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.     

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.     

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.     

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.     

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.     

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 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 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.     

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 team-oriented design methodology for mixed model assembly systems

Team-oriented approaches are widely being used in modern real life assembly systems, as are other modern systems. In this paper, first the literature of single and mixed model assembly line balancing, which plays an important role for the design of assembly systems, is reviewed. The associated literature matrixes reveal that team-oriented approaches do not have an intensive research area. Second, a team-oriented mathematical programming model for creating assembly teams (physical stations) in mixed model assembly lines is devised. Owing to the fact that this model is NP hard, a scheduling based heuristic algorithm is developed. The mixed model assembly line design methodology, which includes a team-oriented algorithm as a step, is proposed. Both model sequencing and worker transfer systems are included in the methodology. The algorithms for each step of the methodology are also coded by using MATLAB and MS Excel is used as the user interface. Furthermore, the presented methodology was applied in a chosen segment of a real life mixed model tractor assembly system.     

A multiple objective grouping genetic algorithm for assembly line design

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with users preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.     

A genetic algorithm approach to Chinese handwriting normalization

Normalization can be used to absorb writing variations and distortions, simplify the recognition processing steps, and improve the recognition rate of a Chinese handwriting recognition system. In this study, a genetic algorithm approach to Chinese handwriting normalization is proposed. In the proposed approach, a generalized normalization transform is defined as a linearly weighted combination of several normalization transforms and then genetic algorithms (GA's) are used to determine the optimal set of weighting coefficients. Here the fitness function contains three proposed features representing the characteristics of Chinese characters, namely, stroke density variation (SDV), character area coverage (CAC), and centroid offset (CO). Experimental results show the feasibility of the proposed approach.     

A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.     

A genetic algorithm with memory for mixed discrete-continuous design optimization

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.     

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.     

A genetic algorithm approach to image reconstruction in electricalimpedance tomography

Electrical impedance tomography (EIT) determines the resistivity distribution inside an inhomogeneous object by means of voltage and/or current measurements conducted at the object boundary. A genetic algorithm (GA) approach is proposed for the solution of the EIT inverse problem, in particular for the reconstruction of “static” images. Results of numerical experiments of EIT solved by the GA approach (GA-EIT in the following) are presented and compared to those obtained by other more-established inversion methods, such as the modified Newton-Raphson and the double-constraint method. The GA approach is relatively expensive in terms of computing time and resources, and at present this limits the applicability of GA-EIT to the field of static imaging. However, the continuous and rapid growth of computing resources makes the development of real-time dynamic imaging applications based on GAs conceivable in the near future