A Human‐Oriented Simulation Approach for Labor Assignment Flexibility in Changeover Processes of Manufacturing Cells

Skilled operators are the most decisive key factors in manufacturing cells. An optimal assignment of operators is crucial for flexibility and productivity. Although there are many publications dealing with labor assignment problems, different forms of human cooperation on the shop floor and decentralized decision making, which are the main factors for system flexibility, are seldom concerned in existing models. In this article, a human‐oriented methodology to analyze, simulate, and evaluate labor assignment flexibility in changeover processes in manufacturing cells is introduced, which is characterized by an agent‐based approach. First, the problem architecture is presented along with the concepts of labor flexibility. Then, different types of human behavior in the changeover process are modeled. Furthermore, a human–machine interaction model is developed to integrate the human agent models into a generalized discrete event dynamic system (DEDS) process model. In this way, work process dynamics and cooperative behavior can be explicitly modeled and simulated. Third, the model is verified on the basis of a motorcycle engine manufacturing cell, and simulation experiments with different labor assignment schemes are designed and conducted. The simulation results show that assignment strategies incorporating different skill levels and cooperation styles have a significant impact on system performance. The agent‐based approach in conjunction with the human–machine interaction model can be used to analyze and solve a large class of assignment problems in flexible manufacturing systems, especially when human cooperation and collaboration are key factors shaping overall system performance.     

Crowd simulation for emergency response using BDI agents based on immersive virtual reality

This paper presents a novel methodology involving a Virtual Reality (VR)-based Belief, Desire, and Intention (BDI) software agent to construct crowd simulation and demonstrates the use of the same for crowd evacuation management under terrorist bomb attacks in public areas. The proposed BDI agent framework allows modeling of human behavior with a high degree of fidelity. The realistic attributes that govern the BDI characteristics of the agent are reverse-engineered by conducting human-in-the-loop experiments in the VR-based Cave Automatic Virtual Environment (CAVE). To enhance generality and interoperability of the proposed crowd simulation modeling scheme, input data models have been developed to define environment attributes (e.g., maps, demographics, evacuation management parameters). The validity of the proposed data models are tested with two different evacuation scenarios. Finally, experiments are conducted to demonstrate the effect of various crowd evacuation management parameters on the key performance indicators in the evacuation scenario such as crowd evacuation rate and densities. The results reveal that constructed simulation can be used as an effective emergency management tool.     

Genetic learning of virtual team member preferences

Virtual team members do not have complete understanding of other team members’ preferences, which makes team coordination somewhat difficult and time consuming. Traditional approaches for team coordination require a lot of inter-agent electronic communication and often result in wasted effort. Methods that reduce inter-agent communication and conflicts are likely to increase productivity of virtual teams. In this research, we propose an evolutionary genetic algorithm (GA) based intelligent agent that learns a team member preferences from past actions, and develops a team-coordination schedule by minimizing schedule conflicts between different members serving on a virtual team. Using a discrete event simulation methodology, we test the proposed intelligent agent on different virtual teams of sizes two, four, six and eight members. The results of our experiments indicate that the GA-based intelligent agent learns individual team member preferences and generates a team-coordination schedule at a lower inter-agent communication cost.     

Behavioral,data-driven,agent-based evacuation simulation for building safety design using machine learning and discrete choice models

To improve occupant safety during building emergencies, evacuation simulations have been widely used for building safety design. Since occupant behavior is a determining factor for the outcome of building emergencies, accurately capturing how occupants make decisions and integrating occupants’ decision-making processes in evacuation simulations is important. In this study, based on the results of fire evacuation experiments in a virtual metro station, how different social (crowd flow) and environmental (visual access and vertical movement) factors would affect individuals’ wayfinding behavior was predicted using machine learning and discrete choice models. The trained models were further employed in agent-based evacuation simulations to examine crowd evacuation performance under different building design scenarios. Both the machine learning and discrete choice models could accurately predict individuals’ directional choices during emergency evacuations. Different building attributes could collectively influence occupant behavior, leading to distinct exit choices and evacuation times. While both the trained machine learning and discrete choice models generated similar results, the discrete choice model had better interpretability. Moreover, by comparing the trained models in this study with a model developed in a prior study, it was found that agents had significantly distinct responses to different building designs. Critical factors (e.g., type and size of buildings, occupants’ familiarity with the building) for the applicability of evacuation models were identified. Furthermore, recommendations were provided for future research that aims at employing evacuation simulations for building design evaluation and optimization.     

遗传算法与行人疏散模型结合的建筑出口优化

构建基于遗传算法与行人运动仿真模型的双层优化框架，选取单室双出口场景，考虑两个出口在同一侧、相邻侧、相对侧3种典型布局，设定3种人员身形，3种疏散初始人群密度，9种疏散出口大小条件，形成243个数值实验场景，分别计算获得最有利于人员疏散的出口布局。研究发现疏散出口宽度与疏散效率呈正向关系，疏散人员半径、初始人员密度与疏散效率呈负向关系，同时在不同布局中，影响疏散效率的关键参数有一定区别。     

小批量多返工生产系统瞬态性能分析和优化

随着智能制造和绿色制造战略的提出,实际生产对小批量定制化加工的需求日益增加,同时为了进一步提高生产制造的加工效率和产品质量,返工生产线在企业中的应用也日益频繁.基于小批量生产背景,针对应用随机故障机器和有限缓冲区的多返工生产系统,进行瞬态性能分析,并对多返工系统进行生产调度.为定量分析该生产系统的瞬态性能,通过马尔科夫分析方法建立系统的数学模型,提出一种动态的分解和聚合算法来预测系统的生产率、消耗率、在制品库存等瞬态性能指标,并基于仿真对比实验验证所提出算法的精确性和有效性.此外,针对返工生产系统在劳动力有限的情景下,采用优化算法对各机器效率进行合理分配来优化系统的生产性能.最后,根据劳动力分配实验结果总结分配策略,为该系统的生产运行提供决策支持.     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

A high performance simulation methodology for multilevel grid-connected inverters

To design a high reliability multilevel grid-connected inverter,a high performance simulation methodology based on Saber is proposed.The simulation methodology with optimized simulation speed can simulate the factors that have significant impacts on the stability and performance of the control system,such as digital delay,dead band,and the quantization error.The control algorithm in the simulation methodology is implemented using the C language,which facilitates the future porting to an actual system since most actual digital controllers are programmed in the C language.The modeling of the control system is focused mainly on diode-clamped three-level grid-connected inverters,and simulations for other topologies can be easily built based on this simulation.An example of designing a proportional-resonant (PR) controller with the aid of the simulation is introduced.The integer scaling effect in fixed-point digital signal processors (DSPs) on the control system is demonstrated and the performance of the controller is validated through experiments.     

秦岭特长公路隧道火灾数值仿真研究

该文通过计算机建立了实验中所用的秦岭特长公路隧道的数值模型,并采用与实验中相同的火灾规模和边界条件对不同风速下隧道拱顶纵向温度分布进行了数值仿真研究。将仿真的结果和实验数据进行了比较,实现了对这个数值模型的有效性的验证。然后,采用这个数值模型对秦岭特长公路隧道发生火灾时的热环境进行了数值仿真研究,获得了从实验中很难得到的数据并在此基础是对隧道的性能化防火设计、消防安全措施、特别是对人员的在火灾时的安全逃生作了可靠的分析和预测。     

A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior

Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion across different areas relative to the storm path and the most congested O-D pairs in the network. The simulation tool can be used as a planning tool to make decisions related to how traffic information should be communicated and in the design of traffic management policies such as contra-flow strategies during evacuations.     

半导体制造系统仿真调度中的优化方法

基于仿真的调度方法通常需要进行大量的仿真或者采用好的规则以优化调度结果. 本 文建立了以减小平均在制品为优化目标的半导体制造系统的调度模型, 对模型进行分解和简 化. 把结论作为一个调度规则直接应用于仿真调度方法. 由于充分利用了系统全局的状态信 息, 可以有效地减少仿真的次数, 提高了仿真调度的优化能力.     

An integrated multidimensional process improvement methodology for manufacturing systems

In this paper, an integrated multidimensional process improvement methodology (IMPIM) is formulated to address the yield management, process control and cost management problems of a manufacturing system. Simulation is used as a platform to implement the integrated multidimensional process methodology by incorporating the productivity, quality and cost dimension in a unified, systematic and holistic manner. Total Quality Management (TQM) addresses the quality parameters and Activity-Based Costing is used to manage the cost dimension of the system. Discrete event simulation is then used as a platform to perform process reengineering (Business Process Reengineering) and process improvement (TQM). The general implementation framework of the IMPIM is given with a step-by-step explanation. A conceptual discussion is also provided for the integrated methodology. The generic IMPIM is then formulated and the detailed implementation procedures for two case studies are compared with the generic methodology.     

Alignment Properties in Ordinal Comparison of Discrete Event Dynamic Systems

In the design and optimization of discrete event dynamic systems, it is often necessary to order alternative designs based on their relative performance, i.e., to rank them from best to worst. In this paper, alignment of observed performance orders with true orders is considered and properties of the alignment are investigated. Spearman's rank correlation coefficient is a measure of agreement between the observed performance orders and the true ones. It is shown that Spearman's coefficient converges exponentially in the simulation time or observation time, which gives a strong evidence of the efficiency of order comparison for discrete event dynamic systems. In the context of simulation, the effect of simulation dependence on the alignment is also discussed. It is found that neither independent simulation nor the scheme of common random numbers (CRN), a popular scheme for variance reduction, can yield dominant performance. Finally, numerical examples based on a networking optimization problem are provided to illustrate the convergence of Spearman's coefficient. In these examples, the standard clock (SC) simulation technique provides much faster convergence than either independent simulations or CRN simulations. Both the SC and CRN methods use the same random number sequence to drive many events in parallel; however, under SC the events driving the parallel experiments are all identical, whereas under CRN they may be different.     

Manufacturing cell formation with production data using neural networks

Batch type production strategies need adoption of cellular manufacturing (CM) in order to improve operational effectiveness by reducing manufacturing lead time and costs related to inventory and material handling. CM necessitates that parts are to be grouped into part families based on their similarities in manufacturing and design attributes. Then, machines are allocated into machine cells to produce the identified part families so that productivity and flexibility of the system can be improved. Zero-one part-machine incidence matrix (PMIM) generated from route sheet information is commonly presented as input for clustering of parts and machines. An entry of ‘1’ in PMIM indicates that the part is visiting the machine and zero otherwise. The output is generated in the form of block diagonal structure where each block represents a machine cell having more than one machines and a part family. The major limitations of this approach lies in the fact that important production factors like operation time, sequence of operations, and lot size of the parts are not accounted for. In this paper, an attempt has been made to propose a clustering methodology based on adaptive resonance theory (ART) for addressing these issues. Initially, a methodology considering only the operation sequence of the parts has been proposed. Then, the methodology is suitably modified to deal with combination of operation sequence and operation time of the parts to address generalized cell formation (CF) problem. A new performance measure is proposed to quantify the performance of the proposed methodology. The performance of the proposed algorithm is tested with benchmark problems from open literature and the results are compared with the existing methods. The results clearly indicate that the proposed methodology outperforms the existing methods in most cases.     

Contextually enriched competence model in the field of sustainable manufacturing for simulation style technology enhanced learning environments

In order to remain competitive, engineers need to acquire competences necessary to attain the sustainability objectives that are emerging in the manufacturing industry. The training of current and future engineers needs to achieve two criteria: the learners need to be able to apply the learning into complex, life-like situations and the learning outcomes need to be achieved rapidly. Competence-based and technology-enhanced learning (TEL) in general and serious games and simulations in particular have recently attracted a great deal of attention as they have the potential to deliver on both accounts. Designing learning content for simulation style competence-based TEL environments places special demands on competence modelling. Competences are traditionally defined and modelled using internal-to-individual factors such as knowledge, skills and attitudes. However, designing simulation style competence-based TEL environments, such as serious games, cannot be based solely on these factors. In work situations competences are performed neither in solitude nor isolation. Contextual factors such as organization processes, laws and norms, the market and available technologies significantly influence competence performance. As the simulations in question need to be life-like constructions of work situations the role of contextual factors is heightened. In addition, the content area of sustainable manufacturing is novel, which adds to the challenge. The two main innovative contributions that are presented in this paper are: (1) an enhanced competence modelling methodology which aids in identifying and structuring relevant contextual factors that have an impact on the performance of the competences in question, and (2) the output of applying this methodology, i.e. a contextually enriched competence model in the field of sustainable manufacturing that can be used to facilitate the design of the TEL content for serious games and simulations. The work presented in the paper is a part of the European Seventh Framework Programme Integrated Project TARGET, the main aim of which is to develop a new genre of TEL environments that can be effectively used in engineering education to support future engineers’ rapid competence development in the field of sustainable manufacturing.     

Data-Enabled Modeling and Analysis of Multistage Manufacturing Systems with Quality Rework Loops

In a multistage serial production line, multiple inspection stations and repair processes are typically involved to ensure high product quality. Quality rework is the activity to repair or repeat the work on the defect parts during manufacturing processes. The rework process after each inspection can add cost and cycle time to the normal process and impose negative impact on the throughput. This paper studies real-time performance of multistage serial manufacturing systems with quality rework loops and machine random failures. A production line with multiple quality rework loops is first unify by segmenting it into a set of serially connected quality rework loops. An event-based data-enabled mathematical model is developed to evaluate real-time production rate of each machine for such a system. In addition, the system properties are analyzed and permanent production loss due to quality rework loops and random machine failures are identified respectively. The permanent production loss attribution to each disruption event and machine can be used as real-time performance indicators to diagnose production system inefficiency. The mathematical model and system performance identification methodology are studied analytically and validated through numerical case studies.     

Data-driven dynamic bottleneck detection in complex manufacturing systems

Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE).     

A simulation comparison of process and cellular layouts in a dual resource constrained environment

Cellular manufacturing is an integral part of a comprehensive Group Technology program designed to improved the productivity of batch production systems. It has been suggested that shorter throughput times and accompanying reductions in work-in-process inventory are possible due to the inherent flexibility of cellular layouts with respect to worker scheduling. This research examines the impact of a dual resource (labor and equipment) constrained shop on the relative performance of cell layouts vis-a-vis process layouts. In addition, three operator scheduling rules are tested in the cellular layout. The first rule assigns operators on a first come-first served basis to jobs competing for an operator's services. The second rule requires operators in a cell to select a job from the machine queue with the longest queue of jobs. The third rule has operators remaining at a machine queue until it is empty. In the process layout, operators are assinged to waiting jobs on a first come-first served basis. In the initial experiment, the process layout outperformed the cellular layout on both work-in-process levels and throughout time. Additional experiments investigated the sensitivity of the initial results to changes in shop congestion. The process layout outperformed the cellular layout in all of the experiments. The results may be attributed to lower machine and labor utilization in the cellular layout from the dedication of equipment to limited part families.     

An integrated methodology for manufacturing systems design using manual and computer simulation

Traditional approaches to manufacturing systems design utilize a sequential procedure that focuses on production capacity requirements, with human operator task design developed late in the systems design phase. Implementing manufacturing systems in this way is difficult when operations management must design flexible and efficient processes, with an often incomplete understanding of how people can best perform within the system. This study developed an integrated methodology that uses both manual and computer simulations to evaluate system performance and ergonomic issues early in the system design process. Information about operator performance and ergonomics is obtained in the manual simulations, while estimates of operator utilization and system throughput is obtained through computer simulations. An iterative design process is used, with the results of manual and computer simulations informing each other during subsequent simulations. An industrial case study is presented here to demonstrate the effectiveness of this methodology. The results show that the methodology can be used to design manufacturing systems with significant savings in labor cost and improved manufacturing system flexibility. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 19–40, 2003.