A granularity model for balancing the structural complexity of manufacturing systems equipment and layout

The structural complexity of a manufacturing system results primarily from the complexity of its equipment and their layout. The balance between both complexity sources can be achieved by searching for the best system granularity level, which yields a manufacturing system with the least overall structural complexity. A new system granularity complexity index is developed to sum up and normalize the complexity resulting from the system layout complexity and the equipment structural complexity. A previously developed layout complexity index together with a code-based structural complexity assessment are used to determine the structural complexity of standalone pieces of equipment and to arrive at a balance between the two sources of complexity. Cladistics analysis is used to hierarchically cluster required pieces of equipment and bundle them into more integrated equipment and machines and demonstrate the possible different system granularity levels. The new developed model is a useful tool to create specific system configuration and layout alternatives based on system components adjacency, and then select the system design with the least overall structural complexity among those alternatives. The results of the presented case study clearly demonstrated this trade-off where decomposing manufacturing systems into a highly granular configuration with standalone machines maximizes system layout complexity and minimizes equipment complexity, while at a low level of granularity pieces of equipment are bundled into complex integrated machines, lines or cells but with a very simple system layout.     

Intelligent design of shear wall layout based on graph neural networks

Structural scheme design of shear wall structures is important because it is the first stage that guides the project along its entire structural design process and significantly impacts the subsequent design stages. Design methods for shear wall layouts based on deep generative algorithms have been proposed and achieved some success. However, current generative algorithms rely on pixel images to design shear wall layouts, which have many model parameters and require intensive calculations. Moreover, it is challenging to use pixel image-based methods to reflect the topological characteristics of structures and connect them with the subsequent design stages. The above defects can be effectively solved by representing a shear wall structure in graph data form and adopting graph neural networks (GNNs), which have a robust topological-characteristic-extraction capability. However, there is no existing research using GNN methods in the design of shear wall structures owing to the lack of graph representation methods and high-quality structural graph data for shear walls. Therefore, this study develops an intelligent design method for shear wall layouts based on GNNs. Two graph representation methods for a shear wall structure—graph edge representation and graph node representation—are examined. A data augmentation method for shear wall structures in graph data form is established to enhance the universality of the GNN performance. An evaluation method for both graph representation methods is developed. Case studies show that the shear wall layout designed using the established GNN method is highly similar to the design by experienced engineers.     

面向结构化布局设计的产品布局多色模型

为实现产品原理方案向结构化布局映射,提出产品布局元层次关系网及产品布局多色模型.首先在布局模块及其属性关系网的基础上构建布局元层次关系网;然后应用多色集合的个人颜色、统一颜色,给出产品布局元层次关系网的数学描述,形成产品布局多色模型,并提出推理算法;再利用个人颜色、统一颜色之间的围道布尔矩阵推理原理,进行产品结构布局设计过程中功能—运动分配—布局模块之间映射过程的形式化描述.应用该模型进行结构化布局设计,可有效地支持产品结构布局设计的公理化;最后在Pro/E平台中利用VC.net 2003工具进行二次开发,并以数控机床结构布局设计为例验证了文中模型和设计方法的可行性和有效性.     

Agility and production flow layouts: An analytical decision analysis

The term ‘agile manufacturing’ has referred to operational aspects of a manufacturing company concerning their ability to produce customized products at mass production prices and with short lead times. A core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems. Many design dimensions of agility and agile manufacturing exist. To help attain this goal for integrating the many design dimensions, operations infrastructure and capacity must be carefully planned to manage production flow, and thus production layout planning takes on an increasingly important role. Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products. Layout design has a significant impact on the performance of a manufacturing or service industry system and has been an active research area for many decades. Strategic evaluation of production layouts requires consideration of both qualitative and quantitative factors (managerial, organizational, and technical). This paper makes use of the Analytical Network Process (ANP) which captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. An application case study exemplifying the practical usefulness of this type of model describes how management, after implementation of the model, made a mid-course correction related to the production layout initially selected.     

A web-based expert system to assess the complexity of manufacturing organizations

Information-theoretic modelling of manufacturing organizations and their supply chains has led to the development of measures of manufacturing complexity. The measures include assessment of the structural, dynamic and decision-making complexity associated with the processing and movement of material and information around a manufacturing system. A computer program has been written to calculate the decision-making complexity of a manufacturing system, under different system layouts and operating characteristics. In order to make the results of this program accessible to manufacturing organizations, an expert system has been developed to act as a mediator between the program and interested organizations. Given some simple quantitative data on manufacturing performance, the expert system can estimate the organization's complexity and suggest some recommendations to reduce it, based on the data provided by the organization. The expert system will be implemented on the web to enable on-line acquisition and searching of data on companies. The quid pro quo of the expert system is that anonymized data on the organizations will be retained so that complexity benchmarks may be established.     

A support-design framework for Cooperative Robots systems in labor-intensive manufacturing processes

Manufacturing processes and industrial systems gradually change their traditional layouts and configurations, preparing to introduce novel integrated human-robot technologies as collaborative robots and exoskeletons. Whether mass customization of lot size and the production mix discourages the adoption of capital-intensive automation, collaborative robots become affordable and effective and a hotspot of the debate on manufacturing systems. This paper provides a novel support-design framework for the cooperative robot system in labor-intensive manufacturing processes to aid layout and task scheduling design. Through an iterative closed-loop methodology, this framework explores the impact of a cooperative robot in a labour-intensive manufacturing system like the production facility of a food service company. The framework leads the designer through the re-layout of the end-of-line, the economic and technical feasibility analyses, using simulation to estimate payback and ergonomics benefits for workers. Within the proposed layout, we state that adopting a cooperative cobot for the end-of-line is affordable and ergonomically convenient without representing a safety threat for workers. The testbed confirms the framework as an enabling tool for human-robot technologies integration in current manufacturing systems under budget and workers-driven constraints.     

A data-driven generic simulation model for logistics-embedded assembly manufacturing lines

Simulation has been used to evaluate various aspects of manufacturing systems. However, building a simulation model of a manufacturing system is time-consuming and error-prone because of the complexity of the systems. This paper introduces a generic simulation modeling framework to reduce the simulation model build time. The framework consists of layout modeling software and a data-driven generic simulation model. The generic simulation model was developed considering the processing as well as the logistics aspects of assembly manufacturing systems. The framework can be used to quickly develop an integrated simulation model of the production schedule, operation processes and logistics of a system. The framework was validated by developing simulation models of cellular and conveyor manufacturing systems.     

Simulation of automated guided vehicle (AGV) systems based on just-in-time (JIT) philosophy in a job-shop environment

Automated Guided Vehicle (AGV) systems have been frequently used as material handling equipment in manufacturing systems since the last two decades. The use of AGV systems has taken attention of practitioners and researchers. Although there are numerous studies concerning with AGV systems in literature, a few of them deal with the adaptation of these systems into JIT systems. Moreover, the facility layouts considered in those studies have flow-shop environment. In this paper, a simulation model of a hypothetical system which has a job shop environment and which is based on JIT philosophy was developed. In addition, a dispatching algorithm for vehicles moving through stations was presented in order to improve transportation efficiency. In given layout, multiple vehicles can move and their bi-directional flow is allowed. After the model had been established, it was mentioned how to perform simulation output analysis. The factors which may be important for the system were determined by output analysis. An experiment plan was prepared by taking into account these factors. In this plan, two levels were selected for each factor and an experimental design was conducted. The effect of each factor on each performance measure and the interaction of these factors were examined.     

基于RMC的可重构制造系统设备布局优化研究*

为实现多品种变批量生产制造系统阵列式布局的动态重构,提出一种新的设备布局优化方法.建立了可重构制造系统(RMS)设备优化选择数学模型,设计了基于蚁群优化和阶序聚类算法的可重构制造单元(RMC)动态重构算法.以交货期内最小成本为目标,引入系统复杂度和系统响应度从系统能力角度完善了实现RMS重构的约束条件,最终完成了可重构制造系统设备布局优化.最后通过布局实例仿真验证该方法的可行性和有效性.     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

A mathematical model of higher level structural optimization problems and their solution

In this paper, a new mathematical model suitable for higher level structural optimization problems, such as optimization of structural topology, layout and type is presented. In this mathematical model, the relation between two structures with different layouts is established by introducing the nonbasic variables. Using the Kuhn-Tucker condition for optimality, a criterion for determining a better layout of a structure is developed. This provides a measure for selecting the optimal layout of a structure. The method introduces a new way for higher level structural optimization design. Several numerical examples are given to illustrate the effectiveness of this method.     

Optimizing layout using spatial quality metrics and user preferences

Computational design is one of the most common tasks of immersive computer graphics projects, such as games, virtual reality and special effects. Layout planning is a challenging phase of architectural design, which requires optimization across several conflicting criteria. We present an interactive layout solver that assists designers in layout planning by recommending personalized space arrangements based on architectural guidelines and user preferences. Initialized by the designer’s high-level requirements, an interactive evolutionary algorithm is used to converge on an ideal layout by exploring the space of potential solutions. The major contributions of our proposed approach are addressing subjective aspects of the design to generate personalized layouts; and the development of a genetic algorithm with a multi-parental recombination method that improves the chance of generating higher quality offspring. We demonstrate the ability of our method to generate feasible floor plans which are satisfactory, based on spatial quality metrics and designer’s taste. The results show that the presented framework can measurably decrease planning complexity by producing layouts which exhibit characteristics of human-made design.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

A process knowledge representation approach for decision support in design of complex engineered systems

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.     

Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs

In this paper, a data-driven generative method is applied to generate synthetic space allocation probability layout. This generated layout could be helpful in the early stage of an architectural design. For this task, a specific training dataset is generated which is used to train the cGAN model. The training dataset consists of 300 existing apartment layouts which are coloured in a set of low feature representation. The cGAN model is trained with this dataset and the trained model is evaluated based on the quality of its generated layouts regarding the five pre-defined topological and geometrical benchmarks.     

Multiobjective layout optimization of robotic cellular manufacturing systems

This paper proposes a multiobjective layout optimization method for the conceptual design of robot cellular manufacturing systems. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. In this paper, the design criteria for robot cellular manufacturing system layout designs are clarified, and objective functions are formulated. Next, layout design candidates are represented using a sequence-pair scheme to avoid interference between assembly system components, and the use of dummy components is proposed to represent layout areas where components are sparse. A multiobjective genetic algorithm is then used to obtain Pareto optimal solutions for the layout optimization problems. Finally, several numerical examples are provided to illustrate the effectiveness and usefulness of the proposed method.     

Applying simulated annealing for designing cellular manufacturing systems using MDmTSP

The design of cellular manufacturing systems involves many structural and operational issues. One of the important design steps is the formation of part families and machine cells (cell formation). Despite a large number of papers on cell formation published worldwide, only a handful incorporates operation sequence in layout design (intra-cell move calculations). We propose a solution to solve the part-family and machine-cell formation problem considering the within-cell layout problem, simultaneously. In this paper, the cellular manufacturing system is formulated as a multiple departures single destination multiple travelling salesman problem (MDmTSP) and a solution methodology based on simulated annealing is proposed to solve the formulated model. Numerical examples show that the proposed method is efficient and effective in finding optimal solutions. The results also indicate that the proposed approach performs well compared to some well-known cell formation methods.     

A six sigma based multi-objective optimization for machine grouping control in flexible cellular manufacturing systems with guide-path flexibility

Effective design of automatic material handling devices is one of the most important decisions in flexible cellular manufacturing systems. Minimization of material handling operations could lead to optimization of overall operational costs. An automated guided vehicle (AGV) is a driverless vehicle used for the transportation of materials within a production plant partitioned into cells. The tandem layout is according to dividing workstations to some non-overlapping closed zones to which a tandem automated guided vehicle (TAGV) is allocated for internal transfers. This paper illustrates a non-linear multi-objective problem for minimizing the material flow intra and inter-loops and minimization of maximum amount of inter cell flow, considering the limitation of TAGV work-loading. For reducing variability of material flow and establishing balanced zone layout, some new constraints have been added to the problem based on six sigma approach. Due to the complexity of the machine grouping control problem, a modified ant colony optimization algorithm is used for solving this model. Finally, to validate the efficacy of the proposed model, numerical illustrations have been worked out.     

A simulation study comparing GT vs. job shop manufacturing systems

Two discrete simulation models were developed, one representing a typical job shop (JS) manufacturing system (process layout) and the other representing group technology (GT) manufacturing system (cellular layout). Hypothetical data generated by FORTRAN program and MICRO-CRAFT layout package were used to validate the two models. Simulations were performed using SLAM II. The two layouts were compared with one another under controlled experimental conditions using various combinations of four operating variables: batch size, demand rate, ratio of setup time to process time, and transporter speed. Six indicators of systems performance were used in the study. These were: average time in system, products completed, jobs in process, average machine utilization, average queue length, and average waiting time.

The study found that for environment as set for the models, GT system outperformed JS system as expected. However, using large batch size (more than 75) in Experiment 1 (changing batch size) allowed JS system to perform as well as GT system. Certain specific changes in batch size also allowed to establish optimum results in both systems. No individual change of demand rate (Experiment 2), ratio of setup time to process time (Experiment 3), or transporter speed (Experiment 4) allowed JS system to outperform GT system.     

基于Virtools的墙材生产线可视化设计系统开发

布局问题一直是生产线设计过程中的一个难点问题,生产线布局的优劣直接影响到生产线的生产效率。针对墙材生产企业生产线布局中存在的问题,研究生产线的快速动态布局方法。利用当前虚拟仿真技术的优势,基于虚拟仿真软件Virtools,开发墙材生产线可视化布局设计系统。并且结合某墙材生产企业生产线的布局问题,应用所开发的布局设计系统进行仿真布局,在此基础上利用布局设计系统的生产过程仿真功能对此生产线的生产过程进行仿真模拟,检验生产线布局的合理性,以便修改和优化。