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.

Household waste collectors (HWCs) are exposed to hazardous conditions. This study investigates the patterns of workplace injuries and work-related illnesses of HWCs. 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. 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. These findings will be instructive in devising policies and guidelines for preventing workplace injuries and work-related illnesses of HWCs.

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). 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). 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. 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.

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.

In this study, we present a framework for optimizing pick-up and delivery operations in bike-sharing systems (BSSs), with particular emphasis on inventory rebalancing and vehicle routing to enhance operational efficiency. By employing a hybrid genetic algorithm (HGA), this study integrates sophisticated predictive models with multi-objective optimization techniques to strike a balance between operational efficiency and demand fulfillment in urban bike-share networks. For probabilistic demand forecasting, the DeepAR model is applied to a large number of bike stations clustered by geological proximity to enable stochastic inventory management. Our proposed HGA approach leverages both the genetic algorithm for generating feasible vehicle routes and mixed-integer programming for bike rebalancing to minimize travel distances while maintaining balanced inventory levels across all clustered stations. Through rigorous empirical evaluations, we demonstrate the effectiveness of our proposed methodology in improving service quality, thus making significant contributions to sustainable urban mobility. This study not only pushes the boundaries of theoretical knowledge in BSS logistics optimization but also offers managerial insights for practical implementation, particularly in densely populated urban settings.

기업 교육훈련은 조직구성원 및 조직의 성과 향상을 위해서 시행되나 실제 교육훈련의 성과는 기대한 만큼 나타나 고 있지 않다. 이에 따라 본 연구에서는 기업 내 교육성과를 피교육자의 조직몰입도와 업무성과로 정의하고 이에 영향을 미치는 요인들에 대해 연구함으로써 성과 창출을 위해 기업 교육훈련 설계 시 고려해야 하는 것들을 제안한다. 우선 기업 교육훈련에서 제공되는 교육콘텐츠 특성으로서의 과제가치와 직무연관성, 그리고 기업 내 교육훈련에 대한 지원 정도인 조직특성이 피교육자의 교육전이에 영향을 끼치는 지 확인한다. 이후, 교육전이로부터 조직몰입 및 업무성과로의 인과관계 를 분석한다. 이 과정에서 온/오프라인 교육의 조절효과가 있는지 분석하여 교육 전달 방법에 따른 효과성의 차이가 있는지 검증한다. 연구결과, 교육의 과제가치, 직무연관성, 조직보상이 교육전이에, 교육전이가 조직몰입 및 업무성과에 유의한 영 향을 미치는 것으로 나타났으며, 온/오프라인 교육간 차이가 있음을 입증하였다. 본 연구는 프랜차이즈 기업을 대상으로 수행하여 표본의 한계가 존재하며, 향후 대표성을 지닌 표본이나 전문직종의 집단을 대상으로 연구를 확장할 예정이다.