遗传算法在服装生产流水线平衡问题中的应用

将遗传算法应用于服装生产调度中，利用遗传算法的全局优化特点解决并行制造中的流水线平衡问题。并针对男式衬衫的生产工艺进行仿真，结果表明了该算法的有效性。     

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.     

A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem

Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.     

应用遗传算法解决装配线平衡问题

文章针对装配线平衡问题,提出了一种周期性自适应交换、变异遗传算法,通过实验求解表明,该算法是解决装配线问题的有效算法,很好地解决了简单遗传算法容易早熟收敛的问题,大大改善了简单遗传算法的性能。     

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.     

Line balancing of PCB assembly line using immune algorithms

Printed Circuit Boards (PCBs) are widely used in most electronic devices. Typically, a PCB design has a set of components that needs to be assembled. In a broad sense, this assembly task involves placing PCB components at designated location on a PCB board; fixing PCB components; and testing the PCB after assembly operation to ensure that it is in proper working order. The stringent requirements of having a higher component density on PCBs, a shorter assembly time, and a more reliable product prompt manufacturers to automate the process of PCB assembly. Frequently, a few placement machines may work together to form an assembly line. Thus, the application of more than one machine for component placement on a PCB presents a line-balancing problem, which is basically concerned with balancing the workload of all the machines in an assembly line. This paper describes the application of a new artificial intelligence technique known as the immune algorithm to PCB component placement as well as the line balancing of PCB assembly line. It also includes an overview of PCB assembly and an outline of the assembly line balancing problem. Two case studies are used to validate the IA engine developed in this work. The details of IA, the IA engine and the case studies 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.     

Assembly line balancing and group working: A heuristic procedure for workers’ groups operating on the same product and workstation

In this paper, we examine an assembly line balancing problem that differs from the conventional one in the sense that there are multi-manned workstations, where workers’ groups simultaneously perform different assembly works on the same product and workstation. This situation requires that the product is of sufficient size, as for example in the automotive industry, so that the workers do not block each other during the assembly work. The proposed approach here results in shorter physical line length and production space utilization improvement, because the same number of workers can be allocated to fewer workstations. Moreover, the total effectiveness of the assembly line, in terms of idle time and production output rate, remains the same. A heuristic assembly line balancing procedure is thus developed and illustrated. Finally, experimental results of a real-life automobile assembly plant case and well-known problems from the literature indicate the effectiveness and applicability of the proposed approach in practice.     

Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm

Monotonous body postures during repetitive jobs negatively affect assembly-line workers with the developing of Work-related Musculoskeletal Disorders (WMSDs). Ergonomics specialists have offered auxiliary posture diversity to deal with the lack of varieties, especially for high-risk ones. Meanwhile, Assembly Line Balancing (ALB) problem has been recognized as a prior thinking to (re)configure assembly lines via the balancing of their tasks among their workstations. Some conventional criteria, cycle time and overall workload are often considered during the balancing. This paper presents a novel model of ALB problem that incorporates assembly worker postures into the balancing. In addition to the conventional ALB criteria, a new criterion of posture diversity is defined and contributes to enhance the model. The proposed model suggests configurations of assembly lines via the balancing; so that the assigned workers encounter the opportunities of changing their body postures, regularly. To address uncertainties in the conventional criteria, a fuzzy goal programming is used, and an appropriate genetic algorithm is developed to deal with the model. Various computational tests are performed on the different models made with combinations of the three criteria mentioned above. Comparing the pay-offs among the combinations, results show that well balanced task allocation can be obtained through the proposed model.     

Assembly line balancing under uncertainty: Robust optimization models and exact solution method

This research deals with line balancing under uncertainty and presents two robust optimization models. Interval uncertainty for operation times was assumed. The methods proposed generate line designs that are protected against this type of disruptions. A decomposition based algorithm was developed and combined with enhancement strategies to solve optimally large scale instances. The efficiency of this algorithm was tested and the experimental results were presented. The theoretical contribution of this paper lies in the novel models proposed and the decomposition based exact algorithm developed. Moreover, it is of practical interest since the production rate of the assembly lines designed with our algorithm will be more reliable as uncertainty is incorporated. Furthermore, this is a pioneering work on robust assembly line balancing and should serve as the basis for a decision support system on this subject.     

Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge

Particle swarm optimisation (PSO) is an evolutionary metaheuristic inspired by the swarming behaviour observed in flocks of birds. The applications of PSO to solve multi-objective discrete optimisation problems are not widespread. This paper presents a PSO algorithm with negative knowledge (PSONK) to solve multi-objective two-sided mixed-model assembly line balancing problems. Instead of modelling the positions of particles in an absolute manner as in traditional PSO, PSONK employs the knowledge of the relative positions of different particles in generating new solutions. The knowledge of the poor solutions is also utilised to avoid the pairs of adjacent tasks appearing in the poor solutions from being selected as part of new solution strings in the next generation. Much of the effective concept of Pareto optimality is exercised to allow the conflicting objectives to be optimised simultaneously. Experimental results clearly show that PSONK is a competitive and promising algorithm. In addition, when a local search scheme (2-Opt) is embedded into PSONK (called M-PSONK), improved Pareto frontiers (compared to those of PSONK) are attained, but longer computation times are required.     

Assembly line balancing using genetic algorithms

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.     

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.     

A comparative analysis of sequencing heuristics for solving the Toyota Goal Chasing problem

In this paper, the sequencing of a mixed model paced assembly line is investigated assuming the component parts usage smoothing as the goal of the sequence selection. This sequencing problem, commonly known as Toyota Goal Chasing method, is studied here taking into account not only the traditional Goal Chasing approaches, which assume zero-length assembly lines, but also models which consider the effective length of the assembly line. This means that the number of workstations and their extensions become critical parameters in the selection of the optimal sequence of models to be assembled: in fact, the epochs corresponding to the requirement of different components vary in accordance to the values of the line parameters. The sequencing of the parts is carried out here through a set of heuristic procedures, the commonly adopted Goal Chasing algorithms and a simulated annealing, whose performances are compared with respect to different line scenarios. In particular, the numbers of workstations, parts to be worked and components to be assembled are varied to statistically test their influence on the efficiency of the optimizing procedures and on the differences between zero and finite length approaches.     

Web服务器集群的负载均衡中遗传算子的设计

应用遗传算法进行作业调度已被越来越多的学者关注。在Web服务器集群环境中,对于客户端的Web请求分配问题,采用常规的遗传算法进行负载均衡并不总是有效的,好的遗传算子对算法收敛性及收敛到最优解非常重要。基于集群环境中Web请求分配的特点,设计了有针对性的遗传算子,即改进的内外结合交叉算子和主动变异算子。模拟实验结果与分析表明这些算子对Web集群的请求分配是有效的。     

On solving the assembly line worker assignment and balancing problem via beam search

Certain types of manufacturing processes can be modelled by assembly line balancing problems. In this work we deal with a specific assembly line balancing problem that is known as the assembly line worker assignment and balancing problem (ALWABP). This problem appears in settings where tasks must be assigned to workers, and workers to work stations. Task processing times are worker specific, and workers might even be incompatible with certain tasks. The ALWABP was introduced to model assembly lines typical for sheltered work centers for the Disabled.