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).

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.

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.

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.

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.

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.

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.

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.

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.

A scenario relaxation algorithm for finite scenario based robust assembly line balancing

A balancing problem for a mixed model assembly line with uncertain task processmg Ume anO daily model mixed changes is considered, and the objective is to minimize the work variances between stations in the line. For the balancing problem for the scenario-based robust assembly line with a finitely large number of potential scenarios, the direct solution methodology considering all potential scenarios is quite time-consuming. A scenario relaxation algorithm that embeds genetic al- gorithm is developed. This new algorithm guarantees termination at an optimal robust solution with relatively short running time, and makes it possible to solve robust problems with large quantities of potential scenarios. Extensive computational results are reported to show the efficiency and effectiveness of the proposed algorithm.

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.

A Modified Multi-Agent System for Simple Assembly Line Balancing

In this paper, a modified multi-agent system for assembly line balancing is proposed. Each worker in the assembly line is regarded as an agent, and two neighboring agents exchange information about the allocated tasks. To balance the workload, an agent with a smaller workload sends a request message to his/her neighboring agent, who has a larger workload, to exchange tasks between them. Without any centralized control mechanism, each agent behaves to achieve their goal, which is to balance the workload. A tabu list and cooling control are also incorporated. However, the effectiveness of the previous system is limited, and the system depends on problems to be solved. As such, a modified system is proposed. In the proposed system, the cycle time is used when considering the proposal of exchange of allocated tasks instead of the task time allocated to the neighboring workers. Also, in the proposed system, the length of tabu list is determined dynamically based on the current number of possible exchanges, and the best cycle time in the search with cooling at medium speed is recorded for the second search that is finished when the current cycle time reaches the recorded cycle time. The effectiveness of the modified system is investigated by solving problems for various conditions. The results show that the proposed system is effective regardless of the problems that are encountered.

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。

某型复杂工程机械变速箱体复合工艺约束机加工线平衡研究

为解决机加工生产线平衡问题所包含的加工任务刀具需求、机床类型需求、加工方位约束、“紧密”型和“或”型约束等复杂条件,建立了机加工生产线平衡数学模型,并采用含多重筛选机制的粒子群算法进行了求解。首先,建立了满足此复杂实际约束条件的机加工生产线平衡问题数学模型;然后,采用粒子的位置坐标作为粒子群搜索的权重信息,进行了加工任务、集中任务的选取,并设计了多重筛选机制构造启发式任务集生成规则;采用此规则对待分配加工任务进行了多重筛选,以得到可供直接分配的加工任务集合,粒子群算法(PSO)从此集合中依次选取了加工任务,构成了完整的解,并形成了具体的任务分配方案;最后,为提高程序的实用性和可视性,设计了加工任务的甘特图生成模块,通过对某复杂工程机械变速箱体零件的实际案例研究,将简化后的任务信息代入算法进行了求解。研究结果表明:该方法实现了多组平衡率高于90%的优化结果,在节拍时间为1120 s时,得到94.66%的较高平衡率,排产方案表格内容与甘特图显示一致;算法推演结果满足设定的多种复合约束条件,通过与人工排产对比说明了该算法的有效性并具有较好的经济性、实用性;对柔性生产案例进行探讨,证明该算法运算结果具备一定的生产柔性。

基于改进人工蜂群算法的人机协作装配线平衡优化

随着员工成本的增加,协作机器人逐步应用于装配线以独立完成或者协助员工完成装配操作。针对人机协作装配线平衡问题,构建了混合整数规划模型以优化生产节拍,该模型可精确求解小规模案例,同时提出改进人工蜂群算法求解大规模问题。该算法采用操作排序向量和装配模式向量双层编码方式以及有效的解码获得可行的调度方案,并在改进的观察蜂阶段保留较优的种群,在改进的侦察蜂阶段提升新解的性能,在局部搜索阶段提高算法的局部搜索能力。最后,通过与已有算法对比,表明改进人工蜂群算法具有优越性,可以高效求解人机协作装配线平衡问题。

基于改进候鸟迁徙算法的人机协作装配线平衡优化

随着员工成本的增加,协作机器人逐步应用于装配线以独立完成或者协助员工完成装配操作。针对多种机器人和并行协作的人机协作装配线平衡问题,构建了两个混合整数规划模型以优化生产节拍。模型均可实现小规模案例的精确求解,并且改进后的模型在求解时间上明显优于改进前的模型。同时,提出了改进的候鸟迁徙算法以实现大规模问题的求解。该算法采取任务分配向量和装配模式向量双层编码方式,并在解码中采取数学规划方法获得可行的调度方案。改进的候鸟迁徙算法采取改进的领鸟进化阶段和改进的个体进化阶段以加速种群的进化并防止陷入局部最优,采取重启机制增强算法的探索能力。为了测试改进算法的性能,该算法与延迟接受爬山算法、模拟退火算法、遗传算法、离散粒子群算法、原始人工蜂群算法和原始候鸟迁徙算法进行对比,测试结果表明改进算法优于对比算法,可实现人机协作装配线平衡问题的高效求解。

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。