etc. To ensure the Lithium-ion batteries working efficiently, reliably and safely, battery equalization systems play a critical rule, especially in large volume battery packs. Various equalization structures have been proposed to balance the state of charge within a string of battery cells. In this paper, we first review the state-of-the-art equalization structures in a unified model representation scheme, and then focus on a modularized global equalization structure with much less equalizers. Based on the mathematical models for performance evaluation, the modularized global equalization system outperforms the state-of-the-art structures in terms of energy loss with similar equalization speed. In addition, we provide a module segmentation guide to determine the number of modules in the system design.

On the hospital floor, prompt detection and appropriate treatment of clinical deterioration of ward patients are essential for successful rescue. In this paper, a continuous time Markov chain model is presented to describe the ward patient status and analyze the patient rescue processes, which are characterized by the transitions between different patient states, such as risk, non-risk, intervention by care providers (nurse, physician, rapid response team), or elevation to intensive care, etc. Closed formulas to calculate the probability of the patient in different states are developed for single patient case. A system-theoretic method, referred to as shared resource iteration (SRI), is developed to study the multiple patients scenario. It is justified that such an iterative method is convergent and results in a high accuracy in estimation of patient state probabilities through numerical experiments. Moreover, monotonic properties have been investigated to provide guidance for continuous improvement. Note to Practitioners-Improving patient safety is the top priority for hospital management. In the hospital wards, a patient may experience clinical deterioration during his/her stay, which can lead to serious adverse occurrences. Quick and appropriate treatments from the nurses, physicians, and rapid response teams are critical to rescue the patient. In this paper, we introduce an analytical method based on continuous time Markov chain to model and analyze the ward patient rescue process on the hospital floor. Using such a method, the steady-state probabilities of various system states, such as patient in risky or non-risky conditions, nurse, physician and rapid response team interventions, or transferring to intensive care units, etc., can be evaluated. The transitions among different states and their correlations can be investigated. The study on monotonic properties can help determine the direction of improvement efforts. Such a quantitative model can provide a tool for hospital management to evaluate patient rescue process and investigate strategies to improve patient safety from the system point-of-view.

This paper is devoted to modeling, analysis, and improvement of flexible manufacturing systems with asynchronous exponential machines and finite nondedicated buffers. In such systems, each machine may process multiple types of products with different speeds, and buffers are shared for all products. Efficient algorithms to evaluate the system performance are developed. Formulas are derived to calculate line throughput in one- and two-machine lines, and a convergent recursive algorithm is introduced for longer lines. The numerical results show that the method leads to a high accuracy in performance evaluation. Using such a model, bottleneck analysis has been carried out to identify the machine or product whose improvement will lead to the largest improvement in system throughput. Indicators based on the collected data to identify bottleneck machine and product are derived without complete calculation of the partial derivatives of system performance. Such efficient algorithms provide a quantitative tool for analysis and improvement of flexible manufacturing systems.

Mammography is the standardized testing process for early detection of breast cancer. In this paper, a system-theoretic method based on a Markov chain model is presented to analyze such processes. Specifically, the general testing process in a single exam room is formulated using a Markov chain model. To resolve the dimensionality issue, an iteration method, referred to as shared resource iteration, is introduced to analyze the scenarios of two or more exam rooms. Formulas to evaluate the patient length of stay and staff efficiency are developed. The extension to non-Markovian scenarios is also investigated and an empirical formula is proposed. The experimental results indicate that such a method results in a high accuracy of performance estimation. A case study at a breast imaging center of the University of Wisconsin Medical Foundation is presented to illustrate the applicability of the model. In addition, the impact of patient volume increase is also studied, which shows that a capacity increase is necessary to accommodate the high demand.

In modern manufacturing systems, the key performance indicators (KPIs) are defined as a set of metrics to reflect operation performance. The KPIs are not independent and may have intrinsic mutual relationships. In this paper, by categorizing such data into basic and comprehensive KPIs and supporting metrics, we introduce a hierarchical structure for identification and analysis of KPIs in manufacturing operation management, and use it to investigate pairwise relationship and dependencies between KPIs and supporting metrics.

The 11 papers in this special section were originally presented at the 2013 International Conference on Automation Science and Engineering, held August 17-21, 2013, in Madison, Wisconsin, USA. The papers can be characterized in three categories: automation in robot systems, manufacturing systems, and healthcare systems.

The work flow of surgical operations in emergency department and operating rooms can be interrupted due to various disruptions. Reducing such disruptions is of significant importance to ensure successful operations. In this paper, we introduce a continuous-time Markov chain model to analyze the disruptions and their impacts. Analytical formulas have been derived to evaluate the probabilities of normal operations and disruptions. A continuous improvement method has been developed to identify the disruption that impedes surgical operation in the strongest manner. Such a disruption is referred to as the bottleneck disruption. Specifically, the bottleneck disruption can be further categorized with respect to interruption time (BN-${\rm t}$) and frequency (BN-${\rm f}$), so that reducing the interruption time and frequency of the bottleneck, respectively, can lead to the largest improvement in normal operation. An application of the method at an emergency department of a large academic medical center is presented to illustrate the effectiveness of the model and the improvement approach.

This paper is devoted to the performance analysis and continuous improvement of multiproduct manufacturing systems. First, a Bernoulli model of multiproduct serial line with unreliable machines and finite buffers is introduced. In such a model, each machine is capable of processing multiple product types, and each buffer is shared for all products. Closed formulas have been derived to evaluate the production rate of the line with one or two machines, and recursive procedures are used to analyze longer lines. Numerical studies indicate that such a method has a high precision in performance evaluation. The system-theoretic properties, such as asymptotic property, monotonicity, and reversibility, have been investigated. Second, in order to improve system performance, bottleneck (BN) analysis has been carried out to identify the machine and product whose improvement will lead to the largest improvement in the system production rate. Various types of BNs have been defined, and the BN indicators, based on the data collected on the factory floor, are proposed to identify the BNs without the complicated calculations of the production rate and its sensitivities. Numerical experiments have justified the practical usefulness of such indicators for BN identification in multiproduct manufacturing systems. Finally, a case study is introduced to illustrate the applicability of the model and the method.

Battery management system has attracted mounting research attention recently, within which cell equalization plays a key role. Although many research and practices have been devoted to developing system-level structure of cell equalizers, there are still substantial opportunities for performance improvement yet to investigate. This paper proposes a novel architecture for battery cell equalization, referred to as modularized global equalizer. The mathematical model is developed to emulate the equalization dynamics by considering both charging/discharging and energy loss. Analytical formulas are derived to evaluate the performance of the global equalizer. The proposed method is also compared with the state-of-the-art structures in terms of equalization speed and energy loss.

Improving quality in large volume battery manufacturing systems for hybrid and electric vehicles is of significant importance. In this paper, we present a flow model to analyze and improve product quality in electrical vehicle battery assembly lines with 100% inspections and repairs for defective parts. Specifically, a battery assembly line consisting of multiple inspection stations is considered. After each inspection, defective parts will be repaired and sent back to the line. A quality flow model is introduced to analyze quality propagations along the battery production line. Analytical expressions of final product quality are derived and structural properties, such as monotonicity and sensitivities, are investigated. A bottleneck identification and mitigation method is introduced to improve quality performance. Finally, a case study is presented to illustrate the applicability of the method.

Care services within the patient rooms are the most critical and time consuming processes in patient care deliveries in emergency department, clinics, and other healthcare facilities. In this paper, we introduce a Markov chain model to study such processes. A closed, parallel, and reentrant network with limited resources is used to model the process. Formulas to evaluate the patient length of stay and staff utilizations are developed. System-theoretic properties are discussed. The extension to non-Markovian scenarios is also investigated. Such a model provides a quantitative tool for healthcare professionals to study and improve patient flow in care deliveries.

Improving the efficacy of rapid response operations in acute care delivery to ensure patient safety and care quality is of significant importance. In this paper, we study the response time performance (RTP) in rapid response operations. Such performance is defined as the probability that an appropriate decision responding to patient deterioration can be made within a desired time period. First, we derive a closed formula to evaluate the RTP by assuming exponential response time, and investigate the system-theoretic properties. Next, we introduce a bottleneck indicator to identify the response whose improvement will lead to the largest improvement in RTP. Then, we extend the study to non-exponential response time scenario. An approximation formula is proposed to evaluate RTP. Finally, a case study at the University of Kentucky Chandler Hospital is introduced to illustrate the applicability of the method.

This paper is devoted to modeling and analysis of multiproduct manufacturing systems with homogeneous exponential machines and finite buffers. In such systems, each machine processes multiple product types with different speeds, but the processing time for each product type is the same on all machines. Buffers are finite, shared for all products. Analytical methods to evaluate the system performance are developed, and system-theoretic properties are investigated. It is shown from numerical experiments that such a method has a high accuracy in performance evaluation. To improve the performance of such systems, bottleneck analysis is carried out to identify the machine and/or product whose improvement will lead to the largest improvement in system throughput.

The battery is one of the most important components in electric vehicles. In this paper, a virtual battery model, which provides a framework of battery simulation for electric vehicles, is introduced. Using such a framework, we can model and simulate the performance of a battery during its usage, such as battery charge, discharge, and idle status, the impacts of internal and external temperature, the manufacturing quality on joints, the cell capacity and balance management, etc. Such a framework can provide a quantitative tool for design and manufacturing engineers to predict the battery performance, investigate the impacts of manufacturing process, and obtain feedback for improvement in battery design, control, and manufacturing processes. Note to Practitioners-Automotive battery manufacturing has become more and more important due to the need of alternative energy source to gasoline powered engines. Although substantial amount of attention has been paid to study both individual battery cells and the battery pack as a whole, a battery model which includes interactions of all its components (cells, joints, external inputs, etc.) is not available, and the impact of manufacturing quality on battery performance has not been investigated. In this paper, a virtual battery simulation framework is developed to evaluate battery performance under different circumstances, involving the issues of cell capacity, temperature, driving profile, the joint (manufacturing) quality, etc. Such a framework can help battery design and manufacturing engineers to evaluate battery performance, investigate the impacts of manufacturing practices, and provide feedback for improvement.

The central theme of this Special Issue is emerging opportunities and future directions in automation for green manufacturing, where information technology based modeling, analysis, control and optimization are the focus areas. The purpose is to show the state-of-the-art research and applications in the general area of automation in green manufacturing, by bringing together researchers and practitioners from both academia and industry, to address the significant advancement, expose the unsolved challenges, present the critical needs for integration with new technologies, and provide visions for future research and development. This Special Issue presents original, significant and visionary automation papers describing scientific models, methods and technologies with both solid theoretical development and practical importance that improve process, efficiency, productivity, quality, and reliability in green manufacturing. The contributions in this Special Issue can be divided into the following categories in green manufacturing: renewable energy products and systems; energy savings in manufacturing processes and systems; remanufacturing system design; and emission reduction in supply chain management. Specifically, the following papers are included in this Special Issue. The first category addresses renewable energy source, which includes manufacturing and operation of alternative energy products, such as batteries for electric vehicles, and system design to support manufacturing activities using renewable energy sources, such as electricity generated by wind turbines and solar panels. The second category focuses on energy savings in manufacturing, such as operational control and robot scheduling to minimize energy consumptions in production, optimal design of facility and production to reduce energy cost. The third category extends the study to remanufacturing, which plays a significant role to achieve sustainability and multiple life cycles. This includes remanufacturing system analysis, design and optimization, such as strategies for recycling, reassembly, etc. Finally, the last category considers supply chain management, within which multiple firms work together to reduce negative environmental impact.

This paper discusses optimal control policies to achieve resilience in a class of serial manufacturing networks. Resilience is the ability of a system or enterprise to minimize the effects of a disruption. Although resilient manufacturing enterprises have been investigated in many different areas, there has been little analytical investigation into real-time control policies for resiliency. Our goal is to study the real-time resilient control for manufacturing systems through mathematical analysis. To achieve this, we start with developing a model for a general manufacturing network. Then, the optimal control problem is developed for a simple type of serial network called a Decreasing Storage Cost and Decreasing Capacity (DSCDC) network given disruptions with advance warning. It is then shown that the results of the DSCDC network can be generalized to more general serial networks.

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, setup times, etc.) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This can provide production engineers a guideline in operation management.

Rapid response to clinical deterioration plays an important role to improve patient safety. In this paper, we present an initial study on modeling and analysis of the rapid response process in acute care. Specifically, such a process is modeled as a complex network with split, merge, and parallel structures. An analytical method is developed to evaluate the decision time (from detection of patient deteriorating to a doctor's decision for treatment) and its variability. Structural properties are discussed and continuous improvement methods for identification and mitigation of bottlenecks in the rapid response operations are provided. A case study at the acute care at the University of Kentucky Chandler Hospital is introduced to validate the model, and continuous improvement recommendations are investigated. Finally, potential future work to extend the study is discussed.

In this paper, a simulation model of an emergency department (ED) at a large community hospital, Central Baptist Hospital in Lexington, KY, is developed. Using such a model, we can accurately emulate the patient flow in the ED and carry out sensitivity analysis to determine the most critical process for improvement in quality of care (in terms of patient length of stay). In addition, a what-if analysis is performed to investigate the potential change in operation policies and its impact. Floating nurse, combining registration with triage, mandatory requirement of physician's visit within 30 min, and simultaneous reduction of operation times of some most sensitive procedures can all result in substantial improvement. These recommendations have been submitted to the hospital leadership, and implementations are in progress.

In this paper, we study a re-entrant line with unreliable exponential machines and finite buffers, operating under last buffer first serve scheduling policy. First, an approximation method is presented to estimate the throughput of the re-entrant line. Then, a system approach to identify bottleneck based on blockage and starvation information is proposed. It has been shown that the approximation method results in acceptable accuracy, and the bottleneck identification method could correctly detect the bottleneck in most cases.

In this paper, we introduce a Markov chain model to evaluate the quality performance in flexible manufacturing systems with batch productions. In such a model, the product quality is a function of the transition probabilities characterizing the changes among good and defective states (where good quality or defective parts are produced during a cycle, respectively). A transition that has the largest impact on quality, i.e., whose improvement will lead to the largest improvement in quality, is defined as the quality bottleneck transition (BN-t). Analytical expressions of sensitivity of quality with respect to transition probabilities are derived. Indicators to identify bottleneck transitions based on the data collected on the factory floor are developed. Numerical experiments show that such indicators have high accuracy in identifying the correct bottlenecks and can be used as an effective tool for quality improvement effort. Finally, a case study at an automotive paint shop to improve quality through quality bottleneck transition identification is introduced.

The bottleneck (BN) of a production system is a machine with the strongest effect on the system's throughput. In this paper, a method for BN identification in serial lines with rework and Bernoulli machines is developed. The method can be applied using either calculated or measured data on blockages and starvations of the machines. For the case of calculated data, a technique for evaluating performance measures of Bernoulli lines with rework is developed. Along with these quantitative contributions, the paper provides three qualitative results. First, it shows that Bernoulli lines with rework do not observe the property of reversibility. Second, it demonstrates that downstream machines may have a larger effect on the throughput than upstream ones. Third, it demonstrates that BNs may be shifting not only because of changes in machine and buffer parameters but also due to changes in quality of parts produced.

Reentrant lines are widely used in many manufacturing systems. In this paper, we present an iterative method to approximate the performance of reentrant lines with Bernoulli reliability model under last buffer first serve (LBFS) scheduling policy. Recursive procedure to estimate the production rates of reentrant lines is developed. A case study of an automotive ignition component line with reentrant washing operations is introduced to illustrate the applicability of the method.

In many flexible manufacturing systems, batch production is often adopted to improve product quality. For example, in automotive paint shops, vehicles with same colors are typically grouped into small batches to reduce quality degradation and purge cost due to color change. In this paper, we present an analytical method to evaluate the quality performance of flexible manufacturing systems with batch operations. In addition, we investigate the impact of product sequencing and batch policies on product quality and present some insights to achieve better quality using these policies.

In this paper, we present an analytical method to evaluate the quality performance of flexible manufacturing systems with batch operations. By using a Markov chain model, a closed formula to quantify the probability of producing a good part is derived and nonmonotonic properties in quality are investigated.

In systems of general assembly line with material handling, line-side buffers need to be carefully assigned to a limited number of material delivers (drivers) for part delivery to avoid production stoppage due to material shortage. Such a problem is referred to as line-side buffer assignment problem (LBAP). In this paper, we focus on fixed zoning version of LBAP. We formulate the problem, prove its NP-hardness, and propose an algorithm based on two structural characteristics of the LBAP problem-one being the analogousness between our problem and the parallel machine scheduling (PMS) problem and the other being the monotonicity of the system throughput in the course of assigning line-side buffers to drivers. The developed algorithm globally converges with probability one when there exist feasible assignments. The algorithm is tested on a real system, and the results show that it is effective for solving the LBAP problem.

Performance evaluation of complex manufacturing systems is challenging due to many factors such as system complexity, parameter uncertainties, problem size, just to name a few. In many cases when a system is too complex to model using mathematical formulas, simulation is used as an effective alternative to conduct system analysis. A manufacturing system is a good example of such cases where both system performance and system complexity are greatly impacted by material handling (MH) strategy, management, and operational control. In this paper, we study vehicle general assembly (GA) system with MH, and focus on developing an efficient simulation method for modeling and analysis where traditional simulation methods may suffer from computation intensity. Making use of the partial system decomposability, we introduce an aggregated event-scheduling simulation method with two-level framework. A dividing mechanism with boundary conditions is employed in top-level simulation to divide the global event list into small sizes. A timing-focuses strategy based on max-plus algebra is applied in bottom-level local simulation to further reduce local event lists. With this new method it is possible to mimic real production systems fast and accurately within a reasonable computational time frame. The effectiveness and efficiency of the new simulation method are validated through experimental results.

Up to now, it still remains a big challenge for us to build a high performance geo-computing system with high processing speed and also be easy of use by domain researchers. The unprecedented scale data and various complex algorithms pose many computational and management challenges. To properly settle these main issues above, a new system framework for high performance geo-computing is presented in this paper. A High Performance Geo-data Object Storage System (HPGOSS) base on parallel file System is used for eliminating I/O performance bottleneck and deal with the data managing problem result from the close relevancy between geo-information and remote sensing image data. Parallel programming models for fast parallelization of geo-computing algorithms are proposed. In addition, the job scheduling strategy and workflow engine are also discussed. Finally, such system could provide a parallel geo-computing environment with high performance, easy to use, optimal resource utilization, and high scalability.

We consider modeling of manufacturing enterprises as networks which process, store, and transport materials between enterprise nodes in order to feed customer demand. Each node within the network is represented as a dynamic model with associated costs of production and inventory. Examples of disruptions for such an enterprise could be weather events, material shortages, equipment disasters, or labor events. Using the dynamic model, we consider major disruptions within this network. We present aggregation methods which can support the analysis to evaluate the impact of these disruptions, and develop control strategies that reduce the impact of the disruption.

Many production systems have split and merge operations to increase production capacity and variety, improve product quality, and implement product control and scheduling policies. In this paper, we present analytical methods to model and analyze Bernoulli production systems with circulate and priority split/merge policies. The recursive procedures for performance analysis are derived, the convergence of the procedures and uniqueness of the solutions, along with the structural properties, are proved analytically, and the accuracy of the estimation is justified numerically with high precision.

Most of the studies in flexible manufacturing systems address the issues of flexibility, productivity, cost, etc. The impact of flexible lines on product quality is less studied. This paper presents a quantitative model based on Markov chain analysis to evaluate quality performance of a flexible machining system. A case study of flexible fixture is provided to illustrate the applicability of the method.

Manufacturing system design has an impact on product quality. In this paper, we investigate this impact through an application study at an automotive paint shop. Specifically, for repair and rework systems in paint operations, we develop a model to quantify paint quality [in terms of quality buy rate (QBR)] as a function of repair capacity. We show that the QBR can be improved and unnecessary repaints can be reduced by increasing the repair capacity. Note to Practitioners-Manufacturing system design and quality management are important in many manufacturing industries. Although they have attracted substantial research effort, little attention has been paid to address the coupling or interactions between system design and product quality. Empirical evidence and analytical studies have shown that manufacturing system design does impact quality. In this paper, through an application study at a repair and rework system in an automotive paint shop, we show that paint quality, as measured by the quality buy rate, can be improved by designing the system more effectively. Similar problems are also often encountered in other manufacturing systems. Results obtained in this work, along with other results, demonstrate both the theoretical and practical importance of the analysis of manufacturing system design on product quality, and suggest a largely unexplored, but promising research area

In this paper, we study a manufacturing system with repair and rework. We show that in such systems the product quality, in terms of quality buy rate (the good job ratio of all jobs), can be described by a function of repair capacity and first time quality (the good job ratio of all first time processed jobs). Variations in first time quality can lead to reduction of quality buy rate and substantial waste of production capacity and materials. Therefore, the repair and rework system should be designed to be robust to the fluctuations. Moreover, we observe that the quality buy rate is practically independent of the distribution of the first time quality, but depends primarily on its coefficient of variations

In this paper, analytical models to study the performance of Andon type transfer production lines are presented. In addition to provide the calculation formulas to evaluate the good job production rates, we show that when repair time is short, introducing Andon to stop the line and solve the problems (or defects) is a better way to achieve high throughput of good quality jobs.

A system-theoretic method, referred to as overlapping decomposition, is presented to evaluate the performance of a complex manufacturing system with assembly, parallel, rework, feedforward, and scrap operations. The idea of the method is to decompose the complex system into a set of serial production lines, with the first or last machines of each serial line overlapped with another line, and to modify the parameters of overlapping machines to accommodate the effects of machines and buffers in other lines. Iterative procedures are introduced to estimate the system production rate. The convergence of the procedures and the uniqueness of the solutions are proved analytically and the accuracy of the estimates is evaluated numerically. Note to practitioners - Many large volume manufacturing systems consist of complex operations, for instance, assembly, disassembly, rework loop, parallel lines, feedforward lines, scrap, etc. Development of a performance evaluation method to provide fast and accurate analysis of system throughput is important for design and continuous improvements. This paper introduces a system-theoretic method, referred to as, overlapping decomposition, to analyze the performance of such complex manufacturing systems. The complex system is decomposed into overlapped serial production lines and modifications are introduced to accommodate the coupling effects among all these lines. From the theoretical point of view, this paper presents the proofs of the convergence of recursive procedures and the uniqueness of solution. From the application point of view, the method has obtained good results in solving practical problems on the factory floor.

This paper presents an iterative approach to model and analyze one type of flexible manufacturing systems with common lines and dedicated branches to process different products. The mathematical procedures, associated with the justification of convergence and accuracy, are provided. A case study is presented to illustrate the applicability of the method.

In this paper, an overlapping decomposition method is used to estimate the throughput of a production system with multiple rework loops. The idea of the method is to decompose the system into a couple of serial lines and modify the parameters of overlapping machines to accommodate the effects of other lines. Using this method, the throughput of an automotive paint shop is analyzed and continuous improvement procedures are described.Note to Practitioners-Painting is an important element of vehicle production. A paint shop has been a system bottleneck in many automotive assembly plants due to its complexity. Fast and accurate analysis of its system throughput is important for design and continuous improvements. This paper introduces an iterative method to analyze the performance of paint shop type production systems, i.e., systems with multiple rework loops. The method has obtained good results in both theoretical study and applications on the factory floor. In addition, a case study at an automotive paint shop is introduced and continuous improvements process to identify and eliminate system bottlenecks is described. The presented method can also be applied to other production systems with similar structures.

The problem of customer demand satisfaction in production systems with unreliable machines and finite finished goods buffers (FGB) is addressed. The measure of customer demand satisfaction is characterized by the probability to ship to the customer a required number of parts during a fixed time interval. This measure, referred to as the due-time performance (DTP), is often used to characterize the quality of a supplier in the automotive industry supply chain. In the paper, a method for evaluating DTP in serial and assembly lines is developed and the problem of selecting capacity of the FGB is discussed. The results obtained are illustrated by a case study at an automotive component plant.

The problem of satisfying customers demand in pull serial production lines is addressed. The level of demand satisfaction is quantified by the due-time performance (DTP), which is defined as the probability to ship to the customer a required number of parts during a fixed time interval. The definitions of DTP bottlenecks are introduced and a method for their identification is developed.