Machine assignment and part-families formation using group technology

The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples.     

Cell formation in group technology: A new approach

In the U.S.A., machine-component cluster formation is considered a poor alternative to classification and coding as a planning tool for Cellular Manufacture. This paper introduces a heuristic procedure, the Occupancy Value method, for identifying clusters in a machine-component matrix created from route card data. A unique feature of this method is that it progressively develops block diagonalization starting from the northwest corner of the matrix. The flexibility of the procedure is illustrated in detail through a small example. Another large matrix is analyzed to demonstrate the inherent simplicity of the method. Further extensions of this method to implement the manufacturing cells from these initial clusters are discussed.     

Cell formation in group technology using constraint programming and Boolean satisfiability

Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime.     

Cell formation in group technology: Review,evaluation and directions for future research

This paper discusses and reviews a fundamental issue in cellular manufacturing—cell formation. This problem is of strategic and operational importance in that it affects the fundamental structure and the overall layout of a cellular manufacturing system. We first provide a comprehensive mathematical formulation of the cell formation problem and then propose a methodology-based classification of prior research. This classification is used in reviewing the most recent literature on the cell formation problem. Based on a comparison and critical evaluation, we highlight the shortcomings of current approaches and also outline directions for future research.     

基于ACJT的广义群签密方案

群签密是一种能够同时提供群签名和群加密的密码体制。首次将群签密的应用范围推广到仅需要完整性的场合,给出了广义群签密的定义。广义群签密既有群签密的属性,又有对特定的输入单独提供群签名的功能。基于ACJT群签名方案,提出了一个新的群签密方案SC-ACJT。在没有特定的接收方时方案等价于ACJT群签名。相对于现有的群签密方案,SC-ACJT解决了动态成员管理的问题,计算量不依赖于成员的数目。在群签密模式下,SC-ACJT比Kwak-Moon（Ⅱ）少了计算群共享密钥的计算量;在群签名模式下,SC-ACJT比现有方案节省一个附加的群签名算法。     

Naked singularity formation in generalized Vaidya space-time

The gravitational collapse in generalized Vaidya space-time is considered. It is known that the end state of gravitational collapse, as to whether a black hole or a naked singularity is formed, depends on the mass function M(v, r). Here we give conditions for the mass function which correspond to the equation of the state P = αρ. where α ∈ (0, 1/3], and according to these conditions we obtain either a black hole or a naked singularity as the end state of gravitational collapse. We also give the conditions for the mass function under which the singularity is gravitationally strong. We present simple examples showing when the result of gravitational collapse is a naked singularity and when this singularity is strong.     

Team formation based on group technology: A hybrid grouping genetic algorithm approach

This paper presents a new model for team formation based on group technology (TFPGT). Specifically, the model is applied as a generalization of the well-known Machine-Part Cell Formation problem, which has become a classical problem in manufacturing in the last few years. In this case, the model presented is especially well-suited for problems of team formation arising in R&D-oriented or teaching institutions. A parallel hybrid grouping genetic algorithm (HGGA) is also proposed in the paper to solve the TFPGT. The performance of the algorithm is shown in several synthetic TFPGT instances, and in a real problem: the formation of teaching groups at the Department of Signal Theory and Communications of the Universidad de Alcalá in Spain.     

Group technology cell formation — Some new insights

A new technique for designing manufacturing cells based on the hospitality and flexibility of jobs are used. Host cells are formed and further reduction in the number of cells are achieved using the integer programming technique. An example problem has been solved using this approach. Cost data are included in the example problem for evaluating the different designs.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Applications of group technology in distributed manufacturing

This paper discusses the systematic use of group technology (GT) to support important activities of distributed manufacturing including design indexing and retrieval, variant design, variant process planning, and design critiquing. We introduce an Object-Oriented Group Technology (OOGT) scheme, which captures design data critical to the product's manufacture. Our approach uses a STEP-based product information model to generate the OOGT data automatically. The latter are then used to conduct an efficient search for similar products manufactured by selected companies and to retrieve and use information about the manufacturing processes and performance of these similar products.     

Machine cell formation considering processing times and machine capacities: An ortho-synapse Hopfield neural network approach

The machine/part grouping problems can be classified into binary and comprehensive grouping problems depending on whether or not the processing times and the machine capacities are considered. The binary grouping problem arises if the part demands are unknown when the CMS is being developed. If the part demand can be forecast accurately, both the processing times and machine capacities have to be included in the analysis. This gives rise to comprehensive grouping. Both the binary and comprehensive grouping have been proven to be NP-complete which cannot be solved in polynomial time. Considering the large number of parts and machines involved in the industrial design problem, efficient solution methods are highly desirable. In this paper, a new neural network approach (OSHN_g) is proposed to solve the comprehensive grouping problems. The proposed approach has been tested on 28 test problems. The results show that the OSHN_g method is very efficient and its solution quality is comparable to that of a simulated annealing approach.     

Least cost estimating with group technology

A twenty-two digit alpha-numeric Group Technology System was established for the Aerospace Group of VSI Corporation. In addition, the planning for least cost processing planning generation has been completed using the Group Technology (G.T.) System. The G. T. System is also being used for the classical applications such as tooling, reduced set ups, process plans, identifying duplicate parts, etc., in addition to the planned least cost method. The least cost method uses a unique empirical production standards system in conjunction with the G. T. Code for its implementation.     

Direct clustering algorithm for group formation in cellular manufacture

This paper describes a new algorithm which has been developed for forming component families and machine groups for cellular manufacture by progressively restructuring the machine component matrix. The method allows interaction from the user when exceptions and overlap between groups cause the iterative algorithm to prematurely stop. This is an effective and efficient approach which offers great flexibility in determining the optimum families and groups. The algorithm is specifically designed for computer use in dealing with large amounts of data obtainable in realistic situations, although the procedures are simple, and can be carried out manually for the analysis of small machine component matrices.     

Social group formation with mobile cloud services

Social networks have become quite popular these days, and the creation of social groups of people with common interests results in sharing and collaborative relationships between the members. Besides, cloud computing and mobile computing domains have advanced rapidly and are the promising technologies for the near future. Mobile technologies are recently drawing their attention to the cloud computing due to the increasing demand of the applications for processing power, storage space and energy. To bring the benefits from these different developments together, we tried to build an Android application, CroudSTag, which aids in forming social groups of common interest, from the mobile devices. The application obtains a set of pictures/videos from a storage cloud, uses cloud services like the video processing and the face recognition from multiple cloud providers to identify the people from the media content, and aids in forming social groups on facebook, a well-known social network. The application is explained along with detailed architectural and technological choices. The performance analysis of the application shows that the social groups can be formed with significant ease and reasonable performance latencies from the mobile devices.     

可变形网格引导的群体队形仿真

目的 许多群体运动仿真算法侧重于模拟由大量自由移动个体所组成的群体行为,而针对具有特定队形的群体运动仿真算法较少。为解决这一问题,采用改进的网格引导方法,利用可变形网格对群体运动进行控制。方法 首先,对群体队形进行三角划分,建立一个连接所有智能体的队形网格。然后,利用障碍势场法在群体运动的过程中对队形网格进行变形,使智能体在避免与障碍物发生穿透的同时尽可能保持整体队形稳定。最后,针对障碍物穿过队形网格时可能造成的局部智能体 “错位现象”,提出了基于吸引点的网格引导方法,使群体绕过障碍物后能迅速恢复原来队形。结果 使用Unity软件对军队行进、动态车流、群体表演等场景中的智能体编队移动进行仿真,并设置了不同规模的群组交换实验进行对比。本文算法的实时计算花销主要集中在网格变形阶段：在队形网格顶点总数为1 000时,单位仿真步内网格变形阶段的平均运行时间为20.15 ms。队形网格划分阶段是一个预先进行的过程,不影响算法实时性。基于吸引点的网格引导方法提高了智能体的全局移动效率,使队形变化更加自然流畅。结论 实验结果表明,可变形网格引导的群体队形仿真算法在群体运动过程中能有效维持队形稳定,无论障碍环境是静态的还是动态的都能实现良好的群体避障,说明算法的有效性。     

Machine cell/part family formation considering processing times and machine capacities: a simulated annealing approach

Binary machine-part matrices have been widely used to identify machine groups and part families. The methods based on binary machine-part matrices mostly focus on the reduction of setup times and material handling costs. However, some other objectives such as the maximization of within-cell utilization and minimization of workload imbalance may not be achieved without considering other important factors such as processing times, lot sizes and machine capacities. Ignoring the processing times may violate the capacity constraints, and thus lead to an infeasible solution. This paper proposes a generalized grouping efficiency considering processing times and lot sizes. A simulated annealing algorithm is developed to solve the grouping problem and a neural network approach is used to provide a seed solution. Our computational experience indicates that the proposed algorithm is able to find a near optimum solution with less number of duplicated machines and better workload balance as compared to the approach reported in the literature.     

Impacts of new energy technology using generalized input-output analysis

The economic and environmental impacts of several new energy technologies (high and low BTU coal gasification and the gas turbine topping cycle) were examined for the 1980–1985 time period. A projected 1980–1985 U.S. input-output matrix was augmented to include environmental and resource usage variables. Engineering studies were used to modify the input-output matrix to represent the introduction of new energy technologies. The results illustrate the high sensitivity of capital investment to the rate of growth of energy consumption. The results also illustrate several economic mechanisms that will help to hold total capital investment within its historical bounds as a percentage of GNP. The methodology and many possible applications and extensions of generalized input-output model are included. A short critique of the methodology is also presented.     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.