Managing product variety through configuration of pre-assembled vanilla boxes using hierarchical clustering

Postponement strategy and platform-based production are common practices of mass customisation to address supply chain challenges due to the requirement of product variety. This paper focuses on implementing mass customisation through development of semi-finished forms of products (vanilla boxes) to reduce supply chain cost and facilitate the production process. The challenge is that the possible number of vanilla box configurations grows dramatically with the increase in number of product variants. In the solution approach, the basic information of product variety is captured in a matrix format, specifying the component requirements for each product variant. Then, hierarchical clustering is applied over the components with the considerations of demands. The clustering method consists of three major stages: similarity analysis, tree construction and tree-based analysis. The key stage is similarity analysis, in which problem-specific information can be incorporated in the clustering process. Two numerical examples from the literature are used to verify that the clustering approach can yield good-quality solutions.     

A hierarchical model for optimal supplier selection in multiple sourcing contexts

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches.     

Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping

Previous studies have shown that hybrid clustering methods based on textual and citation information outperforms clustering methods that use only one of these components. However, former methods focus on the vector space model. In this paper we apply a hybrid clustering method which is based on the graph model to map the Web of Science database in the mirror of the journals covered by the database. Compared with former hybrid clustering strategies, our method is very fast and even achieves better clustering accuracy. In addition, it detects the number of clusters automatically and provides a top-down hierarchical analysis, which fits in with the practical application. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome provides an appropriate representation of the field structure by comparing with a text-only or citation-only clustering and with another hybrid method based on linear combination of distance matrices. Our dataset consists of about 8,000 journals published in the period 2002–2006. The cognitive analysis, including the ranked journals, term annotation and the visualization of cluster structure demonstrates the efficiency of our strategy.     

Data Mining with Clustering Algorithms to Reduce Packaging Costs: A Case Study

Reducing package‐related cost is essential for various companies and institutions. Different packages are usually designed separately for each and every product, which results in less cost‐effective packaging systems. In this study, a data mining model with three clustering algorithms was developed to modularize a packaging system by reducing the variety of packaging sizes. The three algorithms were k‐means clustering, agglomerative hierarchical clustering and self‐organizing feature map. The package models with similar shapes and sizes were clustered automatically and replaced by one package model with a size that suited them all. The study also analysed the financial effects including the purchasing and inventory costs of the package material and the transportation cost of the packaged products. The case study was carried out at Ericsson to select the best clustering algorithm of the three and to test the effectiveness and applicability of the proposed model. The results show that the packaging system modularized by the agglomerative hierarchical clustering algorithm is more cost‐effective in this case compared with the ones modularized by the other two clustering algorithms and with the one without modularization. Copyright © 2017 John Wiley & Sons, Ltd.     

Semantic views — Interactive hierarchical exploration for patent landscaping

In this paper, we investigate whether a semantic representation of patent documents provides added value for a multi-dimensional visual exploration of a patent landscape compared to traditional approaches that use tf–idf (term frequency–inverse document frequency). Word embeddings from a pre-trained word2vec model created from patent text are used to calculate pairwise similarities in order to represent each document in the semantic space. Then, a hierarchical clustering method is applied to create several semantic aggregation levels for a collection of patent documents. For visual exploration, we have seamlessly integrated multiple interaction metaphors that combine semantics and additional metadata for improving hierarchical exploration of large document collections.     

一种改进的传感器网络分簇分层路由协议

在分析了无线传感器网络中传统的LEACH和LEACH-C路由协议基础上,结合MTE路由协议思想,提出了一种新的改进型分簇分层路由协议(improved clustering hierarchical routing protocol,ICH).文中簇首节点可以采用多跳方式传输数据包,且在选择中继节点时考虑节点剩余能量,对进入下一轮的条件进行了限制.实验表明,改进后的ICH协议的节点存活率比LEACH-C好.     

基于谱系聚类的随机子空间模态参数自动识别

针对随机子空间模态识别过程因人为参与造成识别结果不准确的问题,提出在随机子空间识别过程中引入谱系聚类算法实现模态参数自动识别。通过两种不同状态矩阵计算方法所得到的模态之间的相似程度以及各阶模态的能量剔除由噪声、模态过估计等因素引起的虚假模态;引入谱系聚类算法对结果进行拾取,以频率、阻尼比、模态振型、模态能量作为聚类因子计算结果之间的相似性,通过其将计算结果分为若干类,提取元素多于一定值的类作为拾取结果,实现结果的自动拾取。通过数值仿真和实例分析验证本文方法的有效性。     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.     

传感器网络目标恢复算法研究

分析了传感器网络在目标跟踪过程中产生目标丢失故障的原因.在此基础上采用分簇设计思想,通过对跟踪目标移动速度的计算和目标搜寻区域的建立等方法,提出了目标丢失故障恢复新的算法和技术.实验证明,所提出的算法和技术在延长传感器网络生存时间前提下,能够有效地解决跟踪目标发生丢失故障后目标快速恢复问题.     

A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication

Defects on semiconductor wafers tend to cluster and the spatial defect patterns of these defect clusters contain valuable information about potential problems in the manufacturing processes. This study proposes a model-based clustering algorithm for automatic spatial defect recognition on semiconductor wafers. A mixture model is proposed to model the distributions of defects on wafer surfaces. The proposed algorithm can find the number of defect clusters and identify the pattern of each cluster automatically. It is capable of detecting defect clusters with linear patterns, curvilinear patterns and ellipsoidal patterns. Promising results have been obtained from simulation studies.     

Module partition for 3D CAD assembly models: a hierarchical clustering method based on component dependencies

Reusing previous CAD assembly models directly in new product development is almost impossible in One-of-a-Kind Production (OKP) in which customer requirements vary from one to another. As such, modularisation of CAD assembly models is required to facilitate modular design for OKP. However, to the authors’ best knowledge, there has been no research carried out on modularisation of CAD assembly models so far. To bridge this gap and make the best use of existing CAD models, this paper proposes a novel module partition approach, to group existing CAD assembly models into modules based on component dependencies. In this approach, an extraction algorithm was developed to extract assembly information from a given assembly model directly, by using automated programmable interfaces of CAD software tools. The extracted information is processed to generate the component design structure matrix (DSM) representing hierarchical relations and dependency strengths between components. Four popular hierarchical clustering methods were used to work with the component DSM to produce results of module partition. A case study was carried out to illustrate the proposed methods and demonstrate their feasibility. It enables OKP companies to respond rapidly to changing customer requirements and develop customised products in a short period.     

A decomposition-based approach to flexible flow shop scheduling under machine breakdown

Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.     

RFID-based multi-attribute logistics information processing and anomaly mining in production logistics

Timely collecting logistics information and finding anomalies of material supply plays a critical role in modern manufacturing systems. The problem is how to obtain multi-attribute logistics information of production logistics and build an effective approach for mining anomalies from the huge number of RFID data. The multi-attribute, randomness and various measure units of logistics states further aggravate the problem. In this paper, a novel RFID-based logistics information processing approach is proposed. Firstly, the state features of production logistics is discussed from multi-attribute perspectives including time, location, quantities, sequence and path, and a set of calculating models is set up to process RFID data for getting multi-attribute state data. Furthermore, in case of the randomness and various measure units of state data, a similarity model is presented to unify measure units of state data, and a clustering approach is proposed to divide the huge number of RFID data into different clusters with high close degree for finding out anomalies. Lastly, the experimental results show that the proposed approach can efficiently find out more than 90% of anomalies among production logistics.     

Fuzzy C-Means Algorithm Automatically Determining Optimal Number of Clusters

In clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM algorithm and relevant algorithms automatically determining the optimal number of clusters, kernel function, AP algorithm and new evaluation indexes were applied to improve the confirmation of complexity and search the scope of traditional fuzzy C-means algorithm, and evaluation of clustering results. Besides, three groups of contrast experiments were designed with different datasets for verification. The results showed that the improved algorithm improves time efficiency and accuracy to certain degree.     

A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

Genetic-Frog-Leaping Algorithm for Text Document Clustering

In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of relevance. Nevertheless, as documents usually contain a large number of words, some of them may be irrelevant to the topic under consideration or redundant. This can confuse and complicate the clustering process and make it less accurate. Accordingly, feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features. In this study, we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features. The proposed approach is based on two metaheuristic algorithms: a genetic algorithm (GA) and a shuffled frog-leaping algorithm (SFLA). The GA performs feature selection, and the SFLA performs clustering. To evaluate its effectiveness, the proposed approach was tested on a well-known text document dataset: the “20Newsgroup” dataset from the University of California Irvine Machine Learning Repository. Overall, after multiple experiments were compared and analyzed, it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering, compared with classical K-means clustering. Nevertheless, this improvement requires longer computational time.     

Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model

Agent-based distributed simulation is an efficient methodology for modelling and analysing such complex adaptive systems as dynamic supply chain networks. However, it lacks an acceptable generic standard. Supply chain operations reference (SCOR) model is a cross-functional framework widely accepted as an industry standard. It provides the standard processes, performance metrics, best practices and associated software functionalities for modelling, evaluating and improving supply chain networks. However, it is a static tool. Integration of agent-based distributed simulation and SCOR model can exploit their advantages to form a generic methodology for modelling and simulation of a wide range of supply chain networks. Therefore, this paper proposes a methodology for distributed supply chain network modelling and simulation by means of integration of agent-based distributed simulation and an improved SCOR model. The methodology contains two components: a hierarchical framework for modelling supply chain network based on the improved SCOR model and agent building blocks integrating the standard processes from the SCOR model. The hierarchical framework provides an approach for structure modelling in any level with different granularities based on the improved SCOR model, and allows rapidly mapping a supply chain network into the structure model of a multi-agent system; while agent building blocks are quite useful and convenient to fill the structure model to fulfil its function modelling. With the approach of structure modelling and function filling, not only can the process of agent-based supply chain network modelling be accelerated, but also the built models can be reused and expanded. Because the hierarchical framework is based on the conceptual framework of SCOR model and agent building blocks integrate the standard processes from SCOR model, the proposed methodology is more generic. In addition, the issues of sub-model synchronisation and data distribution management in the agent-based distributed simulation implementation are taken into consideration and the corresponding solutions for these issues are proposed. Finally, an example of a supply chain network is modelled and implemented to illustrate the proposed methodology and related solutions.     

Automatic clustering for generalised cell formation using a hybrid particle swarm optimisation

This paper considers the cell formation (CF) problem in which parts have alternative process routings and the number of machine cells is not known a priori. Very few studies address these two practical issues at the same time. This paper proposes an automatic clustering approach based on a hybrid particle swarm optimisation (PSO) algorithm that can automatically evolve the number and cluster centres of machine cells for a generalised CF problem. In the proposed approach, a solution representation, comprising an integer number and a set of real numbers, is adopted to encode the number of cells and machine cluster centres, respectively. Besides, a discrete PSO algorithm is utilised to search for the number of machine cells, and a continuous PSO algorithm is employed to perform machine clustering. Effectiveness of the proposed approach has been demonstrated for test problems selected from the literature and those generated in this study. The experimental results indicate that the proposed approach is capable of solving the generalised machine CF problem without predetermination of the number of cells.