Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

A Heuristic Method for Two-sided Assembly Line Balancing Problem

A two-sided assembly line is typically found in plants producing large-sized products. Its advantages over a one-sided line and the difficulties faced in two-sided line balancing problems were discussed. A mathematical model for two-ALB problem was suggested. A modification of the “ranked positional weight” method, namely two-ALB RPW for two-ALB problems was developed. Experiments were carried out to verify the performance of the proposed method and the results show that it is effective in solving two-sided assembly line balancing problems.

A Multi-Object Genetic Algorithm for the Assembly Line Balance Optimization in Garment Flexible Job Shop Scheduling

Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is not high,and the production organization is not up to expectations.Aiming at the problem of flexible process route planning in garment workshops,a multi-object genetic algorithm is proposed to solve the assembly line bal-ance optimization problem and minimize the machine adjustment path.The encoding method adopts the object-oriented path representation method,and the initial population is generated by random topology sorting based on an in-degree selection mechanism.The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring.In the iterative process,the bottleneck station is optimized by reasonable process splitting,and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency.The effectiveness and feasibility of the multi-object genetic algorithm are proven by the analysis of clothing cases.Compared with the artificial allocation process,the compilation efficiency of MOGA is increased by more than 15%and completes the optimization of the minimum machine adjustment path.The results are in line with the expected optimization effect.

Optimization of assembly line balancing using genetic algorithm

In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).

An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation.This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem.Hence,this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators,and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction method.In this algorithm,a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules;three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases;and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local optimum.Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

Assembly Unit Resource Balancing Strategy based on Discrete Production Mode

Aiming at the characteristics of obvious block division and strong discreteness in the assembly production mode of electronic products,this paper proposes a composite U-shaped flexible assembly line model,and establishes a multi-objective optimization mathematical model on this basis.According to the characteristics of the model,the improved ranked positional weight(RPW)method is used to adjust the generation process of the initial solution of the genetic algorithm,so that the genetic algorithm can be applied to the block task model.At the same time,the adaptive cross mutation factor is used on the premise that tasks between different blocks are not crossed during cross mutation,which effectively improves the probability of excellent individuals retaining.After that,the algorithm is used to iterate to obtain the optimal solution task assignment.Finally,the algorithm results are compared with actual production data,which verifies the validity and feasibility of the assembly line model for discrete production mode proposed in this paper.

MODEL OF OPERATING AND BALANCING ASSEMBLY LINE IN A MASS CUSTOMIZATION ENVIRONMENT

The successful implementation of mass customization lies on reengineeringtechnology and management methods to organize the production. Especially in assembly phase, variousproduct configurations, due-time penalties and order-driven strategy challenge the traditionaloperation and management of assembly lines. The business features and the operation pattern ofassembly line based on mass customization are analyzed. And the research emphatically studiesvarious technologic factors to improve customer satisfaction and their corresponding implementmethods in operating assembly line. In addition, the models are proposed for operating assembly lineunder dynamic process environment in mass customization. A genetic approach is developed to providethe optimal solution to the models. The effectiveness of the proposed approach is evaluated with anindustrial application.

Assembly Line Balancing Based on Double Chromosome Genetic Algorithm

Aiming at assembly line balancing problem,a double chromosome genetic algorithm(DCGA)is proposed to avoid trapping in local optimum,which is a disadvantage of standard genetic algorithm(SGA).In this algorithm,there are two chromosomes of each individual,and the better one,regarded as dominant chromosome,determines the fitness.Dominant chromosome keeps excellent gene segments to speed up the convergence,and recessive chromosome maintains population diversity to get better global search ability to avoid local optimal solution.When the amounts of chromosomes are equal,the population size of DCGA is half that of SGA,which significantly reduces evolutionary time.Finally,the effectiveness is verified by experiments.

CONCURRENT PRODUCT PORTFOLIO PLANNING AND MIXED PRODUCT ASSEMBLY LINE BALANCING

Reconfigurable products and manufacturing systems have enabled manufacturers to provide ＂cost effective＂ variety to the market. In spite of these new technologies, the expense of manufacturing makes it infeasible to supply all the possible variants to the market for some industries. Therefore, the determination of the right number of product variantsto offer in the product portfolios becomes an important consideration. The product portfolio planning problem had been independently well studied from marketing and engineering perspectives. However, advantages can be gained from using a concurrent marketing and engineering approach. Concurrent product development strategies specifically for reconfigurable products and manufacturing systems can allow manufacturers to select best product portfolios from marketing, product design and manufacturing perspectives. A methodology for the concurrent design of a product portfolio and assembly system is presented. The objective of the concurrent product portfolio planning and assembly system design problem is to obtain the product variants that will make up the product portfolio such that oversupply of optional modules is minimized and the assembly line efficiency is maximized. Explicit design of the assembly system is obtained during the solution of the problem. It is assumed that the demand for optional modules and the assembly times for these modules are known a priori. A genetic algorithm is used in the solution of the problem. The basic premise of this methodology is that the selected product portfolio has a significant impact on the solution of the assembly line balancing problem. An example is used to validate this hypothesis. The example is then further developed to demonstrate how the methodology can be used to obtain the optimal product portfolio. This approach is intended for use by manufacturers during the early design stages of product family design.

Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm

Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered;minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.

Optimization of Energy Resource Management for Assembly Line Balancing Using Adaptive Current Search

This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)—one of the most efficient metaheuristic optimization search techniques. Assembly lines of a specific car factory considered as a case study are balanced by the ACS to optimize their energy resource management. The workload variance of the line is performed as the objective function to be minimized in order to increase the productivity. In this work, the ACS is used to address the number of tasks assigned for each workstation, while the sequence of tasks is assigned by factory. Three real-world assembly line balancing (ALB) problems from a specific car factory are tested. Results obtained by the ACS are compared with those obtained by the genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms. By using the ACS, the productivity can be increased and the energy consumption of the lines can be decreased significantly.

Heuristics for Mixed Model Assembly Line Balancing Problem with Sequencing

The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.

基于改进水波算法的复杂多人共站装配线平衡研究

针对实际装配环境中存在多人协同装配和共站的情况,研究了考虑工序复杂程度与工人能力差异的多人协作共站装配线平衡问题。首先,考虑到装配工序的复杂程度需指派不同规模和类型的工人组,建立了以最小化工作站数量和工人数量为目标函数的多目标优化数学模型;在此基础上,提出一种改进的离散水波优化算法,对算子进行了离散化改造,并设计了基于拓扑排序的编码方案和启发式的解码方案,同时引入扰动个体和路径重连的搜索策略来增加解的多样性,进而提高解的质量;最后,通过求解两类测试用例,以线效率和平滑指数作为性能指标,验证了改进离散水波算法在求解复杂多人共站装配线平衡问题上的可行性与有效性,并将算法应用到动车装配线平衡优化中。

基于线性加权和法的装配线平衡问题求解

针对生产节拍确定条件下以提高装配线平衡程度为目的的装配线平衡问题,将装配线平滑系数和装配线平衡率作为优化目标,考虑装配作业分配、工作站数量等因素,使用线性加权和法,以两个优化目标的优先占比作为权重参数建立单目标装配线平衡优化模型;对遗传算法(Genetic Algorithm,GA)和蚁群(Ant Colony Optimization,ACO)算法的混合算法进行改进,构造新的适应度函数和距离信息矩阵对模型进行求解;最后对经典算例进行数值实验,实验结果与以往算法结果比较,平衡程度改进均值提高了5%,表明改进的模型及算法可以更好地提高装配线的平衡程度,验证了模型及算法的有效性。

基于深度强化学习算法的双边装配线第一类平衡

针对传统优化算法求解双边装配线第一类平衡问题时不能有效利用历史求解经验,难以得到最优解,提出一种深度强化学习求解算法CNN-PPO。设计了CNN-PPO强化学习智能体结构,在近端策略优化算法基础上,引入卷积神经网络增强智能体的数据特征提取能力;根据双边装配线问题特征,定义状态矩阵对双边装配线问题进行描述,并引入标记层辅助智能体进行任务决策;根据问题优化目标设计了奖励函数,结合强化学习在线执行—评价机制,为每次决策选择最优的待分配任务,并通过多个案例测试验证了算法的有效性和稳定性。实验结果表明,所提方法的求解结果具有优越性,59个测试案例中有57个可以达到下界。

用改进的遗传算法解决ALB问题

针对生产装配线平衡问题,提出一种改进的遗传算法。算法采用缩放适应度法、随机普遍取样的选择策略、线性可变的杂交和变异算子。使用PB语言实现了这一应用平台,给出了系统的功能结构图和主要的数据结构,并结合实例给出了ALB-2问题的解决方案。实例对比证明,改进的算法很好地解决了简单遗传算法易早熟的问题,大大改善了简单算法的性能。

考虑人因风险的装配线平衡优化方法

“人本智造”已成为智能制造的新研究方向,日常生产活动需要更多考虑人类工效学(Ergonomics)。目前装配生产线平衡优化主要以提高装配效率为目的,缺乏对装配过程中人类工效学的关注。给出职业重复动作(OCRA)方法详细计算过程,并基于OCRA方法从动作频率、尴尬姿势、体力消耗及额外风险等方面对装配过程的人因风险进行评估,考虑不同作业元素优先关系约束、工作站节拍约束、任务关联性约束和操作者人因风险约束等约束条件,以最小化工作站数为优化目标,建立考虑人因风险的Type-Ι装配线平衡优化模型,进而采用改进的遗传算法作为求解算法,在初始化种群阶段基于优先级规则初始化种群,以提高收敛速度。通过对比算例分析结果,表明提出的模型合理且算法有效。