International Journal of Industrial Engineering: Theory, Applications and Practice
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A METHOD OF CALCULATING WEAPON EFFECTIVENESS INDEX USING MUNITION TRAJECTORY AND VULNERABILITY DATA
No full textThis paper proposes a novel method for calculating the weapon effectiveness index (WEI) by considering various engagement and environmental factors. The calculation is performed by determining the munition trajectory and using the target vulnerability data. Unlike the existing method that considers only the end state of a munition, the proposed method considers the effects of aiming errors, ballistic errors, and environmental conditions when calculating the munition trajectory. The vulnerability data represent the probability of kill (Pk) on the damage assessment (DA) plane corresponding to the impact angle. This paper describes the process of determining the impact point, identifying the appropriate DA plane, calculating the hit position on the DA plane, and obtaining the specific Pk. As a case study, a tank-to-armored vehicle engagement scenario is simulated. The proposed method is verified through a review of the simulation models and the simulation results by modeling and simulation system domain experts and weapon systems development domain experts
A NEW LOCATION SELECTION MODEL OF BUY ONLINE AND PICK UP IN STORE IN OMNICHANNEL LOGISTICS SYSTEMS
No full textThe placement of buy online and pick up in store (BOPS) facilities is an essential consideration to ensure the efficient operations of omnichannel logistics systems. There have been many efforts to improve customer service and the cost of BOPS. This study suggests a structured model to propose an optimal location of BOPS. Firstly, we categorize and characterize the BOPS facilities type. Secondly, we offer evaluation criteria of the BOPS facilities with the fuzzy analytic hierarchy process (Fuzzy-AHP). Thirdly, we define each sub-criteria by equation and then calculate the fitness rankings of locations in a case study using public placement analysis data in South Korea. These results confirmed that the proposed method is a great guideline for real problems with the placement of BOPS facilities. This study proposes a methodology to solve location selection problems of BOPS with available public data, which can be practically applied to omnichannel logistics
A METHOD FOR FOOTBALL SHOT MOTION RECOGNITION BASED ON STEREO VISION
No full textThe core aim of motion-recognition technology is to detect moving targets and extract the target’s movement characteristics, classifying these so as to judge the corresponding action category. Moving target detection and tracking form the core of computer vision research topics in military visual guidance, robot visual navigation, industrial product detection, medical diagnosis, traffic monitoring, and virtual reality, and is also very important in terms of practical value and the broad prospects for development in other fields. However, in human behavior recognition algorithms, the background, light, environment, and other factors make identification accuracy difficult to obtain and unstable, with limited application scenarios. Because traditional target detection and tracking is based on monocular vision algorithm robustness, which is poor, this makes it difficult to detect and track moving targets in complex environments. In recent years, with the maturity of target detection and visual range-finding technology, pure static target visual barium distance or the depth of target detection cannot meet the demand of the market. Therefore, we need the efficient collection of deep information in moving target detection systems. Binocular stereo vision technology, as a hot research field in computer vision, can be widely used in the field of sports to achieve target detection and ranging functions. In view of this, this paper adopted a stereoscopic vision for use in target detection and tracking methods. This paper proposes a stereo-matching method based on hierarchical network minimum segmentation, where the method uses three-dimensional images from two layers of the pyramid decomposition by splitting the global optimal search strategy into a minimum at the top of the image to match with the initial parallax at the bottom of the image through regional gray correlation matching. Both reduce the search space of the stereo matching and improve matching accuracy. At the same time, this paper uses the motion of a football shot to prospect this recognition method, which can identify the target area. The Snake contour model method was also utilized to extract the target contour. This method has the following three characteristics: when ambient light changes slowly and suddenly, it can correctly detect moving targets; when the object is lit with cast shadows, the moving target can be detected correctly and is not affected by the shadows. When rigid target motion and deformation occur, it can correctly extract the target contour
A ROUTING AND SCHEDULING PROBLEM WITH HIERARCHICAL OBJECTIVES UNDER INDUSTRIAL LOGISTICS ENVIRONMENT: ANALYSIS, MODELING, AND TWO-PHASE HEURISTIC
No full textThis study investigates a routing and scheduling problem in industrial logistics that considers resource synchronization on heterogeneous facilities and maximum working duration. The problem calls for constructing routes and arranging the loading operations of dispatched vehicles on heterogeneous loading facilities while minimizing the total travel time first and balancing the workload second. We establish a mixed integer programming model and propose a two-phase heuristic. The first phase is to minimize total travel time by using a hybrid metaheuristic based on adaptive large neighborhood and variable neighborhood descent. The second phase is to balance the workload between different facilities with a post-optimization procedure. Extensive computational experiments demonstrate the effectiveness of our approach. In addition, we found that increasing the loading speed of facilities and extending the maximum working duration has a significant marginal decreasing effect in reducing total travel time, which can help enterprises optimize the allocation of logistics resources
A MODIFIED CLASS OF COMPOSITE DESIGNS FOR THE RESPONSE MODEL APPROACH WITH NOISE FACTORS
Get PDFA class of composite designs involves factorial, axial, and center points. Factorial points are with a variance-optimal design for a first-order or interaction model, and axial points provide information about the existence of curvature. The center points allow for efficient estimation of the pure quadratic terms. From these properties, a class of composite designs is recommended if resources are readily available and a high degree of precision of parameter estimate is expected and evolves from their use in sequential experimentation. However, there are often cost constraints imposed on experiments. Previous studies show that resolution, orthogonal quadratic effect property, and saturated or near-saturated design reduce the number of experiments. This study extends the response model approach with noise factors to composite designs satisfying these properties. These modified composite designs are further discussed and examined in terms of scaled prediction error variance and extended scaled prediction variance, which provides a good distribution of the prediction variance of the response. Based on these criteria, the best performance design is suggested according to the number of control and noise factors. As a result, we show that the modified designs showing robustness to noise factors and stability of predictive variance are a class of modified small composite designs and modified augmented-pair designs
EFFECTS OF DRIVER PERSONAL VARIABLES ON PREFERRED VEHICLE INTERIOR COMPONENTS SETTING
Get PDFThis study identified and characterized the relationship between driver personal variables and preferred vehicle interior components setting. A two-phase modeling approach was employed to characterize the temporal, logical process involved in the driver selection of a preferred vehicle interior components setting. The modified Bayesian multivariate adaptive regression splines (BMARS) modeling method was employed to identify nonlinear and interactive relationships. Forty-two male and forty-four female drivers with a wide range of ages, stature, and BMI participated in the data collection. A highly adjustable vehicle mock-up was used to empirically obtain each participant’s preferred vehicle interior components setting. The study results indicated substantial non-anthropometric variability in the driver-selected seat horizontal positions and identified various interpretable nonlinearities and interactions. The study findings improve the understanding of the relationship between driver personal variables and preferred vehicle interior configuration and further inform the vehicle interior package design for driver accommodation
MULTI-OBJECTIVE OPTIMIZATION MODELING OF INTEGRATED SUPPLY CHAIN FOR SOLID WASTE TREATMENT
Get PDFSolid waste management (SWM) has been proven as a vital research area, as it contributes in providing a basic and renewal source of production resources like recycled raw materials, fuel and energy sources. Hence, this research investigates the SWM problem by simultaneous consideration of key environmental and economic factors. In this regard, a multi-objective mathematical model is presented for an integrated solid waste supply chain to minimize total costs and environmental impacts while maximizing the recovered energy. The designed supply chain is being modeled as a weighted goal programming (WGP) model to achieve the desired objectives, and this model is solved by applying a simplex-based solution algorithm. In addition, the model and the solution algorithm are validated through the application on real case study data. The comparisons’ results show that the integrated supply chain’s model attains reasonably outperforming results in terms of minimizing the average total cost and environmental impacts
OPTIMAL REASSIGNMENT OF FLIGHTS TO AIRPORT BAGGAGE UNLOADING CAROUSELS IN RESPONSE TO TEMPORARY MALFUNCTIONS
Get PDFBeing able to efficiently reassign outbound flights to baggage unloading carousels (BUCs) following temporary malfunctions is very important for airport operators. This study proposes an optimization model with a heuristic to solve the carousel reassignment problem. The objective is to minimize the total disturbance and overlapping time caused by the reassignment of outbound flights. A heuristic is developed to efficiently solve large-sized instances. The proposed approach is then applied to solve real-world instances of the problem at a major international airport in Taiwan. The computation time is about two minutes. The objective value obtained with the heuristic is more than 15% better than that obtained by the manual approach currently used by the operator. The improvement is gained mostly from the reduction in total temporal disturbance and overlapping time. The proposed approach could assist the operator in reassigning outbound flights to BUCs in response to malfunctions
A NOVEL SPLIT SELECTION OF A LOGISTIC REGRESSION TREE FOR THE CLASSIFICATION OF DATA WITH HETEROGENEOUS SUBGROUPS
Get PDFA logistic regression tree (LRT) is a hybrid machine learning method that combines a decision tree model and logistic regression models. An LRT recursively partitions the input data space through splitting and learns multiple logistic regression models optimized for each subpopulation. The split selection is a critical procedure for improving the predictive performance of the LRT. In this paper, we present a novel separability-based split selection method for the construction of an LRT. The separability measure, defined on the feature space of logistic regression models, evaluates the performance of potential child models without fitting, and the optimal split is selected based on the results. Heterogeneous subgroups that have different class-separating patterns can be identified in the split process when they exist in the data. In addition, we compare the performance of our proposed method with the benchmark algorithms through experiments on both synthetic and real-world datasets. The experimental results indicate the effectiveness and generality of our proposed method
MACHINE LEARNING PREDICTION MODEL FOR SMALL DATA SETS INSTEAD OF DESTRUCTIVE TESTS FOR A CASE OF RESISTANCE BRAZING PROCESS VERIFICATION: Destructive tests substitution for resistance brazing process verification
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This paper presents a case study of Machine Learning (ML) prediction model for small data sets instead of destructive testing of brazed contacts. The main problems noted in the study were data availability, data quality, an extremely low number of NOK destructive test results and overall small data set. Recent researches are not very often focused on small data set ML prediction models and even less often on its application in resistance brazing. This paper tends to bridge this gap. The case study methodology consists of data collection, data preparation, correlation analysis, feature selection, model training, hyperparameter optimization, and model evaluation. It is proven possible to train ML prediction model with small datasets to predict numerical test outcomes if dataset quality is adequate. The practical use of this approach is reflected in the reduction of test costs since destructive tests can be quite expensive, and ML prediction model is one time, relatively low investment