Xiaolei Xie

Modeling and Analysis of Ward Patient Rescue Process on the Hospital Floor

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.

Simulation-based optimisation approach for the stochastic two-echelon logistics problem

This work proposes a simulation-based optimisation approach for the two-echelon vehicle routing problem with stochastic demands (2E-VRPSD). In the proposed 2E-VRPSD, freight delivery from the depot to the customers is managed by shipping the freight through intermediate satellites, while each customer has a stochastic demand. The 2E-VRPSD is an extension of the famous capacitated vehicle routing problem with stochastic demands and the two-echelon vehicle routing problem (2E-VRP). A tabu search algorithm is designed to solve the 2E-VRPSD, in which Monte Carlo sampling is adopted to tackle the issue of stochastic demands. Modified two-echelon vehicle routing problem benchmark instances are used in the numerical experiments. The computational results show the advantage of the proposed simulation-based approach.

Reducing energy consumption in serial production lines with Bernoulli reliability machines

Abstract This paper is devoted to developing an integrated model to minimise energy consumption while maintaining desired productivity in Bernoulli serial lines with unreliable machines and finite buffers. For small systems, such as three- and four-machine lines with small buffers, exact analysis to optimally allocate production capacity is introduced. For medium size systems (e.g. three- and four-machine lines with larger buffers, or five-machine lines with small buffers), an aggregation procedure to evaluate line production rate is introduced. Using it, optimal allocation of machine efficiency is searched to minimise energy consumption. Insights and allocation principles are obtained through the analyses. Finally, for larger systems, a fast and accurate heuristic algorithm is presented and validated through extensive numerical experiments to obtain optimal allocation of production capacity to minimise energy consumption while maintaining desired productivity.

Improving energy efficiency in Bernoulli serial lines: an integrated model

In this paper, we present an integrated model of both productivity and energy consumptions in serial production lines with two machines. Bernoulli reliability is assumed for both machines and the capacity of the buffer is finite. The energy consumption of each machine includes the energy required to set up the machine until it is ready for processing, and the additional energy needed to carry out the processing operation to make the product. The former is typically fixed for a specific manufacturing process, while the latter is proportional to the processing rate. The objective of the model is to minimise energy consumption, while maintaining the desired production rate. Specifically, analytical investigation has been carried out to discover the conditions that energy consumption can be minimised with and without the constraints of workforce or machine processing capability. Optimal allocations of them under different scenarios have been derived. Insights for reducing energy consumption while still ensuring desired productivity have been obtained.

A survey on simulation optimization for the manufacturing system operation

Abstract Stochastic effects exist in a great number of manufacturing system operation problems. Simulation optimization methods are widely used for tackling the stochasticity. In order to provide a comprehensive coverage of simulation optimization publications with a focus on applications in manufacturing system operation logically, we classify the literature into two general categories of local optimization and global optimization. The local optimization literature is further divided into two subclasses based on the parameter spaces (discrete or continuous parameters). In each class, we explain how the corresponding methods integrated with simulation solve major manufacturing system operation problems, such as long- and short-term production planning, flow shop scheduling, and job shop scheduling. Finally, the current research status on simulation optimization for manufacturing operations is summarized. Meanwhile, some key issues, such as lack of unified problem benchmarks for comparison and low computational efficiency for real-scale problems, which need future research in this field, are discussed as well.

An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and synchronized visits

Abstract This paper addresses a special vehicle routing problem, which extends the classical problem by considering the time window and synchronized-services constraints. A time window is associated with each client service and some services require simultaneous visits from different vehicles to be accomplished. This problem has many practical applications such as caregiver scheduling problem encountered in the home health care industry. The synchronization constraints in this problem interconnect various vehicles’ routes, making the problem more challenging than standard vehicle routing problem with time windows, especially in designing neighborhood search-based methods. A mixed-integer programming model is proposed for the problem. Motivated by the challenge of computational time, an efficient Adaptive Large Neighborhood Search heuristic is proposed to solve the problem. The approach is evaluated on benchmark instances acquired from the literature and new large-scale test instances first generated in this paper. The numerical results indicate that our solution method is able to outperform existing approaches.

Modeling and Analysis of the Waiting Time of Rapid Response Process in Acute Care

To ensure patient safety, many hospitals have implemented rapid response process to provide care to acute patients who experience acute physiological deterioration. However, those patients sometimes have to wait for a long time before an appropriate decision can be made, due to low availability of care providers. In this letter, we develop an analytical model with the objective of reducing patient waiting time. A method to evaluate patient waiting time during deterioration process is presented. Structural properties are discussed. Continuous improvement issues are investigated by introducing an indicator to identify the bottleneck care provider whose response time reduction could lead to the largest decrease in patient waiting time. A simple approximation formula of bottleneck indicator is derived. Finally, a set of numerical experiments are performed to validate the model.

Improving Colorectal Polyp Classification Based on Physical Examination Data—An Ensemble Learning Approach

Colorectal cancer is a common type of cancer. Due to the alarming incidence and mortality rate, it has received increasing attention on early detection and treatment. Colorectal polyps form and grow at initial stages of most colorectal cancer cases. Due to rather stringent medical resource availability and low screening compliance rate, it is more desirable in China than industrialized countries to characterize the relations between colorectal polyp occurrence and various potential determinants, including basic health information, comorbidities, and lifestyle conditions. Subsequently, one can better predict polyp incidence for each individual. In this letter, we report a data-driven modeling study to improve binary classification of colorectal polyp occurrence. We apply several machine-learning methods, particularly random forests, for physical examination and screening colonoscopy results of a Chinese cohort, to build the classifiers. Our results suggest improved prediction performance with the random forests model. Our study also provides evidence to support the general speculation that emotional status may be an influential risk factor to early colorectal cancer growth in China.

An energy and productivity optimization model in Bernoulli serial lines

In this paper, an integrated model is presented to minimize energy consumption while maintaining desired productivity in serial lines with Bernoulli reliability machines. Exact analysis of optimal allocations of production capacity is carried out for three- and four-machine lines with small buffers. For lines with larger buffers, an aggregation procedure is introduced to evaluate line production rate, and then used to search optimal allocation of machine efficiency to minimize energy usage. Insights and allocation principles are obtained through the analyses.

Analysis of multi-patient rapid response processes: An iterative approach

In acute care, providers need to response quickly to patients deterioration. However, a physicians availability can be limited if multiple patients are declining simultaneously. To study the multi-patient rapid response process, a complex network model with split, merge and parallel structures is introduced, and iteration procedures are presented to evaluate system performance. It is shown that such procedures are convergent and lead to accurate performance evaluation.