Appointment scheduling strategies in primary care clinics and surgical operating units

The principal of scheduling in healthcare delivery systems is to control conflicting preferences and priorities of its stakeholders under the restrictions of limited clinical resources. In most instances of that domain, the number of stakeholders is large and there are a lot of human-associated uncertainties, and thus the scheduling problems are very challenging. However, if we consider this era\u27s escalating healthcare costs and aging population, tackling those problems needs prompt attention. Out of various fields in the healthcare delivery systems, this dissertation has mainly focused on appointment scheduling strategies in primary care clinics and surgical operating units. First, we conducted a simulation study to propose operational guidelines for open access (OA) scheduling strategies in primary care clinics. OA, sometimes so called advanced scheduling, leaves majority of the slots open to same day appointments (SDA) and thus it is expected to reduce patients\u27 waiting times that may result in patient no-shows and cancellations. This research provides the rules of thumb for appropriate proportion of SDA slots to daily capacity, which takes into account the patients waiting times, successful appointment rates, and resource utilization. Overbooking strategies in primary care clinics also have been noted by many researchers due to its ability of working as buffers to uncertain events. Both OA and OB function as alternatives to the conventional appointment scheduling. However, their fundamental comparisons in practice have not been studied a lot. The goal of this study is to compare the two scheduling strategies in terms of overtime staffing, patient waiting, missing appointment opportunities, and time slot utilization. A discrete-event simulation was employed to model the primary care clinic settings with different demand levels. Under the uncertainties in surgical operations, efficient scheduling of surgery starting times before their execution is one of the difficult tasks. Moreover, in a setting of multiple operating rooms (OR), there may be the congestion in admission to PACU after surgeries unless enough PACU resource is provided nor efficient scheduling strategy is applied. In this study, we have developed a two-phased approach to determine the surgery starting times for multiple ORs under limited resources of PACU. The first phase formulates this problem as a flexible job shop scheduling with fuzzy sets for modeling uncertain service durations. From the obtained solution represented as fuzzy numbers, a newsvendor-like heuristic was proposed to establish precise time schedules in the second phase. The performance of the approach was shown superiority over a simulation-based optimization technique from a literature. Finally, we investigated suitable proportion of PACU resources to OR with the obtained start-time schedule from our approach

Comparisons of Traffic Collisions between Expressways and Rural Roads in Truck Drivers

Background: Truck driving is known as one of the occupations with the highest accident rate. This study investigates the characteristics of traffic collisions according to road types (expressway and rural road). Methods: Classifying 267 accidents into expressway and rural road, we analyzed them based on driver characteristics (age, working experience, size of employment), time characteristics (day of accident, time, weather), and accident characteristics (accident causes, accident locations, accident types, driving conditions). Results: When we compared the accidents by road conditions, no differences were found between the driver characteristics. However, from the accident characteristics, the injured person distributions were different by the road conditions. In particular, driving while drowsy is shown to be highly related with the accident characteristics. Conclusion: This study can be used as a guideline and a base line to develop a plan of action to prevent traffic accidents. It can also help to prepare formal regulations about a truck driver's vehicle maintenance and driving attitude for a precaution on road accidents

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Workplace Accidents and Work-related Illnesses of Household Waste Collectors

Background: Household waste collectors (HWCs) are exposed to hazardous conditions. This study investigates the patterns of workplace injuries and work-related illnesses of HWCs. Methods: This study uses cases of workplace injuries and work-related illnesses of HWCs that occurred between 2010 and 2011. We analyzed 325 cases of injuries and 36 cases of illnesses according to the workers' age, length of employment, size of workplace, injured part of body, day and month of injury, type of accident, agency of accident, and collection process. Results: There were significant differences in the effect of workers' length of employment, injured part of body, type of accident, agency of accident, and collection process. Results show that most injuries occur in workers in their 50s and older. This study also shows that 51.4% of injuries occur at businesses with 49 employees or fewer. Injuries to waste collectors happen most often when workers are electrocuted after slipping on the ground. The second most prevalent form of injury is falling, which usually happens when workers hang from the rear of the truck during transportation or otherwise slip and fall from the truck. Work-related illnesses amongst waste collectors are mostly musculoskeletal conditions due to damaging postures. Conclusion: These findings will be instructive in devising policies and guidelines for preventing workplace injuries and work-related illnesses of HWCs

Mathematical Investigation on the Sustainability of UAV Logistics

Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system’s restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO2 emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO2 efficient in all ranges of the GV speeds investigated