Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.     

A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study

The assembly line worker assignment and balancing problem type-II (ALWABP-2) occurs when workers and tasks (where task times depend on workers’ skills) are to be simultaneously assigned to a fixed number of workstations with the goal of minimising the cycle time. In this study, a two-phase variable neighbourhood search (VNS) algorithm is proposed to solve the ALWABP-2 due to the NP-hard nature of this problem. In the first phase of the algorithm, a VNS approach is applied to assign tasks to workstations with the aim of minimising the cycle time while in the second phase, a variable neighbourhood descent method is applied to assign workers to workstations. The performance of the proposed algorithm is tested on well-known benchmark instances. In addition, the proposed algorithm has been used to solve a real case study from a consumer electronics company that manufactures LCD TVs. The results show that the algorithm is superior to the methods reported in the literature in terms of its higher efficiency and robustness. Furthermore, the algorithm is easy to implement and significantly improves the performance of the final assembly line for the investigated LCD TV real case study.     

Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach

A mixed-model assembly line is a type of production line which is used to assemble a variety of product models with a certain level of similarity in operational characteristics. This variety causes workload variance among other problems resulting in low efficiency and line stops. To cope with these problems, a hierarchical design procedure for line balancing and model sequencing is proposed. It is structured in terms of an amelioration procedure. On the basis of our evolutionary algorithm, a genetic encoding procedure entitled priority-based multi-chromosome (PMC) is proposed. It features a multi-functional chromosome and provides efficient representation of task assignment to workstations and model sequencing. The lean production perspective recognises the U-shape assembly line system as more advanced and beneficial compared to the traditional straight line system. To assure the effectiveness of the proposed procedure, both straight and U-shape assembly lines are examined under two major performance criteria, i.e., number of workstations (or line efficiency) as static criterion and variance of workload (line and models) as dynamic criterion. The results of simulation experiments suggest that the proposed procedure is an effective management tool of a mixed-model assembly line system.     

ANTBAL: an ant colony optimization algorithm for balancing mixed-model assembly lines with parallel workstations

This paper presents ANTBAL, an ant colony optimization algorithm for balancing mixed-model assembly lines. The proposed algorithm accounts for zoning constraints and parallel workstations and aims to minimize the number of operators in the assembly line for a given cycle time. In addition to this goal, ANTBAL looks for solutions that smooth the workload among workstations, which is an important aspect to account for in balancing mixed-model assembly lines. Computational experience shows the superior performance of the proposed algorithm.     

缓冲区容量约束下发动机混流装配线排序研究

为解决缓冲区容量约束下发动机混流装配排序问题,以关键部件消耗均匀化和最大完工时间最小化为目标,建立了优化数学模型,设计了一种多目标遗传算法,采用了混合交叉算子和启发式变异方法,并设计了基于帕累托分级和共享函数的适应度函数,将多目标遗传算法和多目标模拟退火算法的优化结果进行了比较。研究结果表明,多目标遗传算法在满意度和计算效率方面均优于多目标模拟退火算法,是一种有效的混流装配线排序问题求解算法。     

Sustainable operator assignment in an assembly line using genetic algorithm

This paper addresses the operator assignment in predefined workstations of an assembly line to get a sustainable result of fitness function of cycle time, total idle time and output where genetic algorithm is used as a solving tool. A proper operator assignment is important to get a sustainable balanced line. To improve the efficiency and meet the desired target output within the time limit, a balanced assembly line is a must. Real world lines consist of a large number of tasks and it is very time consuming and crucial to choose the most suitable operator for a particular workstation. In addition, it is very important to assign the suitable operator at the right place as his skill of operating machines finally reflects in productivity or in the cost of production. To verify better assignments of workers, a genetic algorithm is adopted here. A heuristic is proposed to find out the sustainable assignment of operators in the predefined workstations.     

随机型双边装配线平衡问题的一种启发式算法

为有效求解随机型双边装配线第Ⅰ类平衡问题(STALBP 1),在分析双边装配线平衡特点的基础上,考虑各任务操作时间的随机性,提出了一种启发式算法。在该启发式算法中,假定各任务的操作时间服从正态分布,运用具有操作方位约束的任务优先分配等规则来进行任务的选择和分配,通过改变预设超限概率,在不同生产节拍下,分别得到不同的平衡方案。实例验证了算法的有效性。     

大规模带复杂任务约束的装配线平衡问题优化

介绍了装配线平衡问题的传统模型,分析了传统启发式算法与遗传算法在解决现今生产中的大规模带复杂任务约束问题时的弊端.针对传统模型的局限性,给出了修正模型,然后集合数种组合优化算法的优点,对传统启发式算法的候选规则与任务分配规则进行改进,给出了一种可行、高效率的优化算法,最后用实例验证了算法的优良性能.     

A two-stage heuristic method for balancing mixed-model assembly lines with parallel workstations

This work presents a new mathematical programming model for the mixed-model assembly line balancing problem with parallel workstations and zoning constraints. It allows the user to control the process to create parallel workstations. The model's primary goal is to minimize the number of workstations along the line, for a given cycle time, and its secondary goal is to balance the workloads between and within workstations. A two-stage procedure, using a simulated annealing approach, was developed to tackle this complex problem. The first stage of the procedure looks for a sub-optimal solution to the problem's primary goal, whilst the second stage deals with the secondary goal. The procedure is illustrated with a numerical example and the results from computational experiments show that even for large-scale problems the proposed procedure performs very well.     

An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times

Some practical arrangements in assembly lines necessitate set-up times between consecutive tasks. To create more realistic models of operations, set-up times must be considered. In this study, a sequence-dependent set-up times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included set-up times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent set-up times between consecutive tasks and consecutive cycles, called the “forward set-up time” and the “backward set-up time”, respectively. Additionally, we examine the “crossover set-up time”, which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent set-up times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.     

Design of flexible assembly line to minimize equipment cost

In this paper we develop an optimal and a heuristic algorithm for the problem of designing a flexible assembly line when several equipment alternatives are available. The design problem addresses the questions of selecting the equipment and assigning tasks to workstations, when precedence constraints exist among tasks. The objective is to minimize total equipment costs, given a predetermined cycle time (derived from the required production rate). We develop an exact branch and bound algorithm which is capable of solving practical problems of moderate size. The algorithm's efficiency is enhanced due to the development of good lower bounds, as well as the use of some dominance rules to reduce the size of the branch and bound tree. We also suggest the use of a branch-and-bound-based heuristic procedure for large problems, and analyze the design and performance of this heuristic.     

U-shaped assembly line balancing problem with genetic algorithm

This paper presents a multi-objective genetic algorithm (moGA) to solve the U-shaped assembly line balancing problem (UALBP). As a consequence of introducing the just-in-time (JIT) production principle, it has been recognized that U-shaped assembly line systems offer several benefits over the traditional straight line systems. We consider both the traditional straight line system and the U-shaped assembly line system, thus as an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.     

考虑效率差异和流动分配的混流装配线工人优化

本文研究了考虑工人装配水平差异、不同工位之间可以流动分配的装配线工人优化调度，在确定混流装配线的排序后，装配工人在其最擅长的工位工作，在完成本工位的工作后参与其他工位的装配工作，定义相关的优化因素变量和约束条件，建立了考虑工人工位效率差异且可流动分配的混流装配线工人优化数学模型。采用削峰填谷算法和多种群移民算法进行工人的调整和均衡优化，利用一个三层神经网络对削峰填谷的工位进行评估选择，得到工人的最优分配方案，最后通过Matlab对举例进行了验证。算例优化后，减少了2名装配工人，并提高了装配线的平衡率。     

An integrated model for assembly line re-balancing problem

In this paper, an integrated approach for assembly line rebalancing problem (IALRP) is proposed to quickly react and find an optimal rebalancing of the line when disruptive event occurs because of product demand changes. This model is motivated by real-life application of an automotive cable manufacturer which provides more realistic constraints. To solve the problem, we propose a genetic algorithm (GA) hybridised with a heuristic priority rule-based procedure. This hybridisation is used to add more rich seeds to the initial population and consequently to improve the convergence capability and performance of the GA. After the disturbance, we aim to find a rebalance with the proposed approach to maximise the line efficiency and distributing the idle time across the workstations as equally as possible. To evaluate the efficiency of the proposed algorithm, set of samples collected from the literature are used. The real case study and the experiment results show the proposed approach is very effective and competitive.     

A Petri net-based heuristic for mixed-model assembly line balancing problem of Type-E

To effectively respond to the changing market demands, a manufacturer should produce variety of products with small lots. Thus, multiple products (models) are assembled simultaneously on a same line. However, it is very challenging to balance such an assembly line. This paper conducts a study on balancing a mixed-model assembly line of Type E. To solve this problem, a coloured-timed Petri net model is developed to describe the task precedence relationship. Also, the optimisation problem is formulated as a mathematical programming model. Then, with the models, a two-stage heuristic algorithm is proposed to solve the problem. At the first stage, based on the Petri net model, a P-invariant algorithm (PA) is presented to minimise the number of workstations. At the second stage, a heuristic is proposed to further minimise the cycle time by combining the PA with a binary search algorithm (BSA). Performance of the proposed method is evaluated by an illustrative example and numerical experiments. It is shown that it works well in terms of both solution accuracy and computational efficiency for large size problems.     

MIP approach to balancing transfer lines with blocks of parallel operations

A novel line balancing problem is considered. It differs from assembly line balancing problems in that the operations of each workstation are partitioned into blocks of simultaneously executed (parallel) operations. The blocks of each workstation are executed sequentially. For the line design stage considered in this paper, the compatibility (inclusion and exclusion) constraints for grouping operations into blocks and workstations as well as precedence constraints are known. The goal is to minimize a weighted sum of the number of workstations and the number of blocks while achieving a desired cycle time and satisfying all the constraints. The developed exact and heuristic methods are based on a mixed-integer programming approach. Experimental results are reported.     

Two-sided U-type assembly line balancing problem

U-type and two-sided assembly lines are two types of design having advantages over traditional straight assembly lines. In this paper, a new line design hybrid of U-type and two-sided lines is presented. A bi-objective 0-1 integer programming model is developed to solve the line balancing problem of the proposed design. Zoning constraints are also considered for the proposed design. A number of test problems from the literature with up to 65 tasks are solved. Benefits of two-sided U-type lines are discussed.     

Simulated annealing algorithms for the multi-manned assembly line balancing problem: minimising cycle time

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time. A mixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time.     

An optimisation support for the design of hybrid production lines including assembly and disassembly tasks

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system.