Manufacturing and Construction Management

An actual demand-forecasting problem of the US apparel dealers is studied. Demand is highly fluctuating during the peak sale season and low prior to the peak season. The model is described by the continuous time stochastic process applying the Bayesian process. The standard gamma distribution is selected for the demand process and an inverse gamma distribution is chosen as the conjugate prior for the model. The choice is supported by the maximum likelihood estimate among a number of non-negative distribution models. The proposed Bayesian models predict the probability of the future demand expressed explicitly conditional on the observed demand prior to the peak season. The data set illustrates partial demand of a seasonal product procured by the US dealers from overseas. In recent years, hazard and operational risks due to weather disasters and equipment shutdowns were felt significantly. These caused supply chain disruption and unrecorded demand. The model is extended to contribute to forecast from an unrecorded data set due to supply disruption. Forecasts are compared with real data and a widely implemented adaptive Holt-Winters (H-W) seasonal forecasting model. Results show that the forecasts calculated by the proposed methods do better than those of the adaptive H-W model.

Advancements in artificial intelligence (AI) are enabling the integration of explainable AI (XAI) models in Magnetic Resonance Imaging (MRI) to improve diagnostic accuracy and personalize prostate cancer treatment. This paper explores the effectiveness of two XAI techniques, Local Interpretable Model-agnostic Explanations (LIME) and shape analysis, in interpreting prostate cancer MRI datasets. A central focus is the necessary validation to ensure these models provide reliable and actionable diagnostic insights. However, the persistent "black-box problem," which refers to the lack of transparency and interpretability in AI decision-making, has led to clinical skepticism regarding deep learning applications. Since most AI models produce results without clearly explaining their internal processes, radiologists may need help understanding the rationale behind predictions, limiting AI's role as a decisionsupport tool. In this study, we stress the need for validation to ensure that XAI-generated explanations are reliable and clinically useful. Their findings are intended to help integrate XAI into clinical practice and aid in transforming AI from an opaquer to a more transparent and crucial aid to radiologists in diagnosing and managing prostate cancer.

Multimodal freight transportation emerges as the go-to strategy for costeffectively and sustainably moving goods over long distances. In a multimodal freight system, where a single contract includes various transportation methods, businesses aiming for economic success must make well-informed decisions about which modes of transport to use. These decisions prioritize secure deliveries, competitive cost advantages, and the minimization of environmental footprints associated with transportation-related pollution. Within the dynamic landscape of logistics innovation, various multicriteria decision-making (MCDM) approaches empower businesses to evaluate freight transport options thoroughly. In this study, we utilize a case study to demonstrate the application of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) algorithm for MCDM decision-making in freight mode selection. We further enhance the TOPSIS framework by integrating the entropy weight coefficient method. This enhancement aids in assigning precise weights to each criterion involved in mode selection, leading to a more reliable decision-making process. The proposed model provides costeffective and timely deliveries, minimizing environmental footprint and meeting consumers' needs. Our findings reveal that freight carbon footprint is the primary concern, followed by freight cost, time sensitivity, and service reliability. The study identifies the combination of Rail/Truck as the ideal mode of transport and containers in flat cars (COFC) as the next best option for the selected case. The proposed algorithm, incorporating the enhanced TOPSIS framework, benefits companies navigating the complexities of multimodal transport. It empowers making more strategic and informed transportation decisions. This demonstration will be increasingly valuable as companies navigate the ever-growing trade within the global supply chains.

Freight transportation in urban areas has increased significantly in a shorter period due to the widespread use of e-commerce, fast delivery, and population growth. Recently, a noticeable government initiative aimed at creating an effective, acceptable, and sustainable city logistics policy. This paper examines freight consolidation as a transportation strategy for optimizing last-mile delivery costs. Freight consolidation involves combining smaller shipments from various origins into a single, larger shipment for more efficient transportation to a common destination. This approach is particularly beneficial for last-mile delivery, where frequent deliveries of smaller quantities are frequently visible. Finally, we provide an illustrative example targeting urban freight stakeholders for practicing possible consolidation methodology. The result in the illustrative example shows that freight with 3-day consolidation, despite the delay penalty, is cheaper than daily shipping, and both are cheaper than 2-day consolidated shipping. The study will benefit urban businesses and freight services.

A primary motivation for using Open Educational Resources (OERs) is to increase access by reducing students' financial burden. While this was a motivating factor in this study, the authors also were interested in understanding OER's impact on student learning. Therefore, this study describes the adaption process for an OER textbook used in a junior-level undergraduate electricity course and evaluates this OER's effectiveness to increase student access and improve learning over the baseline textbook. A quasi-experimental, non-equivalent design collected exam scores, and self-reported survey responses from n=144 students in a comparison (non-adapted OER; n=83) and treatment (adapted OER; n=61) group was used. Based on Student's t, Kolmogorov Smirnov, and Chi-square (χ2) tests, the adapted OER was effective at improving access and learning while also saving enrolled students an average of $8,000 per year, collectively. A brief background of OER databases, materials, and methods used to develop the OER and the intellectual contribution of the newly adapted OER, and lessons learned are included in the study.

Identifying human factors and job characteristics affecting performance is crucial for a sustainable management of the workforce in a labor-intensive industry, such as the construction industry. At the individual worker level, physiological status and environmental stressors are reported to influence workforce performance. Previous exploratory research reported a U-shaped curve between physical strain and productivity at the group level. This paper revisits those exploratory findings to validate the U-curve relationship between physical strain and productivity at the group level. It also evaluates the relationship at the individual level while controlling for personal demographic information and heat stress. This study builds upon the previous study by expanding the original dataset and conducting an identical experimental protocol. The subjects wore sensors to monitor their heart rate, which indirectly measured their physical strain. Cameras were used to record experimental activities. The correlations between physical strain and productivity were analyzed at both the individual and group levels. We divided each subject's four-hour experimental data into 5-minute intervals, assigned time variables, and generated panel data sets for each subject. Then, the time effect on the subjects was observed to reflect the effect of time on physical strain and productivity. To conduct the panel data regression analysis, individual physical differences such as age, gender and body mass index were defined as control variables; physical strain and time effect were set as independent variables; and productivity was defined as a dependent variable. Random-effect models of panel data analysis were used to investigate the relationship. Based on the above statistical analysis, we observed the U-curve relationship between physical strain and task-level productivity both in the group and individual level as the construction laborers' job characteristics. This research finding demonstrates how physical strain in a laborer involved in construction work is correlated to task productivity.

Weather data help inform the sustainable management of construction workforce. Weather data can be used to predict the level of heat stress faced by workers and inform job rotation. Oftentimes, researchers struggle to obtain authorization from project owners or contractors when it comes to installing on-site weather station for collecting desirable data. Thus, researchers look to alternative data sources. This paper studies two alternative data sources: the weather stations validated by the world meteorological organization (WMO) and those provided by Weather Underground (WU), an online platform from The Weather Company, LLC. Specifically, we selected a project site in Seattle and estimated weather data for the site location on a geographical information system using inverse distance weighted interpolation. Weather data were collected from 35 weather stations within a 100 mile (160 km) radius from the project site through WU. We seek to understand: 1) through the spatial interpolation and for the locations which house the WMO stations, are the weather data derived from localized personal weather stations comparable to those measured by the WMO stations? 2) if they are comparable, for the location which houses the selected project site, are the weather data derived from localized personal weather stations areas comparable to those measured by the WMO stations?, and 3) if they are not comparable, how big is the difference when it is translated into the scale of heat stress? Eventually, our study hopes to develop a robust method to monitor construction workers' level of heat stress at specific project locations through localized weather data.

Both physiological status and jobsite environmental stressors influence workforce behavior and performance. Understanding these relationships at the individual worker level is paramount for sustainably managing the construction industry workforce. Astonishing improvements in sensing technology can benefit field research by providing ways to validate occupational performance models based on data that measure workers' physiological variables and environmental stressors. However, only a few studies have taken advantage of these technological improvements to conduct construction field studies. This paper describes a field monitoring study hosted at a mid-rise, mixed-use building construction site in Seattle, WA. This study was valuable in term of its breadth and period of the observations because it used some of the latest off-the-shelf wearable biosensors to collect 339 hours of workers' biosignal data from five subjects, during summer and fall, for a total of up to three weeks per subject. This research empirically validated that the heart rate is a good predictor of a worker's physical strain. Descriptive statistics and a time series plot were used to analyze the heart rate pattern as a predictor of worker's physical strain level. Correlation analysis was used to analyze the association between the workers' heart rate and jobsite environmental stressors. Also, analyzing video recordings and questionnaires helped interpreting the analytical results. This paper reports the lessons learned and the challenges of implementing a selected combination of wearable biosensor and environmental sensing technologies. These research findings are preparatory to validating a demand and capability model to be used for predicting construction workers' performance.

Among the various construction safety and health (S&H) training methods, 3D digital game-based learning is believed to have a great potential. This method can combine various educational strategies to engage learners that have grown up with computer gaming. To this end, an increasing number of 3D serious games have been developed for the learning of construction S&H. However, developing a virtual construction-site simulation for serious gaming is a tedious process. Little has been done to understand the simulation parameters that should be controlled in order to adequately represent the dynamics of a construction site and, as a result, to influence student learning. Therefore, the purpose of this study is to understand and prioritize the 3D construction-site simulation parameters for learning hazard recognition. In the paper, the researchers reported their findings on the S&H game design framework as an initial step to identify how simulation parameters such as texture, lighting, and animation could align with game contents and positively affect student learning. A future experiment for validating each parameter was also introduced. The researchers expect that the final research outcomes will enable construction education stakeholders to better understand and manage the design complexity of 3D construction-site modeling for S&H serious games.

The construction environment affects workers' physical responses as well as individual and crew-level performance. Previous research has analyzed the relationship between worker performance and severe environments. This paper reports initial results and a process for establishing reliable, physiologically acceptable bounds and heart rate (HR) zones for construction workers. Five workers at the same building jobsite accepted to wear a Physiological Status Monitoring (PSM) system for several days while at work. HR data were collected to identify physiologically acceptable bounds and HR zones as practical threshold-based guidelines in construction. Since HR is affected by various external variables, daily video recordings and weather data were also collected and used to explain results.

Most injuries among construction workers are work-related musculoskeletal disorders (WMSDs) caused by repeated, intensive lifting and lowering of construction materials, which increase muscular tension, particularly in the lower back. Accelerometer devices can help prevent such WMSDs by providing data on non-neutral postures that can be used to provide worker feedback. The purpose of this paper is to evaluate the Zephyr BioHarness TM 3 device worn under the armpit as a tool to analyze postures of construction workers, especially when lifting and lowering materials. This wearable sensor can be used in construction worker studies, due to its location on the body, which interferes little with safety harnesses and tool belts. The device has been mostly used to monitor physiological signals such as heart rate, but also has an embedded triaxial accelerometer that can be used to monitor posture. This study evaluates using Zephyr accelerometer data for posture analysis for construction lift/lowering activities compared with a more common accelerometer device used in research, the ActiGraph GT9X. For this, the current study compared the two devices ability to assess upper body thoracic bending posture while lifting and lowering masonry materials in a laboratory setting. A participant bent body 45 and 90 degrees at slow, medium, and fast velocities guided by a metronome, and placed concrete tiles for 10 minutes. The protocol was repeated 10 times. The percentage of time bending in excess of set threshold angles, the 10th, 50th, and 90th percentile bending angles, and average angles, were compared for the two devices. With a slow frontier-anterior bending of 0.5 Hz, the chest-worn Zephyr showed agreement with the results from the ActiGraph on the chest, with the smallest bias observed (mean difference = 2.18°). However, the percentage error was 30.9%. These findings indicate that the Zephyr should be worn on at the chest location in studies of thoracic bending posture for construction tasks with low-angular velocity. For the dynamic posture analysis, both the chest-and under-non-dominant-armpit-worn Zephyr accelerometers reported higher means; 10 th , 50 th and 90 th percentile bending angles; and percentage of times over set threshold angles. In the static posture analysis, the Zephyr overestimated non-neutral trunk flexion and lateral bending compared to the ActiGraph. Additionally, while the placement of the ActiGraph at the chest location produced good posture measurements, placements on head, shoulder and center of waist locations are not recommended for working posture assessment due to a high bias on observed statistics in the dynamic posture analysis.

During vacuuming tasks, janitors report discomfort in the shoulders. In this study, we investigated the biomechanical impacts of two factors, backpack vacuum position and carpet vacuuming technique, on the shoulder. The scenarios consisted of three backpack locations on the back (L3, T12, and T7 regions) and two vacuuming methods: front-to-back (FB) and side-to-side (SS) presented in a random order. Twenty-two professional and novice janitors were recruited to perform the task. To determine the physical workload on the shoulders, muscle activity on the left and right trapezius and infraspinatus was monitored using surface electromyography. The results showed that the peak dominant side trapezius muscle loading with the SS technique was significantly higher (p<0.05) than with the FB technique. However, the peak dominant side infraspinatus muscle loading with the SS technique was significantly lower (p<0.05) than with the FB technique. The backpack machine location did not have a statistically significant effect on the trapezius and infraspinatus muscles. Professional janitors were familiar with the FB technique in their practice. Thus, in the SS technique scenarios, we found that they frequently exerted the right trapezius muscle as in FB scenarios rather than twisting their torso as intended by the SS technique.