International Journal of Industrial Engineering: Theory, Applications and Practice
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PARCEL LOCKERS LOCATION AND ROUTING PROBLEM: A BIBLIOMETRIC, DESCRIPTIVE, AND CONTENT ANALYSIS OF EXISTING STUDIES
No full textParcel lockers and collection and delivery points are not new concepts and are recognized by many firms; however, they are not broadly utilized worldwide. The employment of this concept can provide a solution to failed deliveries. In this study, parcel lockers and similar concepts are reviewed quantitatively and analytically. Parcel lockers have been widely studied; however, quantitative approaches are lacking. Therefore, mathematical models, spatial analysis, optimization, and simulation studies on location and routing problems have been extensively examined. This study aims to indicate the current state and future directions. After a rigorous elimination process in the Scopus database, 72 papers were reviewed, and descriptive, bibliometric, and content analyses were performed. This study shows that parcel lockers and collection and delivery points offer various advantages as solutions to last-mile delivery problems, such as regulating traffic congestion, reducing faulty deliveries and the resulting costs, and increasing consolidation
EFFECT OF GMAW TWIN WIRE TANDEM PARAMETERS ON MECHANICAL PROPERTIES OF WELDED JOINTS
Get PDFTandem welding has the advantage of higher productivity over a limitation considering narrow groove joints. Tandem has higher deposition and heat input. To study the effect of variation in robotic tandem parameters on fatigue properties of welded joints, the following parameters were investigated, viz. first case root & the second pass using a single wire, subsequent run tandem twin and second case using root pass using a single wire, subsequent run using tandem twin wire welding. Butt joint samples were subjected to ultrasonic testing, mechanical & metallurgical testing, and fatigue testing. Found Group 1 tensile strength, yield strength, % elongation & impact value to be 12 %, 9 %, 23 % & 14 %, respectively, higher than Group 2. Explained variations in microhardness & better fatigue life were observed for Group 1. This investigation will help manufacturers in decision-making while selecting tandem parameters considering productivity or reliability
AUTOMATED DEFECT INSPECTION ALGORITHM FOR SEMICONDUCTOR-PACKAGED CHIPS
No full textDetecting product quality defects through image recognition technology is one of the key technologies of intelligent manufacturing and an important step for enterprises to construct a smart factory. The internal wire bonding of a chip easily receives interference and produces defects in the capsulation step of the semiconductor enterprise. Companies need to pick out defective chips to prevent them from entering the market. A traditional method is to use human visual inspection, which may lead to low efficiency and high labor cost. Hence, this study intends to use a machine vision detection method based on image processing technology. The objectives are to identify the defect of a chip and replace the workers with human manual detection work. This study proposes two algorithms to solve such problems. The template matching algorithm (TMA) determines whether the chip is defective based on the standard template. Meanwhile, the neighborhood comparison algorithm (NCA), which is implemented by Halcon software, calculates the similarity of the neighbor chips to judge the target chip’s defects. A German semiconductor company has provided enough samples to support our research. Experimental results show that the two algorithms are effective in the defect detection of specific products. The advantage of the TMA lies in its processing speed, but the applicability and accuracy of NCA are excellent. The algorithm proposed in this study can be used for enterprises through integration into the actual detection process
IMPROVED PARALLEL UNIVERSES ALGORITHM: AN EVOLUTIONARY ALGORITHM FOR COMBINATORIAL OPTIMIZATION
No full textThis paper presents a basic approach to parallel universes algorithm (PU), and also a new version of PU. The new version is called the improved parallel universes (IPU) algorithm and is based on focusing on the solutions with a higher probability of improvement and two other strategies. These changes caused the IPU algorithm speed and optimality beyond what is achieved by the PU algorithm. The efficiency of the IPU algorithm is evaluated with the test function. Also, to take a closer look at the ability of the proposed algorithm, its performance is studied in the "capacitated vehicle routing problem" (CVRP) and "hybrid flow shop scheduling problem" (HFS), these two problems are selected because of their different structures. The results of the proposed algorithm on these two problems are compared with the results of several metaheuristic algorithms and the results indicate that the proposed algorithm is able to compete with other metaheuristic algorithms
AN APPROACH FOR DETERMINING THE COST MATRIX OF MULTIVARIATE QUALITY LOSS FUNCTION
No full textMultivariate quality loss functions are commonly used in product and process design parameter optimization, which involves simultaneous consideration of multiple responses in determination of the levels of design parameters that provide high quality performance. These functions are also used in statistical tolerancing and quality improvement decision making. This study investigates the bivariate loss function in terms of its ability to represent different values or preferences a decision maker may attribute to different settings of responses. Then, an interactive and evolutionary method for estimating parameters of the multivariate quality loss function is proposed. This method can be used for such functions regardless of the number of quality characteristics under consideration. It is shown that the method converges to the true underlying loss function after a few iterations even when the information provided by the decision maker contains certain degrees of errors
FRAMEWORK TO DETERMINE THE QUALITY COST AND RISK OF ALTERNATIVE CONTROL PLANS IN UNCERTAIN CONTEXTS
No full textIn manufacturing companies, quality control plans are essential to fulfill quality requirements and have associated quality appraisal and failure costs. However, there are barriers to the determination of quality costs, limiting the ability of companies to establish quality control plans at the lowest cost and, consequently, becoming more competitive. The paper’s objective is to develop a framework to define a quality control strategy for a manufacturing process, by selecting amongst alternative quality control strategies (e.g., 100% inspection, statistical process control, and no inspection), the one that minimizes quality costs. The cost of quality determination depends on many parameters; some of them are volatile and uncertain. The framework represents such uncertainty by intervals and through simulation defines the best quality control strategy. A risk indicator is also developed to represent the possibility that the quality control strategy defined may not be optimal. Sensitivity analysis is performed to identify the model parameters with more impact on the quality cost, allowing to create multidisciplinary teams through cooperation, can better characterize relevant uncertain parameters, among the many parameters Industry 4.0 era makes available to managers
A NOVEL METHODOLOGY FOR PROCESS PARAMETER OPTIMIZATION BASED ON SUPPORT VECTOR DATA DESCRIPTION
No full textA novel methodology for process parameter optimization based on support vector data description (SVDD) is proposed to improve the quality of steel products and reduce the mass quality problems caused by the inability to timely adjust the production process parameters. First, the control limit is calculated by the SVDD method with normal samples. Next, the normal samples adjacent to abnormal samples are selected based on the Euclidean distance and used to construct the optimization rules, which are adopted to optimize the production process. The effectiveness of the proposed method is verified using a dataset from a steel production process. The results indicate that the methodology has good applicability in complex production processes with nonlinear and strong correlation characteristics
FEATURE SELECTION AND PARAMETER OPTIMIZATION FOR SUPPORT VECTOR MACHINES USING PARTICLE SWARM OPTIMIZATION AND HARMONY SEARCH
No full textThe present paper proposes a mechanism, Diverse Particle Swarm Optimization and Harmony Search (DPSO_HS), which finds feature subsets and parameter values for Support Vector Machines (SVM) when addressing classification problems by incorporating Particle Swarm Optimization (PSO) and Harmony Search (HS). Specifically, we introduced HS to enhance diversity in the PSO process since it has the advantage of providing diverse solutions as compared to other methodologies, as it considers all solutions in memory when improvising a new solution. For performance evaluation, various datasets with a wide range of features, instances, and classes were considered. DPSO_HS showed an increased diversity and classification accuracy as compared to PSO where statistical significance was found in most datasets. In addition, with two different hybridized approaches based on PSO, we observed that the proposed method showed higher accuracy for most datasets. We also reviewed the results of previous research with identical datasets and found that DPSO_HS achieved higher or equal accuracy rates for most datasets
PREDICTION OF PARTICLE SIZE DISTRIBUTION OF A BALL MILL USING IMPROVISED NEURAL NETWORK TECHNIQUE AND TIME SERIES
No full textIn the mining industry, it is important to minimize the wastage of raw materials while achieving the desired particle size distribution by grinding the original input mix. To date, the procedure is performed manually, and there is no such control mechanism for grinding that reduces wastage to achieve the desired output, resulting in the loss of material. This study aims to develop an autonomous system for predicting the desired states of breakage by analyzing the acoustic signatures of the materials being crushed. The signal envelope is detected from the time-series acoustic data, which changed gradually during grinding. We designed an autoregression model using the signal envelopes of different grinding stages to predict the desired particle size. In another scenario, the acoustic signatures are approximated to a Gaussian distribution, and the kernel density estimation function is applied to obtain the best-fitted observed data points with the help of local points. An improvised neural network technique is used to classify the unknown patterns of crushing at different breakage states, which validates the experimental results. The network is trained with the input patterns corresponding to the observed data points and the output of the autoregression model. The prediction accuracy of the proposed approach is approximately 97%
THE MOMENT INTEGRATED SOLUTION METHOD IN MULTI–CRITERIA DECISION–MAKING
No full textIn this study, the aim was to develop a new method called The Moment Integrated Solution (THEMIS) method, inspired by moment as used in Physics, and is proposed to solve Multi-Criteria Decision-Making (MCDM) problems. The most important characteristic of the proposed method is based on the pairwise comparison by computing THEMIS method taking the value of [0,1]. According to THEMIS method, firstly, pairwise comparison criteria that have been evaluated by the decision-maker are computed by THEMIS, thereby creating a decision matrix that will be computed by THEMIS method. Finally, the scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on whichever best alternative is selected. To analyze the proposed method and evaluate its performance, by computing both the Analytical Hierarchy Process (AHP) and THEMIS methods, we carried out some numerical examples, such as 'a family's house buying' in the literature. The 'mobile telephone selection' problem in the literature was analyzed statistically, and the proposed method with a 5x5 matrix was simulated. THEMIS method has given better results, albeit with small differences. However, it should be noted that the improvement is not a significant percentage. It was observed that the method has the capability to accurately rank alternatives and criteria as well as AHP. It is considered that the methodological contribution of the proposed method brings a new approach to group decision-making. It can also be considered as an alternative method under MCDM