Developing a multi-objective genetic optimisation approach for an operational design of a manual mixed-model assembly line with walking workers

A walking worker assembly line (WWAL), in which each cross-trained worker travels along the line to carry out all required tasks, is an example of lean system, specifically designed to respond quickly and economically to the fluctuating nature of market demands. Because of the complexity of WWAL design problems, classical heuristic approaches are not capable of solving problematic design characteristic of WWAL of very large design space. This paper presents a new genetic approach to address the mixed model walking worker manual assembly line optimisation design problem with multiple objectives. The aim is to select a set of operational variables to perform to the required demand for two product models. The goal is to produce the required models at the lowest cost possible, whilst keeping within an ergonomically balanced operation. Genetic algorithms are developed to tackle this problem. This paper describes the fundamental structure of this approach, as well as the influence of the crossover probability, the mutation probability and the size of the population on the performance of the genetic algorithm. The paper also presents an application of a developed algorithm to the operational design problem of plastic electrical box assembly line.     

Lexicographic bottleneck mixed-model assembly line balancing problem: Artificial bee colony and tabu search approaches with optimised parameters

The lexicographic bottleneck assembly line balancing problem is a recently introduced problem which aims at obtaining a smooth workload distribution among workstations. This is achieved hierarchically. The workload of the most heavily loaded workstation is minimised, followed by the workload of the second most heavily loaded workstation and so on. This study contributes to knowledge by examining the application of the lexicographic bottleneck objective on mixed-model lines, where more than one product model is produced in an inter-mixed sequence. The main characteristics of the lexicographic bottleneck mixed-model assembly line balancing problem are described with numerical examples. Another contribution of the study is the methodology used to deal with the complex structure of the problem. Two effective meta-heuristic approaches, namely artificial bee colony and tabu search, are proposed. The parameters of the proposed meta-heuristics are optimised using response surface methodology, which is a well-known design of experiments technique, as a unique contribution to the expert and intelligent systems literature. Different from the common tendency in the literature (which aims to optimise one parameter at a time), all parameters are optimised simultaneously. Therefore, it is shown how a complex production planning problem can be solved using sophisticated artificial intelligence techniques with optimised parameters. The methodology used for parameter setting can be applied to other metaheuristics for solving complex problems in practice. The performances of both algorithms are assessed using well-known test problems and it is observed that both algorithms find promising solutions. Artificial bee colony algorithm outperforms tabu search in minimising the number of workstations while tabu search shows a better performance in minimising the value of lexicographic bottleneck objective function.     

Optimisation of mixed-model assembly line balancing problem under uncertain demand

Assembly lines play a crucial role in determining the profitability of a company. Market conditions have increased the importance of mixed-model assembly lines. Variations in the demand are frequent in real industrial environments and often leads to failure of the mixed-model assembly line balancing scheme. Decision makers have to take into account this uncertainty. In an assembly line balancing problem, there is a massive amount of research in the literature assuming deterministic environment, and many other works consider uncertain task times. This research utilises the uncertainty theory to model uncertain demand and introduces complexity theory to measure the uncertainty of assembly lines. Scenario probability and triangular fuzzy number are used to describe the uncertain demand. The station complexity was measured based on information entropy and fuzzy entropy to assist in balancing systems with robust performances, considering the influence of multi-model products in the station on the assembly line. Taking minimum station complexity, minimum workload difference within station, maximum productivity as objective functions, a new optimization model for mixed-model assembly line balancing under uncertain demand was established. Then an improved genetic algorithm was applied to solve the model. Finally, the effectiveness of the model was verified by several instances of mixed-model assembly line for automobile engine.     

A note on Toyota's goal of sequencing mixed models on an assembly line

Toyota's goal of sequencing mixed models on an assembly line is to keep the constant usage of every part used in the assembly line. This goal is a good way of fitting the just-in-time concept in Toyota’s production system. In all of Toyota's goal oriented studies a consideration which has not been explained explicitly in the literature is that all the parts of a given product are assumed to be used at the epoch of just this unit into the assembly line. This treatment is equivalent to an assumption of a single workstation assembly line with zero length. For an assembly line with multiple workstations, however, it is clear that the parts of a given product are used at different epochs subsequent to originally feeding this unit into it. This note discusses Toyota's goal of sequencing mixed models on an assembly line with multiple workstations. The sequencing problem is formulated based on defining the ideal usage rate of a part as the requirement for the part per time period. A modified goal chasing algorithm is proposed for solving this sequencing problem. An example is given to illustrate the methodology.     

Design of assembly lines with the concurrent consideration of productivity and upper extremity musculoskeletal disorders using linear models

Work-related musculoskeletal disorders (WMSDs) are common occupational diseases among assembly workers due to repetitive motions or heavy workloads. The conventional approaches to decreasing WMSDs in assembly workers usually focus on individual assembly work at the station level. These approaches, however, do not pay enough attention to work allocation at the whole assembly line level such as balancing ergonomic burdens among workers by proper work assignment. This paper presents a methodology that can be used to integrate ergonomic measures of upper extremities into assembly line design problems. Linear models are developed to link work-worker assignment to the upper extremity ergonomic measures based on a guideline from American Conference of Governmental Industrial Hygienists. These linear models allow ergonomic and productivity measures to be integrated as a mixed-integer programming model. The case studies of this paper show the new model can effectively balance and control exposure levels in the upper extremity while not significantly decreasing line efficiency. This research shows the potential to reduce the need of numerous task adjustments for ergonomic improvement after initial assembly line design in conventional trial-and-error based assembly task adjustment. Furthermore, these linearization methods can be generalized in order to incorporate other ergonomic measures in tabulated forms into assembly line design problems.     

Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem

This paper addresses a novel distributed assembly permutation flowshop scheduling problem that has important applications in modern supply chains and manufacturing systems. The problem considers a number of identical factories, each one consisting of a flowshop for part-processing plus an assembly line for product-processing. The objective is to minimize the makespan. To suit the needs of different CPU time and solution quality, we present a mixed integer linear model, three constructive heuristics, two variable neighborhood search methods, and an iterated greedy algorithm. Important problem-specific knowledge is obtained to enhance the effectiveness of the algorithms. Accelerations for evaluating solutions are proposed to save computational efforts. The parameters and operators of the algorithms are calibrated and analyzed using a design of experiments. To prove the algorithms, we present a total of 16 adaptations of other well-known and recent heuristics, variable neighborhood search algorithms, and meta-heuristics for the problem and carry out a comprehensive set of computational and statistical experiments with a total of 810 instances. The results show that the proposed algorithms are very effective and efficient to solve the problem under consideration as they outperform the existing methods by a significant margin.     

A memetic algorithm applied to the design of water distribution networks

The optimal design of water distribution networks is a real optimization problem that consists of finding the best way to convey water from the sources to the users, satisfying their requirements. Many researchers have reported algorithms for minimizing the network cost applying a large variety of techniques, such as linear programming, non-linear programming, global optimization methods and meta-heuristic approaches. However, a totally satisfactory and efficient method is not available as yet. Many works have assessed the performance of these techniques using small or medium-sized benchmark networks proposed in the literature, but few of them have tested these methods with large-scale real networks. This paper introduces a new memetic algorithm for the optimal design of water distribution networks. In order to establish an accurate conclusion, five other approaches have also been adapted, namely simulated annealing, mixed simulated annealing and tabu search, scatter search, genetic algorithms and binary linear integer programming. The results obtained in three water distribution networks show that the memetic algorithm performs better than the other methods, especially when the size of the problem increases.     

Data driven production modeling and simulation of complex automobile general assembly plant

Flexible manufacturing and customization has long been a topic in modern manufacturing society. However, challenges still remain on the responsiveness of production system to the fluctuation of market demand. In this paper, we developed a data driven simulation methodology to automatically model a production system and rapidly modify the model corresponding to dynamic requirements and real time information. This methodology provides a “rapid prototyping” capability for production system modeling and enables a quick analyzing and remodeling capability to respond to the fluctuation of demands. The approach is developed and applied to an automotive general assembly plant with an online material handling system. A complete information model based on IDEF1X is constructed for this domain specific modeling and simulation. The main simulation modules for assembly line and material handling system of the plant floor are analyzed and a simulator is implemented in ARENA by SIMAN/VBA program. The case study of an automotive assembly plant shows that the data driven approach enables the modeling and simulation of the complex assembly plant in a “real-time” fashion and therefore effectively improve the responsiveness and flexibility of the production line.     

Dynamic workforce allocation in a constrained flow shop with multi-agent system

The present work addresses the problem of real time workforce scheduling in assembly lines where the number of operators is less to the number of workstations.The problem is faced developing a two-steps procedure made of (i) a centralized scheduling based on a constraint optimization problem (COP) for initial operator scheduling, and (ii) a decentralized algorithm performed by a multiagent system (MAS) to manage workers in case of unforeseen events.In the proposed MAS architecture, Agents represent the operators trying to find local assignments for themselves. The system is validated with a simulation model and implemented with a hardware infrastructure in a real assembly line of electromechanical components. The main original contribution of the paper consists in proving – by means of both validation through a simulation model and test in a real assembly line of electromechanical components – that (1) multi-agent systems could be successfully adopted to solve a workforce scheduling problem, and (2) a combined approach consisting of centralized + distributed approach would provide better results compared with the application of one of the two approaches alone.     

基于几何可靠性机器模型的装配系统实时性能分析

装配系统是生产系统的基本结构之一, 广泛应用于汽车、电器、电子产品等实际生产环境中.与传统的串行生产线取得的研究成果相比, 装配系统的研究, 特别是对系统暂态过程的实时性能分析的研究仍然未得到深入探讨.本文针对具有三台几何可靠性机器模型和有限缓冲区容量框架下的装配系统, 首先建立了用于此类系统暂态性能分析的数学模型, 通过马尔科夫方法导出了系统性能分析的解析公式.然后, 提出了一种基于分解的性能评估算法来近似系统的实时性能.具体来说, 本文推导出了用于计算具有三台几何可靠性机器模型的装配系统的实时生产率、消耗率、在制品数量, 以及完成一个生产批次所需时间的解析表达式.最后, 通过数值实验对所提出算法的准确性进行验证.     

Mixed model U-line balancing type-1 problem: A new approach

Avoiding work overload (imbalance) in mixed model U-line production systems entails an investigation into both balancing and sequencing problems at the same time and that is why some authors have considered both planning problems simultaneously. However because of the existing differences between planning horizons of balancing and sequencing problems (the former is a long to mid-term planning problem whereas the latter has a short term planning horizon) this simultaneous approach is only practical under very special conditions. It is also known that installation of an assembly line usually needs considerable capital investments and consequently it is necessary to design and balance such a system so that it works as efficiently as possible. To do so, in this paper, we develop a new approach to balance a mixed model U-shaped production system independent of what product sequences may be. This new approach is based on minimization of crossover workstations. Due to utilization of crossover workstations, balancing mixed model assembly lines in U-shaped line layouts is more complicated than that of straight lines. Some kind of issues including the ‘model mixes’ appearing in such workstations and the time taken for an operator to move from one side of the line to another increase the complexity of mixed model U-line balancing problems (MMULBP). Therefore it seems reasonable to develop a model in which minimizing the number of crossover workstations and maximizing the line efficiency are considered at the same time. Such a model is presented in this paper. In the proposed model, minimizing the variation of workload is also considered and taking into account operator's travel times, an extra time is assigned to workload of crossover workstations. Furthermore a genetic algorithm (GA) is proposed and a number of well-known test problems are solved by the GA and the related results are illustrated. Finally, the conclusion is presented.     

实时对象的分类与比较研究

传统实时系统的设计方法已无法满足现代实时系统对集成,局部验证及预想性的设计要求,面向对象的方法是解决这个问题的良好手段,但其固有的特性很难满足实时系统的可预测性要求,为此,本文介绍了实时对象的基本思想,并根据所采纳实时调度策略对现有的实时对象模型进行了分类与比较,最后,指出了它们的适用范围。     

Event and object oriented simulation to fast evaluate operational objectives of mixed model assembly lines problems

In this paper an event and object oriented simulator for assembly lines is presented. The tool, developed in Java, is capable to simulate mixed model assembly lines, with stochastic task times, parallel stations, fixed scheduling sequences, and buffers within workstations. The simulator is a flexible supporting tool in finding solution of the mixed model assembly line balancing problem (and the optimal sequencing and buffer allocation problems associated to it). It is capable to immediately calculate the throughput of a complex line, by simply receiving as inputs three arrays representing: the task times durations, the line configuration (number of workcentres, of buffers within them, of parallel workstations in each workcentre, of tasks assigned to each workcentre), and the sequence of models entering the line. Its fastness and flexibility allow its utilization in those algorithms and procedures where the evaluation of a fitness function (which includes the throughput as performance indicator) has to be performed several times. It allows overcoming the limit of using others measures of throughput, presented in literature, that are poorly correlated to its real value when the complexity of the line increases. The simulator is an expandable tool; in its current version provides the possibility to simulate both straight and U-shaped lines, and to generate both random and fixed sequences of models entering the line.     

Terrain Avoidance Nonlinear Model Predictive Control for Autonomous Rotorcraft

This paper describes a terrain avoidance control methodology for autonomous rotorcraft applied to low altitude flight. A simple nonlinear model predictive control (NMPC) formulation is used to adequately address the terrain avoidance problem, which involves stabilizing a nonlinear and highly coupled dynamic model of a helicopter, while avoiding collisions with the terrain as well as preventing input and state saturations. The physical input saturations are made intrinsic to the model, such that the control is always admissible and the MPC design is simplified. A comparison of several optimization approaches is provided, where the performance of the traditional gradient method with fixed step is compared with the quasi-Newton method and a line search algorithm. The simulation results show that the adopted strategy achieves good performance even when the desired path is on collision course with the terrain.     

数据驱动的汽车总装线建模与仿真技术研究

现代制造业的生产模式向柔性制造和客户化定制方向发展,为了提高制造系统的快速响应能力,实现快速仿真分析,提出基于数据驱动的建模与仿真方法,其具有建模周期短、模型可读性、可重用性较高的特点.利用IDEFIX建模方法建立模型的底层数据结构,基于Arena构建仿真模型生成器,利用已建好的数据模型提供的属性信息,自动生成目标系统...     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

汽车装配车间生产计划与调度的同时优化方法

文中提出三种新方法来解决汽车装配车间生产计划与调度的同时优化问题.首先将汽 车装配线简化为一个Flow shop问题,并建立其混合整数规划模型,以求得使各装配工位的准 备成本和空闲时间尽可能少并尽可能满足产品需求的粗生产计划.然后在粗生产计划的基础上 考虑装配线的细节,用Tabu搜索法与快速调度仿真相结合的三种不同启发式算法使生产计划 与调度同时得到优化,并给出了三种算法的复杂性.大量算例的比较研究表明了这些算法的有 效性和适用性.     

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.     

多目标遗传算法在混流装配线排序中的应用

混流装配线实现在一条流水线上装配多种不同类型的产品。该文在总结混流装配线排序问题的基础上建立了二种排序的目标函数：最小化工作站的闲置与超载时间和保持均匀的零部件消耗速率。引入了基于Pareto理论和小生镜单元技术的适应度函数及选择算子构建了多目标遗传算法用于混流装配线的排序优化问题。通过一个混流装配线的多目标排序实验,验证了该方法的有效性。