A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

A methodology to incorporate product mix variations in cellular manufacturing

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system and, consequently, a large number of concepts, theories and algorithms have been proposed. One common assumption for most of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers resulting in a highly volatile market. This has given rise to a new class of products characterized by low volume and high variety. To incorporate product mix changes into an existing cellular manufacturing system many important issues have to be tackled. In this paper, a methodology to incorporate new parts and machines into an existing cellular manufacturing system has been presented. The objective is to fit the new parts and machines into an existing cellular manufacturing system thereby increasing machine utilization and reducing investment in new equipment.     

A mathematical programming model for manufacturing cell formation to develop multiple configurations

This paper presents and analyses a mathematical model for the design of manufacturing cells which considers two conflicting objectives such as the heterogeneity of cells and the intercell moves. A genetic algorithm (GA) based solution methodology is developed for the model which is also solved using an optimization package. The model is suitable for getting multiple potential solutions in a structured way for the cell formation problem by making a trade-off between the two objectives, instead of reaching at a single negotiating solution. This model provides the decision maker the flexibility of choosing a suitable cell design from different alternatives by considering the practical constraints. A part assignment heuristic is also developed by which part-families can be identified and is integrated with the GA based solution procedure. A comparison of the proposed method is made with other seven methods using 36 problems from the literature. Grouping efficacy is the basis for comparison and it is found to give reasonably good results.     

A concurrent solution for intra-cell flow path layouts and I/O point locations of cells in a cellular manufacturing system

In this paper, we study the I/O (Input/Output) point location problem and the intra-cell flow path layout problem of cells in a cellular manufacturing system. Traditional approaches have often solved these two problems as separate problems, despite they are mutually affected. As a result, the results obtained by traditional approaches may not be as desirable as expected. In this study, we propose a layout procedure that can solve these two problems concurrently, so that the sum of the inter-cell flow distance and the intra-cell flow distance can be minimized. We assume cells have been arranged along a straight-line inter-cell flow path. Furthermore, the machines’ positions on each cell’s intra-cell flow path have been determined, but the intra-cell flow path of each cell has not been placed on the shop floor yet. We also assume the configuration of intra-cell flow paths is serpentine. The proposed layout procedure classifies the flow distance incurred by inter-cell flow into five types and minimizes them with different solution procedures containing various linear programming models. The proposed layout procedure has four phases. At the first phase, we find each cell’s input and output points on the inter-cell flow path by considering only the inter-cell flow distance. At the second phase, we find the input and outpoint points on each cell’s intra-cell flow path by considering only the intra-cell flow distance. At the third phase, we use the points found in the previous two phases as references to help us correctly orient and arrange each cell’s intra-cell flow path on the shop floor. Finally, at the fourth phase, we find the accurate the input points and output points on each cell’s intra-cell flow path and the inter-cell flow path by simultaneously considering the inter-cell and the intra-cell flow distance. We use an example to illustrate the proposed layout procedure. The results of the example show that the proposed layout procedure can effectively find each cell’s I/O point locations and intra-cell flow path layout by considering both intra-cell and inter-cell flow distance at the same time.     

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm.     

A 5-component mathematical model for salt-induced hypertension in Dahl-S and Dahl-R rats

Salt-induced hypertension has been demonstrated in a variety of species including rats, monkeys, chimpanzees and humans. Until recently, the multiple phases of this blood pressure increase due to high salt intake had not been closely studied. This work builds upon a recent study, which developed a grey-box multi-component model of salt-induced hypertension in the Dahl-S rat. The previous 3-component model has been extended here to include additional model dynamics to improve the model fit and add new important elements to the model response. The model was optimised using numerical techniques with experimental data from 4 different protocols with Dahl-S, Dahl-R and FF2 hybrid rats. Results show a marked improvement over the previous model and confirm the merit of the 5-component model structure. A comparison between the model dynamics for different rat strains has also been included.     

A mathematical model and a genetic algorithm for two-sided assembly line balancing

A two-sided assembly line is a type of production line where tasks are performed in parallel at both sides of the line. The line is often found in producing large products such as trucks and buses. This paper presents a mathematical model and a genetic algorithm (GA) for two-sided assembly line balancing (two-ALB). The mathematical model can be used as a foundation for further practical development in the design of two-sided assembly lines. In the GA, we adopt the strategy of localized evolution and steady-state reproduction to promote population diversity and search efficiency. When designing the GA components, including encoding and decoding schemes, procedures of forming the initial population, and genetic operators, we take account of the features specific to two-ALB. Through computational experiments, the performance of the proposed GA is compared with that of a heuristic and an existing GA with various problem instances. The experimental results show that the proposed GA outperforms the heuristic and the compared GA.     

Improving problem solving ability in mathematics by using a mathematical model: A computerized approach

The present study investigates the improvement of students’ mathematical performance by using a mathematical model through a computerized approach. We had developed an intervention program and 11 years students worked independently on a mathematical model in order to improve their self-representation in mathematics, to self-regulate their performance and consequently to improve their problem solving ability. The emphasis of using the specific model was on dividing the problem solving procedure into stages, the concentration on the students’ cognitive processes at each stage and the self-regulation of those cognitive processes in order to overcome cognitive obstacles. The use of the computer offered the opportunity to give students general comments, hints and feedback without the involvement of their teachers. Students had to communicate with a cartoon animation presenting a human being who faced difficulties and cognitive obstacles during problem solving procedure. Three tools were constructed for pre- and post-test (self-representation, mathematical performance and self-regulation). There were administered to 255 students (11 years old), who constituted the experimental and the control group. Results confirmed that providing students with the opportunity to self-reflect on their learning behavior when they encounter obstacles in problem solving is one possible way to enhance students’ self-regulation and consequently their mathematical performance.     

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

A decentralized model for flow shop production with flexible transportation system

The recent advances in technology sectors often clash with traditional organizational paradigms which can limit or make difficult an efficient implementation in the real world. In this paper we show how it is possible to exploit the advantages of innovative technologies in manufacturing when these are supported by new and efficient methods for production management. More in details, we face a flow shop scheduling problem in a shoe manufacturing system in which overtaking of jobs is allowed thanks to an innovative transportation system. Overtaking means that a job can be put in waiting state and another job can surpass it, allowing the change of the scheduling sequence. Preemption is not allowed. The objective function of the problem is the minimization of the maximum lateness. We propose a decentralized model, based on multi-agent system theory, to represent the production cells of the plant and to include the potentiality offered by overtaking of jobs at decisional level. The adoption of a decentralized approach increases the system flexibility since each machine is able to solve its local scheduling problem. Adding or removing machines to the plant will not imply a change in the scheduling algorithms. The outcomes of this work are reached firstly through a formulation of the problem with three flow shop scheduling models, secondly through a comparison of the models with respect to different performance indicators. The results highlight as the decentralized approach is able to reach comparable performances with the centralized one for a relevant number of instances. Moreover sensitivity analysis shows as in the decentralized model the computational time required to solve bigger instances increases less quickly than in the case of centralized ones. Finally, simulations of the decentralized approach clarify as the correlation of the local solution procedure is effected by the number of machines of the flow shop and the coordination mechanism is effected by the number of the jobs to be scheduled.     

A new delay-dependent stability criterion for linear neutral systems with norm-bounded uncertainties in all system matrices

This paper deals with the problem of robust stability for a class of uncertain linear neutral systems. The uncertainties under consideration are of norm-bounded type and appear in all system matrices. A new delay-dependent stability criterion is obtained and formulated in the form of linear matrix inequalities (LMIs). Neither model transformation nor bounding technique for cross terms is involved through derivation of the stability criterion. Numerical examples show that the results obtained in this paper significantly improve the estimate of the stability limit over some existing results in the literature.     

RMS中的基于时间的设备选择方法研究

根据生产需求快速确定制造资源是可重组制造系统高效快速响应市场变化的关键功能之一。文中提出了以系统整体加工时间消耗最少为目标的时间虚负荷矩阵算法,这种算法通过矩阵进行运算,计算简单直观,在计算过程中同时实现设备优化选择和工序分配,这种算法还可应用于车间作业调度。     

A novel model for a manufacturing system with joint production lines in terms of prior-set

A novel model integrating transformation and decomposition techniques is proposed to construct a manufacturing system with joint production lines as a multi-state manufacturing network (MMN). Reworking actions and different defect rates of workstations are both taken into account in the MMN model. The capacity analysis and performance evaluation are implemented accordingly. In particular, a technique in terms of ‘prior-set’ is developed to deal with multiple reworking actions. Subsequently, two simple algorithms are proposed to generate all minimal capacity vectors that workstations should provide to satisfy a given demand. In terms of such vectors, the probability of demand satisfaction can be derived. Such a probability is referred to as the system reliability, which is a performance indicator to state the capability of the MMN. According to each specific minimal capacity vector, the production manager may further determine a better strategy to produce products.     

A standard manufacturing information model to support design for manufacturing in virtual enterprises

Manufacturing enterprises are being forced into greater collaboration with customers and suppliers in order to produce quality products in smaller batches, shorter lead times and with greater variety. Consequently, the design-for-manufacturing task must be conducted in these virtual and distributed enterprises across traditional organizational boundaries. This paper proposes the use of standard information models to support the product realization process. While extensive work has been performed in developing product data models little effort has been performed in developing a manufacturing model. The design-for-manufacturing stages are identified with their requisite information requirements. Different approaches used to model various aspects of manufacturing processes are reviewed and found inadequate for supporting the entire design-for-manufacturing task. The development of a standard manufacturing systems information model written in EXPRESS and based upon the modelling me thodology adhered to by standard for the exchange of product (STEP) is proposed to fill the void. Initial developments in this area are discussed, the model is illustrated with an example, and the potential benefits to manufacturing are reviewed.     

A virtual manufacturing system for components made of a multiphase perfect material

A component, which has an optimized combination of different materials (including homogeneous materials and different types of heterogeneous materials) in its different portions for a specific application, is considered as being made of a multiphase perfect material. To manufacture such components, a hybrid layered manufacturing technology has been proposed. Since it would be risky and expensive to make such a physical machine, virtual manufacturing technology is adopted to further study and optimize the hybrid layered manufacturing technology, so as to provide a reliable foundation for future practical manufacturing with a much better prospect of success, a shorter lead time, and a much lower investment cost. This paper develops a virtual manufacturing system for it.     

A decision support system for personnel scheduling in a manufacturing environment

A Decision Support System (DSS) is developed to support managers in the task of scheduling labor in the area of manufacturing. The DSS is designed to generate labor requirements by worker category and work center based on master production schedules. It is a PC-based, menu-driven program that generates a capacity plan based on data supplied by the user of the system.     

An integrated model for solving cell formation and cell layout problem simultaneously considering new situations

Cellular manufacturing system (CMS) is one of the group technology (GT) usages. Among the necessary decisions for a successful CMS implementation, cell formation problem (CFP) and cell layout problem (CLP) are two most popular ones. The majority of past studies in CMS discussed on CFPs and some of those focused on CLP ones. A few researchers solve the CPF and CLP simultaneously. In this paper, we present a new integrated mathematical model considering cell formation and cell layout simultaneously. The goal of our model is to group similar parts and corresponding different machines in same cells. Machines sequence in each cell and cell positions is also specified in the system. Moreover, our proposed model considers forward and backtracking movements as well as new assumptions for distances between cells using sequence data and production volume. One appropriate adjusted measure from the literature and two new measures of performance for evaluating solutions are defined. To validate the model, two well-known critical benchmark examples are employed. Computational experiments demonstrate that our proposal is a proficient model and show the effectiveness of our implementation.     

A knowledge-based expert system for technology acquisition in small and medium scale manufacturing organizations

Consistent and reliable decision making for technology acquisition in small and medium scale manufacturing organizations is vitally important since these firms are the backbone of national economies, both in developed and developing countries. Because of their flexibility, small and medium scale firms are successful in adopting new technologies. However, a careful analysis should be conducted in technology acquisition decisions. Since these decisions require special type of knowledge and expertise, expert system, as an important tool of computerized decision making, can overcome these multidimensional difficulties.

This paper proposes a knowledge-based approach making use of issues such as sales, processes, costs and general policies, in decision processes for technology acquisition by small and medium scale manufacturing organizations in the developing environments.