International Journal of Industrial Engineering: Theory, Applications and Practice
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A PCA-BASED METHOD FOR REMANUFACTURING PROCESS OPTIMIZATION FROM SUSTAINABILITY ASPECTS
No full textThe optimization of the remanufacturing process is an important issue in remanufacturing activities because it affects the performance and quality level of remanufactured products to a great extent. However, the implementation of the remanufacturing process is affected by many factors, such as technical, environmental, economic, and social factors, to different degrees. To implement remanufacturing effectively, a remanufacturing process optimization method is developed on the basis of principal component analysis (PCA) theory from the aspect of sustainability. In this method, the system boundary is first defined, and then the remanufacturing process is designed to be associated with four main sustainability criteria: technical process, economic cost, environmental effect, and social influence. Each criterion includes several indicators, which total up to sixteen. Afterward, the sixteen sustainability indicators are reduced to three main indicators via PCA dimension reduction method. Finally, the remanufacturing process is optimized by the construction comprehensive evaluation function of the three main indicators. To verify the effectiveness of the method, diesel engine remanufacturing is taken as the case
AN EXPLORATORY STUDY ON THE CHARACTERISTICS OF DRIVERS' PREFERRED SEAT POSITIONS
No full textAmong many vehicle interior components, the one that interacts with the driver continuously at all times is the driver’s seat. Many studies and standards have utilized driver-selected seat positions (DSSPs) as a basis for evaluating vehicle interior designs for driver comfort and safety. However, little research has examined the basic characteristics of DSSPs. This study analyses empirically obtained DSSPs to explore their geometric, mathematical and statistical properties. Six indices pertinent to the size, shape, orientation and location of each individual’s DSSP point cloud are employed. The normality of the DSSP point clouds and possible correlational relationships among the indices and those between the indices and selected anthropometric dimensions are statistically tested. The results suggest that 1) DSSP prediction and modeling must reflect the unimodal, non-normal nature of DSSP distributions and the correlational structures identified, and 2) both inter-individual/intra-individual variability in DSSPs need to be considered in designing and evaluating seat adjustability features
RESEARCH ON THE MAINTAINABILITY EVALUATION METHOD OF SHIP POWER CABINS IN NARROW SPACES
No full textIn order to solve the design problems of narrow-space maintainability of ship power cabins at the research and development (R&D) stage, ergonomics simulation and systems engineering evaluation techniques are used to study narrow-space maintainability by taking a ship power cabin as an object. Firstly, the core indexes of comfort, maintenance workspace, accessibility and visibility are identified, and weights are determined after consulting technical experts; then, maintenance tasks are decomposed into basic operation units to guide visualization simulation; finally, operation units are analyzed one by one to identify maintenance weaknesses and make recommendations for improvement. Taking "pipeline oil leakage maintenance" as an example, the feasibility of the method is verified by comparing and analyzing the maintainability before and after improvement. The results show that this method can identify and solve design problems of maintainability in narrow spaces of ship power cabins at the R&D stage and has practical engineering value
SPORTS ECOTOURISM DEMAND PREDICTION USING IMPROVED FRUIT FLY OPTIMIZATION ALGORITHM
No full textWith improvements in the national consumption level, the tourism industry is playing an increasingly important role in the national economy, and the proportion of tourism revenue in GDP is constantly increasing. In this paper, we first improve the standard Drosophila algorithm by adaptively adjusting the fly population number and search step size while optimizing the initial iteration position and improving the local search ability and search efficiency. Then, the improved algorithm (FOA) is combined with the echo state network to establish a two-stage combined prediction model called the Adaptive Fruit Fly Optimization Algorithm-Echo State Network (AFOA-ESN). The experimental results show that the AFOA-ESN model has higher prediction accuracy compared to other prediction models, and the convergence rate and prediction accuracy of the AFOA-ESN are better than the standard ESN and FOA-ESN, proving the effectiveness of model improvement
PRODUCTION SCHEDULING OPTIMIZATION OF FLEXIBLE MANUFACTURING SYSTEM FOR GREEN MANUFACTURING
No full textThe manufacturing industry is the economic pillar of the country, which can provide a large number of jobs and alleviate the employment pressure. In the manufacturing industry, the scheduling problem is the core problem of manufacturing intelligence and automation. Scheduling plays an extremely important role in the productivity as well as reliability of large-scale complex production systems. Therefore, a production scheduling method based on a genetic algorithm is proposed, which first uses a genetic algorithm to achieve scheduling optimization. Then, the minimizing carbon emission method is introduced to achieve green manufacturing. Finally, the production scheduling system (PSS) of flexible manufacturing form is constructed. The experimental results show that the proposed production scheduling method has a better scheduling optimization effect. The resource utilization rate reaches 0.884, and the workpiece processing time is reduced to 0.32s. The carbon emission cost is significantly lower than that of the traditional production scheduling system. The results show that using genetic algorithms to optimize the production scheduling of flexible manufacturing systems is feasible. It can improve production efficiency while reducing carbon emissions. The model proposed in this study can be a good solution to practical problems in industrial production intelligence
BAYESIAN INFERENCE TO ESTIMATE RANDOM FAILURE PROBABILITY
Get PDFThis study introduces a process based on Bayesian inference, enhancing the accuracy of random failure probability estimation, outlined in a detailed six-step procedure. This method focuses on comprehensive data analysis and precise probability estimations, proving particularly beneficial for limited datasets. Applied to brake disc random failure probability assessment, our approach's results were compared with those obtained through Maximum Likelihood Estimation (MLE) across various specimen sizes. This comparative analysis included both graphical and statistical evaluations. The experimental findings demonstrate that our Bayesian inference-based process effectively addresses the challenges posed by small datasets, significantly enhancing estimation accuracy. This methodology is especially advantageous in scenarios where data collection is difficult, providing reliability engineers with an essential framework for leveraging prior information to improve risk management in diverse industrial applications
SERCON-BASED TIMESTAMPED VIRTUAL MACHINE MIGRATION SCHEME FOR CLOUD
Get PDFWith the advent of cloud computing, the need for deploying multiple virtual machines (VMs) on multiple hosts to address the ever-increasing user demands for services has raised concerns regarding energy consumption. Considerable energy is consumed while keeping the data centers with a large number of servers active. However, in data centers, there are cases where these servers may not get utilized efficiently. There can be servers that consume sufficient energy while running resources for a small task (demanding fewer resources), but there can also be servers that receive user requests so frequently that resources may be exhausted, and the server becomes unable to fulfill requests. In such a scenario, there is an urgent need to conserve energy and resources which is addressed by performing server consolidation. Server consolidation aims to reduce the total number of active servers in the cloud such that performance does not get compromised as well as energy is conserved in an attempt to make each server run to its maximum. This is done by reducing the number of active servers in a data center by transferring the workload of one or more VM(s) from one server to another, referred to as VM Migration (VMM). During VMM, time is supposed as a major constraint for effective and user-transparent migration. Thus, this paper proposes a novel VM migration strategy considering time sensitivity as a primary constraint. The aim of the proposed Time Sensitive Virtual Machine Migration (TS-VMM) is to reduce the number of migrations to a minimum with effective cost optimization and maximum server utilization
RELATIONSHIP BETWEEN THE DEGREE OF URBAN INTELLIGENCE WITH ARTIFICIAL INTELLIGENCE AND FUZZY ALGORITHMS AND THE PERFORMANCE OF ENTERPRISES
Get PDFThis article has comprehensively evaluated the development and evaluation model of intelligent cities on the performance of enterprises in the performance of intelligent cities in the artificial intelligence technology model, established a fuzzy algorithm analysis and evaluation model, and comprehensively evaluated the development level of intelligent cities and the level of performance management in cities. By using AI technologies such as the Internet of Things cloud computing, the intelligent terminals of the whole city are organically integrated to achieve the integration and development of various industries in smart cities and promote the two-way improvement of artificial intelligence and the performance management of enterprises in the city. At the same time as the development advantage of urban enterprises, the development advantage of urban enterprises is more convenient for making human production and living activities in cities through the integration and development of diverse elements and multi-technical fields in life
A NOVEL MODEL FOR THE CALCULATION OF SAFETY STOCK OF PERISHABLE PRODUCTS WITH A TOTAL WASTE CONSTRAINT
Get PDFPerishable products cover a high percentage of all goods. The variability, long lead times, risk period, and high service level increase the safety stock level. An increase in safety stock will also increase the probability of perished products because of the increased probability of sales of less than stock during shelf life. This study proposes a model for calculating safety stocks of perishable products besides showing the effect of perishability on service level. The effects of long lead times, risk periods, high sales and lead-time variance, and short shelf life adversely affect perished products. The study investigates and proposes a novel model for calculating total expected waste and costs with a waste quantity constraint. A real-life example compares a proposed model with waste constraints and the traditional safety stock model based on costs and waste quantity. The case study shows the better results of the proposed models
MACHINE LEARNING METHODS AND PREDICTIVE MODELING TO IDENTIFY FAILURES IN THE MILITARY AIRCRAFT : Aicraft quality control
No full textModern aircraft are costly and require heavy investment. It is the same regardless of industries, such as commercial airlines and militaries. It is primarily about maintaining desired readiness by reducing ground time in the militaries, which is critical to maintaining air superiority and winning the war. There are two types of maintenance activities such as preventive and corrective maintenance. Preventive maintenance requires taking action before failures happen. Meanwhile, corrective maintenance reacts to failures, which takes time to buy parts and repair failed components. If we can predict aircraft failures accurately, we will be able to change corrective maintenance activities to preventive maintenance activities, which will reduce aircraft downtime and, thus, increase aircraft readiness or availability. This paper proposes multiple machine learning tools to minimize aircraft downtime to predict aircraft failures with the highest accuracy possible. This paper validates the usefulness of the proposed machine learning tools by experimenting with the actual data obtained from the maintenance record of 33 aircraft operated by the U.S. Air Forces