International Journal of Industrial Engineering: Theory, Applications and Practice
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TWO NOVEL ROBUST NETWORK DATA ENVELOPMENT ANALYSIS MODELS TO OBTAIN THE PERFORMANCE SCORE INTERVAL OF MULTI-STAGE SERIES SYSTEMS
No full textThe main goal of this paper is to present a new approach for measuring the performance of n-stage series systems in the presence of uncertain data, which are two challenging issues in evaluating the efficiency of Decision-Making Units (DMUs) using traditional Data Envelopment Analysis (DEA) models. By using a Network DEA model and its dual, as well as using Bertsimas et al.'s robustness technique, two Robust Network DEA have been presented. These models can display a range of DMUs performance with appropriate accuracy. Proposed models were used to determine the efficiency range of Iranian dairy companies' supply chain with three stages. The results show that the proposed models are applicable and effective. Total efficiency bounds are obtained with percentage deviations of 20%, 10% and 5%. The lower bounds have relative errors of 0.39, 0.23 and 0.12 and a correlation coefficient of more than 97%, and the upper bounds have relative error of 1.1, 0.84 and 0.62 and a correlation coefficient of about 90%. Therefore, the proposed model for calculating the lower bound is more accurate. The calculation of the efficiency bounds of the sub-stages also confirms this issue. Finally, the obtained results have been compared with the values obtained through a fuzzy three-stage DEA model, our results have a higher correlation coefficient and more accurate upper bounds
INTEGRATED ARTIFICIAL IMMUNE SYSTEM AND TAGUCHI APPROACH FOR PRODUCTION SCHEDULING IN THE GARMENT INDUSTRY
No full textPresently, Vietnam is the third-largest garment exporter in the world, with a 6.1% market share. Nevertheless, Vietnam's apparel industry faces fierce competition from other countries making low-cost garments. Enterprises must enhance many aspects to survive in a competitive market, including ensuring product availability at optimal prices. To achieve this, all production managers shall schedule production while factoring in early and late production costs. This study presents a novel integer nonlinear programming model to minimize the cost of earliness and tardiness, considering weight for storage. The metaheuristic utilized to solve the problem in this work is the integrated artificial immune system (AIS) algorithm and the Taguchi technique. Subsequently, implementing a sensitivity analysis to ascertain the weight for storage is a crucial decision faced by production managers. Finally, this study compares the proposed method to the Vietnamese garment industry's current technique and shows its suitability for production scheduling in this sector
Improved Systematic Layout Design Based on Low-Carbon Plant Factory
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In recent years, mechanization and greening have become the inevitable trend of agricultural development. Plant factories have the advantages of high planting area efficiency, stable crop growth and continuous production. Although its demand and scale are increasing, there is still a lack of low-carbon research on the layout of plant factory, which leads to the restriction of actual production capacity and unnecessary waste of resources and environmental burden. Therefore, this paper takes a leafy vegetable processing plant factory as the research object and puts forward a plant layout design based on the improved system layout planning (SLP) method for the design requirements of high yield, high efficiency and low carbon. In this paper, the initial layout plan of a vegetable processing plant is output by combing the plant foundation data (PQRST) needed for layout planning, analyzing and quantifying the logistic, carbon emission, and non-logistic relationships among the operating units. And draw the position correlation map and the area correlation map based on the integrated relationship, and correct them according to the actual constraints and restrictions. For the three alternative layout schemes of the plant factory based on the improved SLP method, the material flow volume and carbon emission factors of the schemes are calculated and analyzed, and the optimal layout scheme 2 is determined according to the results. Combined with the parameter design scheme of the production line, the layout scheme of the vegetable processing plant and the factory simulation software, the detailed design of the layout factory simulation model is established, and the original scheme is compared with the new scheme to verify the design effect
Digital Twin-Oriented Collaborative Optimization of Process Planning and Scheduling in A Flexible Job Shop
No full textProcess planning and scheduling are two crucial components in flexible manufacturing systems. To address the challenge of information interaction and sharing during the process planning and scheduling stage of parts, a digital twin-oriented approach is proposed. The objective is to optimize the maximum makespan while accommodating fluctuations in the job shop site. Firstly, in the process planning stage, an enhanced genetic algorithm is employed to generate multiple near-optimal process routes. These routes are coded using a four-level coding system, enhancing the efficiency of the planning process. Then, in the production scheduling stage, a hybrid particle swarm optimization algorithm is constructed, considering the characteristics of multi-process routes and the status of shop production resources and production systems. To improve local search ability, various neighborhood structures are utilized. Finally, the proposed method is evaluated through production example simulations and compared with genetic algorithm and particle swarm optimization. The results demonstrate that this method has a quicker convergence rate, shorter execution time, and higher computation precision, which is not only remarkable but also practical for addressing the collaborative optimization of process planning and scheduling in discrete manufacturing systems
Adjustable Lateral-Titling Mechatronic Bed for Assisting Pediatric Posture Drainage
No full textLower respiratory tract infections (LRTIs) are commonly found among infants and result in high mortality. Airway clearance techniques (ACTs) like postural drainage (PD) help clear pulmonary secretions in the lungs. A mechatronic bed was designed and developed using two symmetrical four-bar linkage mechanisms for the lateral tilting of a pediatric patient while keeping both side rails in a vertical position for safe operation. This assists in performing PD. The mechatronic bed allows a single caretaker to manually perform PD adjustment through a Raspberry Pi graphical user interface (GUI), thereby controlling the bed’s lateral tilt angle between [-25°, 25°] using a geared DC servo-motor. Validation with a 3.4-kg infant simulator confirmed safety. No rolling or flipping motion of the infant simulator was observed, with only minor slippage at extreme angles. This bed structure and electronic equipment comply with medical device safety guidelines, ensuring the safety of pediatric patients and caregivers. The mechatronic bed significantly enhances PD treatment safety and efficiency, reducing caregiver strain and improving patient recovery in pediatric care
Assessment of Measurement System Capability: A Case Study on Complex Geometry Parts
No full textThis paper discusses the most fundamental problem related to the variation of the complex geometry part manufacturing process caused by the variation of the measuring system, and it outlines potential solutions. Optical measuring instruments are frequently used for measurements of complex geometry, but the complex features hinder uniform part illumination, which makes accurate measurement more challenging. The aim of this research is to identify measurement variation sources that are significant to the manufacturing process variation. For the comparison of the capabilities of the measurement system, the two most frequent measuring devices used in the process are analyzed (digital micrometer and 3D coordinate measuring machine (CMM)). The data analysis method in this research is based on measurement system capability indices (Cg and Cgk). It was found that the Cg and Cgk indices are greater than the threshold value of 1.33, whereas the repeatability R and accuracy and repeatability A&R indices are less than the threshold of 15%. However, results showed that the absolute error is significantly higher with micrometer measurements, identifying the micrometer as a major source of the variation, which, in the end, affects the manufacturing process and misleads decision-makers to make wrong conclusions
Assessing the Impact of Urban Morphology on Metro-Bicycle Sharing Transfers Using Random Forest Classification
No full textThe urban built environment shapes the city's morphology, which possesses the capacity to influence the use of bike-sharing systems. Bike-sharing offers a solution to the "first-last mile" problem associated with metro systems, providing flexible and cost-effective means to enhance transit accessibility and reduce travel expenses. This study employs a bike-sharing trajectory dataset to analyze usage patterns and integrates urban morphology—defined by land use, Points of Interest (POIs), and spatial clusters of transportation facilities—to determine if the urban form can affect cycling behavior. The findings reveal that, in addition to urban morphological factors, bike-sharing usage patterns exhibit strong classification performance. The misclassification rates were 0.3439 for departures and 0.2472 for arrivals. The difference in misclassification rates can be attributed to the diverse urban contexts surrounding metro stations. For instance, stations located in residential areas tend to have more predictable bike-sharing patterns, resulting in lower misclassification rates. In contrast, stations in commercial zones with higher land-use diversity and Points of Interest (POIs) exhibit more variability in cycling behavior, leading to higher error rates. The research demonstrates that the spatial characteristics of urban morphology—such as land-use diversity and clustering of POIs—play a pivotal role in influencing metro-bicycle sharing patterns. The model achieved an 83.5% accuracy rate in distinguishing between bike-sharing rides to or from metro stations. These findings underscore the integrated role of urban form in shaping travel behavior, especially regarding the synergy between metro systems and bicycle-sharing
An Effective Metaheuristic Algorithm for Uniform Parallel Machine Scheduling with Resource Consumption Restriction to Minimize The Maximum Lateness
No full textThis paper addresses a uniform parallel machine scheduling problem with resource consumption restriction. The objective is to minimize the maximum lateness under the constraint that the total resource consumption cannot exceed the given budget. To solve this NP-hard problem, a mixed integer programming model is first developed to obtain the exact solution for small-sized instances. Afterward, an effective simulated annealing (SA) algorithm with a repairing procedure is designed to generate near-optimal solutions within reasonable CPU time. The performance of the designed algorithm is evaluated by comparing it with two metaheuristic algorithms adapted from the relevant literature that address similar scheduling problems. The experimental and statistical results show that for small-sized instances, the proposed SA can optimally solve 99.13% of these instances, significantly outperforming its competitors. For large-sized instances, it is shown that the proposed SA can provide the best solution for 97.88% of these instances, markedly surpassing its competitors
Neutrosophic Optimization in Transportation Networks: Navigating Transportation Challenges with and Without Warehouses
No full textNeutrosophic set theory is an extension of classical set theory that deals with uncertainty, imprecision, and indeterminacy by introducing three degrees of membership, and further it offers the researchers a wide range of applications in numerous disciplines. In general, neutrosophic sets (NS) focus on the hesitant, ambiguous, and uncertain data present in real-world mathematical challenges. They are an enhanced form of crisp, fuzzy, and intuitionistic fuzzy sets. The simplest form of the neutrosophic set is a single-valued set, and it provides better outcomes than a regular NS. Recent research enhances the applications of single-valued neutrosophic sets (SVNS) through approaches like MCDM, MADM, MCGDM, and many other extensively deployed fields. This study has proposed two different ranking methods, namely the Removal Area Method (RAM) and the Mean Interval Method (MIM), to de-neutrosify the single-valued trapezoidal neutrosophic numbers (SVTNNS). These methods offer significantly greater accuracy compared to conventional transportation problem (TP) approaches, particularly when evaluated using various ranking functions through (SVTNNS). Further, the TP has been addressed with or without warehouses to determine the optimum cost for the proposed problems, where all the source, demand, and cost representations are treated as SVTNNS. The quantitative analysis of the transportation problem, both with and without the inclusion of warehouses, yielded optimal results of 3,684.83 and 263,703.64 using the MIM and RAM ranking methods, respectively. Thus, the proposed ranking methods demonstrate their innovation and effectiveness by outperforming traditional approaches in transportation problems, as validated through comprehensive numerical examples. Additionally, a thorough comparison with conventional methods highlights the impact of the new techniques in achieving optimal solutions. The detailed results, further supported by sensitivity analysis, provide strong evidence that the optimal solutions are robust and justified, confirming the reliability and accuracy of the proposed methods
Investigation of Non-Value-Added Activities to Reduce Lead Time in The Mass-Customized Glass Process Industry
No full textIncreasing levels of customization in customer orders leads to numerous new challenges in the industry. One significant aspect is achieving the optimal lead time to meet customer demands. The reconfigurable hybrid permutation mass customization problem (RHPMCP), a subset of flow shop problems with significant application in the mass-customized tuff glass process industry, is the primary focus of this study. We categorize this study into two phases; the first phase investigates the non-value-added activities to identify which output parameter (i.e., Makespan, flow-time, idle-time, and efficiency) significantly affects the schedule. We employ a new decision-making method for investigation by integrating a Z-number-based Consistent Fuzzy Analytic Hierarchy Process (Z-CFAHP) and a Z-number-based fuzzy Combined Compromise Solution (Z-FCoCoSo). The second phase, using Analytical Batch Enhanced State-Action-Reward-State-Action Optimization (ABESO), reduces the identified high-impact output parameter makespan to obtain the optimal lead time. The proposed approaches are validated by sensitivity analysis for decision-making, and scheduling problems are compared to the existing scheduling rule in the real-time tuff glass process industry. This research provides a practical solution for optimizing lead time in the tuff glass process industry, demonstrating the effectiveness of the proposed method.