Xiuli Qu

An analytical model for reverse automotive production planning and pricing

Automotive shredders need a reverse production planning strategy that includes determining at what price to purchase vehicle hulks from different sources. In this paper, we formulate the automotive reverse production planning and pricing problem in a nonlinear programming model, develop an approximate supply function for hulks when adjacent shredders price independently, and compare two hulk pricing strategies in three trends for ferrous metal and hulk prices: constant, increasing and decreasing. The case study results indicate that adjusting purchase price based on hulk composition in coordination with planning for purchasing, storing and processing can increase net revenue by 7-15%.

A model for quantifying the value of RFID-enabled equipment tracking in hospitals

US hospitals spend millions of dollars on lost, misplaced and stolen equipment every year. Radio frequency identification (RFID) technology provides potential solutions to this problem. The nations top healthcare providers installing RFID have demonstrated the benefits of RFID. However, most other providers have not followed suit due to the lack of models available to provide measurable steps in successful RFID installation and sustainability, and to predict legitimate returns on investment. To respond this need, we propose a Markov chain model that could quantify the benefits of RFID from reducing equipment shrinkage and staff time of searching for equipment and increasing equipment utilization in hospitals. Using the proposed Markov chain model, a sensitivity analysis is conducted to investigate the performance improvement by RFID-enabled equipment tracking in a hospital. Our results demonstrate that an RFID-enabled equipment tracking system could significantly increase equipment utilization. In addition, the proposed model may be used to evaluate the equipment preparation and maintenance policies in hospitals with RFID, and could be easily extended to quantifying the benefits of RFID tracking systems in other industries.

Rescheduling of elective patients upon the arrival of emergency patients

In this study, a mixed integer linear programming (MILP) model is developed for rescheduling elective patients upon the arrival of emergency patients by considering two types of clinical units, namely operating rooms and post-anesthesia care units (PACUs). The model considers the overtime cost of the operating rooms and/or the PACUs, the cost of postponing or preponing elective surgeries, and the cost of turning down the emergency patients. The results indicate that a mainstream commercial solver can efficiently find an optimal solution in a particular scenario with light elective surgery load, but becomes very inefficient in searching optimal solutions in all other scenarios. As such, a genetic algorithm is developed to efficiently obtain the approximately optimal solutions in those scenarios that are difficult for the commercial solver. In the genetic algorithm, a novel chromosome structure is proposed and applied to represent the feasible solutions to the MILP model. It is shown that for the scenarios with heavy load of elective surgeries, the genetic algorithm can find approximate optimal solutions significantly faster than the commercial solver. In practice, the two solution methodologies should be used jointly to provide hospitals a solid tool for making sound and timely decisions in admitting emergency patients and rescheduling elective patients.

A two-phase approach to scheduling multi-category outpatient appointments – A case study of a women’s clinic

In this paper, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. In many outpatient clinics, various service types are categorized to address the operational challenge of substantial changeover time between certain pairs of services. In the first phase of our approach, a mixed-integer program is formulated to assign service categories to clinic sessions during a week and determine the optimal number of appointments reserved for each service type in each clinic session. The objective in the first phase is to balance the workload of the providers among clinic sessions. In the second phase, a stochastic mixed-integer program is formulated for each clinic session to assign each contained appointment with a starting time based on several time-based performance measures. To solve the formulated stochastic program, we develop a Monte Carlo sampling based genetic algorithm. The two-phase approach is tested numerically with cases derived from a real women’s clinic. Our results demonstrate that the two-phase approach can efficiently find promising weekly appointment scheduling templates for outpatient clinics. In addition, our results suggest that the best suboptimal scheduling templates found become more sensitive to the weighting coefficients of the time-based measures as the provider workload increases.

A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients☆

Abstract With the pressing demand for improving patient accessibility, the traditional scheduling system may not be effective for mitigating the adverse effects caused by no-shows, appointment cancellations and late arrivals. For this reason, open access scheduling, which specifies that a portion of clinic appointment slots be reserved for short-notice appointments, was proposed and adopted in recent years. In literature, many studies have developed a variety of approaches and models to optimize the open access scheduling systems, while few considers the inclusion of walk-in patients and the optimal allocation of reserved slots on the scheduling template under the open access configuration. In this paper, we propose a Discrete Event Simulation and Genetic Algorithm (DES–GA) approach to find the heuristic optimal scheduling template under the clinic setting that allows both open access and walk-in patients. The solution can provide scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient. In this approach, the cost is measured by the weighted summation of patient waiting time, provider idle time, and provider overtime. A case study and sensitivity analysis are conducted to show how the heuristic optimal scheduling template generated from the proposed approach could vary under different scenarios. This also illustrates the viability of our model. The results show that the heuristic optimal scheduling templates are significantly affected by the patient attendance rate, level of demands of same-day appointment and walk-in admissions, as well as the cost coefficients associated with patient waiting time, provider idle time and provider overtime.

A mean-variance model to optimize the fixed versus open appointment percentages in open access scheduling systems

Although healthcare quality may improve with short-notice scheduling and subsequently higher patient show-up rates, the variability in patient flow may negatively impact the service design. This study demonstrates how to select the percentage for short-notice or open appointments in an open access scheduling system subject to two quality performance metrics. Specifically, we develop a mean-variance model and an efficient solution procedure to help clinic administrators determine the open appointment percentage subject to increasing the average number of patients seen while also reducing the variability. Our numerical results indicate that for cases with high patient demand and high patient no-show rates for fixed appointments, one or more Pareto optimal percentages of open appointments significantly decrease the variability in the number of patients seen with only a negligible decrease in the expected number of patients seen. While our method provides a useful tool for clinic administrators, it also presents a modeling foundation for open access scheduling with quality management objectives to smooth patient flow and improve capacity utilization.

Evaluating the Effectiveness of Pre-Positioning Policies in Response to Natural Disasters

Recent natural disasters highlight the complexities associated with planning, coordination and distribution of supplies in a manner which provides timely and effective response. In this paper, the authors present a model to quantify the benefits associated with pre-positioning local supplies. They assume the supplies are in a high-risk location and may be destroyed if an appropriate strategy to protect the supplies is not implemented. A stochastic linear programming model is developed where the first-stage decision pre-positions existing supplies to minimize the supply loss. Second-stage decisions attempt to maximize the responsiveness of the system by allocating supplies to satisfy demand. The benefits associated with pre-positioning versus non-pre-positioning are discussed.

A two-stage stochastic programming model for phlebotomist scheduling in hospital laboratories

This paper introduces a two-stage stochastic integer linear programming model to improve phlebotomist scheduling in laboratory facilities of healthcare delivery systems. The model developed enables laboratory management to determine optimal scheduling policies that minimize work overload. The stochastic programming model considers the uncertainty associated with the blood collection demand in laboratory environments when optimizing phlebotomist scheduling. The paper presents an application of the model to a hospital laboratory in urban North Carolina as a case study discussing the implications for hospital laboratory management.

Simulation-Based Evaluation of Port Emergency Evacuation Plans for Predictable Natural Disasters

Abstract Effective port emergency evacuation could reduce the potential damages and losses caused by a predictive natural disaster. Thus, most U.S. ports have evacuation plans or guidelines established for predictive natural disasters, especially hurricanes. However, hurricane Katrina still revealed that these existing emergency evacuation plans may not be as effective as originally expected. The objective of this study is to provide a quantitative approach to evaluating the effectiveness of port emergency evacuation plans for hurricanes. A discrete-event simulation model is developed in this study to capture the dynamic evacuation process in a complex port environment when a hurricane approaches a port. Using this simulation model, an experimental study is conducted to compare the effectiveness of 80 port hurricane evacuation plans in the two public container terminals within the Port of Houston (POH). These 80 plans include the current POH hurricane evacuation plan and 79 variants of this plan. The simulation results indicate that an effective port hurricane evacuation plan should require prohibiting inbound and outbound traffic when the hurricane is forecast to be 12 hours away from landfall, and consistently securing port facilities, equipment and containers during the entire evacuation process.