Sequencing mixed-model assembly lines with genetic algorithms

This research introduces the use of an artificial-intelligence based technique, genetic algorithms (GA), to solve mixed-model assembly-line sequencing problems. This paper shows how practitioners can comfortably implement this approach to solve practical problems. A substantial example is given for which GA produces a solution in just a matter of seconds that improves upon Toyota's Goal Chasing Algorithm. The new method is then investigated on a test bed of 80 problems. Results indicate GA generates an improved sequence over Goal Chasing on 50 of the problems and also shows a performance advantage of 2% across all 80 problems using Toyota's variability of parts consumption criterion. The paper concludes that further investigation to fine tune the GA methodology is warranted. It also points out that the GA approach can readily be used by practitioners to address a variety of managerial goals concurrently, such as inventory and work load equalization.     

A Tabu-enhanced genetic algorithm approach for assembly process planning

Over the past decade, much work has been done to optimize assembly process plans to improve productivity. Among them, genetic algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimization methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighborhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu search (TS) constitutes a meta-procedure that organizes and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behavior can possibly be obtained by incorporating the Tabus intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.     

Dynamic material flow control in mixed model assembly lines

This study investigates the control of material flow in mixed model assembly lines. It focuses on the use of tugger trains to feed stations in assembly lines by materials and parts from a warehouse or a supermarket. The movement of tugger trains is based on the principle of in-plant milk run. The study considers a strategy to deal with disturbances such as machine breakdown, line stoppage, defective parts, and resequencing of product models. These disturbances lead to unexpected fluctuations in stations demand for parts. The strategy is applied using a mix between the demand-oriented and e-kanban systems to facilitate the planning of three problems, namely, train routing, scheduling, and loading. The information obtained using e-kanban is combined with the information about the expected stations demand based on previously known sequence of product models and the materials needed for each model. Routing was investigated analytically while scheduling and loading problems were investigated using integer programming. Results showed that the method proposed outperforms the traditional methods of material flow planning.     

A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints

In this paper, we propose a hybrid genetic algorithm to solve mixed model assembly line balancing problem of type I (MMALBP-I). There are three objectives to be achieved: to minimize the number of workstations, maximize the workload smoothness between workstations, and maximize the workload smoothness within workstations. The proposed approach is able to address some particular features of the problem such as parallel workstations and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively. We aim to improve its exploring capability by sequentially hybridizing the three well known heuristics, Kilbridge & Wester Heuristic, Phase-I of Moodie & Young Method, and Ranked Positional Weight Technique, with genetic algorithm. The proposed hybrid genetic algorithm is tested on 20 representatives MMALBP-I and the results are compared with those of other algorithms.     

准序化供货模式下的混装线重排序

准序化供货是在准时制的基础上对零部件进行排序供货,其顺利实施需要依靠稳定的生产序列与零部件交付的可靠性。针对零部件交付过程中的意外事件,研究了受到意外事件影响时的工件重排序问题。首先以最小化所有工件在各工作站的超载时间与空闲时间总成本为目标,建立了问题的数学模型,并提出了三种贪婪规则和一种模拟退火算法。接着设计了算例来验证算法的性能。实验结果表明,四种算法均可在较短的时间内起到重排序的效果,其中模拟退火算法效果最好,也优于文献中的局部搜索算法。最后讨论了影响算例运行结果的因素。     

Genetic algorithms for sequencing problems in mixed model assembly lines

Mixed model assembly lines are a type of production line where a variety of product models similar in product characteristics are assembled. The effective utilisation of these lines requires that a schedule for assembling the different products be determined. In this paper, the performance of genetic algorithms for sequencing problems in mixed model assembly lines is investigated. The problem first considered is a comparison between a existing heuristic and the proposed genetic algorithm to get the constant usage of every part used by the line considering variation at multi levels (Number of levels fixed as four. level 1—product, level 2—subassembly, level 3—component, level 4—raw-materials) for various test-bed problems. The algorithms proposed by Miltenburg and Sinnamon hereafter referred to as MS 1992 [IIE Trans. 24 (1992) 121] and the proposed genetic algorithm (GA) applied to mixed model assembly line are compared. Results of evaluation indicate that the GA performs better over MS1992 on 25 of the 40 problems investigated.

The other problem solved is a multiple objective sequencing problem in mixed model assembly lines. Three practically important objectives are minimizing total utility work keeping a constant rate of part-usage, minimizing the variability in parts usage and minimizing total setup cost. In this paper, the performance of the selection mechanisms, the Pareto stratum-niche cubicle and the selection based on scalar fitness function value are compared with respect to the objective of minimising variation in part-usage, minimising total utility work and minimising the setup cost. Results of evaluation indicate that the genetic algorithm that uses the Pareto stratum-niche cubicle performs better than the genetic algorithm with the other selection mechanisms.     

A hybrid genetic algorithm approach on multi-objective of assembly planning problem

In practice, modeling an assembly system often requires assigning a set of operations to a set of workstations. The aim is to optimize some performance indices of an assembly line. This assignation is usually a tedious design procedure so a significant amount of manpower is required to obtain a good work plan. Poor assembly planning may significantly increase the cost of products and reduce productivity. However, these optimization problems fall into the class of NP-hard problems. Finding an optimal solution in an acceptable time is difficult, even using a powerful computer. This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences. A self-tuning method was developed to correct infeasible chromosomes. Several examples were employed to illustrate the proposed approach. Experimental results indicated that the proposed method can efficiently yield many alternative assembly plans to support the design and operation of a flexible assembly system.     

Balancing and scheduling of flexible mixed model assembly lines

Mixed model assembly line literature involves two problems: balancing and model sequencing. The general tendency in current studies is to deal with these problems in different time frames. However, in today’s competitive market, the mixed model assembly line balancing problem has been turned into an operational problem. In this paper, we propose mixed integer programming (MIP) and constraint programming (CP) models which consider both balancing and model sequencing within the same formulation along with the optimal schedule of tasks at a station. Furthermore, we also compare the proposed exact models with decomposition schemes developed for solving different instances of varying sizes. This is the first paper in the literature which takes into account the network type precedence diagrams and limited buffer capacities between stations. Besides, it is the first study that CP method is applied to balancing and scheduling of mixed model assembly lines. Our empirical study shows that the CP approach outperforms the MIP approach as well as the decomposition schemes.     

一种新型混合算法在混流装配调度中的应用

针对含有缓冲区的混流装配中同时存在的生产成本和库存成本问题,提出了一种基于遗传算法和差分进化算法的混合框架,并将其用于混流装配调度的实际问题中。通过融合遗传算法有效处理离散变量及差分进化算法有效处理连续变量的优点,在综合考虑降低生产成本和缓冲区库存的同时,兼顾了每个型号产品生产的顺序及数量。计算机仿真结果表明,与传统算法相比,该算法在混流装配调度上具有收敛速度快、优化能力强、算法可靠等优势。该混合算法可以显著改善多参数、高度非线性问题的优化结果,提高计算效率。     

A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines

Assembly Lines (ALs) are used for mass production as they offer lots of advantages over other production systems in terms of lead time and cost. The advent of mass customization has forced the manufacturing industries to update to Mixed-Model Assembly Lines (MMALs) but at the cost of increased complexity. In the real world, industries need to determine the sequence of models based on various conflicting performance measures/criteria. This paper investigates the Multi-Criteria Model Sequencing Problem (MC-MSP) using a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm. To address the multiple criteria, a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm was developed by integrating a priori approach with NEH algorithm. Discrete Event Simulation (DES) was used to evaluate each solution. A mathematical model was developed for three criteria: flow time, makespan and idle time. Further, to validate the effectiveness of the proposed SMC-NEH a case study and Taillard's benchmark instances were solved and a Multi-Criteria Decision-Making (MCDM) analysis was performed to compare the performance of the proposed SMC-NEH algorithm with the traditional NEH algorithm and its variants. The results showed that the proposed SMC-NEH algorithm outperformed the others in optimizing the conflicting multi-criteria problem.     

汽车混流装配线物料动态配送研究

为解决汽车混流装配线物料准确地动态配送问题,设计了基于RFID技术的汽车混流装配的零部件动态配送方案.通过RFID识别跟踪实际生产进度,将配送单动态地发给配送人员,采用惩罚函数对人员配送效率进行考核,并计算配送开始的最佳时间.结合算例验证了方案的可行性和实用性.     

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.     

A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach

This research deals with balancing a mixed-model U-line in a Just-In-Time (JIT) production system. The research intends to reduce the number of stations via balancing the workload and maximizing the weighted efficiency, which both are considered as the objectives of this research paper.After balancing the line and determining the number of stations, the labor assignment policy should be set. In this study, it was assumed that there are two types of operators: permanent and temporary. Both types can work in regular and overtime periods. Based on their skill levels, workers are classified into four types. The sign at each work station indicates types of workers allowed to work at that station. An alert system using the hybrid kanban systems was also considered. To solve this problem, a Simulated Annealing algorithm was applied in the following three stages. First, the balancing problem was solved and the number of stations was determined. Second, workers were assigned to the workstations in which they are qualified to work. Following that, an alert system based on the kanban system was designed to balance the work in the process inventory. This was achieved by defining control points based on the processing time and making control decisions to minimize the number of kanban cards. In the proposed SA algorithm, two methods for the temperature cooling schedule were considered and two methods were defined for determining the number of neighborhood search. The initial temperature was considered equal to the cost of the initial solution to reach the convergence situation as soon as possible. Five problems were solved in small size using the GAMS software. The results obtained from the GAMS software were compared with those obtained from the SA algorithm to determine the performance difference. The computational results demonstrated that the SA algorithm is more consistent with the answers obtained. Also seven large scale problems were solved. The results showed that the SA algorithm still have better reliability. To show the efficiency of the proposed SA algorithm, an axel assembly company was studied. To satisfy demands and reduce backlogging, a mixed model assembly line was designed for this case study. The results showed that the mixed model assembly line designed using the SA algorithm had good efficiency.     

Human factor policy testing in the sequencing of manual mixed model assembly lines

In this paper the human resource management in manual mixed model assembly U-lines is considered. The objective is to minimise the total conveyor stoppage time to achieve the full efficiency of the line. A model, that includes effects of the human resource, was developed in order to evaluate human factor policies impact on the optimal solution of this line sequencing problem. Different human resource management policies are introduced to cope with the particular layout of the proposed line. Several examples have been proposed to investigate the effects of line dimensions on the proposed management policies. The examples have been solved through a genetic algorithm. The obtained results confirm the effectiveness of the proposed model on the performance optimisation of the line.Scope and purposeThis paper deals with the sequencing problem of a mixed model U-assembly line; in particular, it considers the minimisation of the total line stoppage time as an objective function. In order to improve the assembly line performances and to empathise the impact of the human resource in production environment, several help policies between workers have been analysed. Moreover, in the proposed model, finite values of the workers walking speed have been taken into account: in this way, their influence on the adopted help policies was evaluated. As the proposed problem was demonstrated to be NP-hard, a proper genetic algorithm was developed for its optimisation. An extensive experiment was carried out to determine a proper choice among the adopted help policies.     

A genetic algorithm for generating optimal assembly plans

In this paper, we propose a genetic algorithm that generates and assesses assembly plans. An appropriately modified version of the well-known partially matched crossover, and purposely defined mutation operators allow the algorithm to produce near-optimal assembly plans starting from a randomly initialised population of (possibly non-feasible) assembly sequences. The quality of a feasible assembly sequence is evaluated based on the following three optimisation criteria: (i) minimising the orientation changes of the product; (ii) minimising the gripper replacements; and (iii) grouping technologically similar assembly operations. Two examples that endorse the soundness of our approach are also included.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

A multi-objective genetic algorithm for mixed-model assembly line rebalancing

When demand structure or production technology changes, a mixed-model assembly line (MAL) may have to be reconfigured to improve its efficiency in the new production environment. In this paper, we address the rebalancing problem for a MAL with seasonal demands. The rebalancing problem concerns how to reassign assembly tasks and operators to candidate stations under the constraint of a given cycle time. The objectives are to minimize the number of stations, workload variation at each station for different models, and rebalancing cost. A multi-objective genetic algorithm (moGA) is proposed to solve this problem. The genetic algorithm (GA) uses a partial representation technique, where only a part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed optimally. A non-dominated ranking method is used to evaluate the fitness of each chromosome. A local search procedure is developed to enhance the search ability of moGA. The performance of moGA is tested on 23 reprehensive problems and the obtained results are compared with those by other authors.     

A complexity model for sequence planning in mixed-model assembly lines

Sequence planning is an important problem in assembly line design. It is to determine the order of assembly tasks to be performed sequentially. Significant research has been done to find good sequences based on various criteria, such as process time, investment cost, and product quality. This paper discusses the selection of optimal sequences based on complexity induced by product variety in mixed-model assembly line. The complexity was defined as operator choice complexity, which indirectly measures the human performance in making choices, such as selecting parts, tools, fixtures, and assembly procedures in a multi-product, multi-stage, manual assembly environment. The complexity measure and its model for assembly lines have been developed in an earlier paper by the authors. According to the complexity models developed, assembly sequence determines the directions in which complexity flows. Thus proper assembly sequence planning can reduce complexity. However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming. Methodologies developed in this paper extend the previous work on modeling complexity, and provide solution strategies for assembly sequence planning to minimize complexity.     

A genetic algorithm approach to cluster analysis

A common problem in the social and agricultural sciences is to find clusters in experimental data; the standard attack is a deterministic search terminating in a locally optimal clustering. We propose here a genetic algorithm (GA) for performing cluster analysis. GAs have been used profitably in a variety of contexts in which it is either impractical or impossible to directly solve for a globally optimal solution to complex numerical problems. In the present case, our GA clustering technique attempted to maximize a variance-ratio (VR) based goodness-of-fit criterion defined in terms of external cluster isolation and internal cluster homogeneity. Although our GA-based clustering algorithm cannot guarantee to recover the cluster solution that exhibits the global maximum of this fitness function, it does explicitly work toward this goal (in marked contrast to existing clustering algorithms, especially hierarchical agglomerative ones such as Ward's method). Using both constrained and unconstrained simulated datasets, Monte Carlo results showed that in some conditions the genetic clustering algorithm did indeed surpass the performance of conventional clustering techniques (Ward's and K-means) in terms of an internal (VR) criterion. Suggestions for future refinement and study are offered.