A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.     

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.     

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.     

Incorporating Physical Demand Criteria into Assembly Line Balancing

Many assembly line balancing algorithms consider only task precedence and duration when minimizing cycle time. However, disregarding the physical demands of these tasks may contribute to the development of work-related musculoskeletal disorders in the assembly line workers. Three line balancing heuristics that incorporate physical demand criteria were developed to solve the problem of finding assembly line balances that consider both the time and physical demands of the assembly tasks: a ranking heuristic, a combinatorial genetic algorithm, and a problem space genetic algorithm. Each heuristic was tested using 100 assembly line balancing problems. Incorporating physical demands using these algorithms does impact the assembly line configuration. Results indicated that the problem space genetic algorithm was the most adept at finding line balances that minimized cycle time and physical workload placed upon participants. Benefits of using this approach in manufacturing environments are discussed.     

Integrated assembly line balancing with resource restrictions

This paper documents a study carried out on the problem of designing an integrated assembly line when many workers with a variety of skills are employed. This study addresses the problem of selecting multi-functional workers with different salaries to match their skills and of assigning tasks to work stations when there are precedence restrictions among the tasks. The objective of this study is to minimise the total annual work station costs and the annual salary of the assigned workers within a predetermined cycle time. A mixed integer linear program is developed with a genetic algorithm in order to address the problem of resource restrictions related to integrated assembly line balancing. Numerical examples demonstrate the efficiency of the developed genetic algorithm.     

ASALBP: the alternative subgraphs assembly line balancing problem

Assembly line balancing problems basically consist of assigning a set of tasks to a group of workstations while maintaining the tasks’ precedence relations, which are represented by a predetermined precedence graph. However, one or more parts of a product's assembly process may admit alternative precedence subgraphs, which represent possible assembly variants. In general, because of the great difficulty of the problem and the impossibility of representing alternative subgraphs in a precedence graph, the system designer will decide to select, a priori, one of such alternative subgraphs. This paper presents, characterizes and formulates a new general assembly line balancing problem with practical relevance: the alternative subgraphs assembly line balancing problem (ASALBP). Its novel characteristic is that it considers the possibility of having alternative assembly subgraphs, with the processing times and/or the precedence relations of certain tasks dependent on the assembly subgraph selected. Therefore, solving this problem implies simultaneously selecting an assembly subgraph for each part of the assembly that allows alternatives and balancing the line. The potentially positive effects of this on the solution of the problem are shown in a numerical example. Finally, a simple mathematical programming model is described and the results of a brief computational experiment are presented.     

Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, each ACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.     

Using Ant Techniques to Solve the Assembly Line Balancing Problem

This paper presents an approach, based on ant techniques, to effectively address the assembly line balancing problem with the complicating factors of parallel workstations, stochastic task durations, and mixed-models. A methodology was inspired by the behavior of social insects in an attempt to distribute tasks among workers so that strategic performance measures are optimized. This methodology is used to address several assembly line balancing problems from the literature. The assembly line layouts obtained from these solutions are used for simulated production runs so that output performance measures (such as cycle time performance) are obtained. Output performance measures resulting from this approach are compared to output performance measures obtained from several other heuristics, such as simulated annealing. A comparison shows that the ant approach is competitive with the other heuristic methods in terms of these performance measures.     

多类约束下U型装配线平衡建模研究

现有针对U型装配线平衡问题的研究假设除了节拍约束和任务间的优先顺序关系外,不存在其它约束,实际上受生产环境、产品设计、工艺要求以及人因等多种因素的制约,在对U型装配线进行优化设计时还需满足其它约束,将各种表象不同的约束抽象为相连、相斥、相关以及工作站属性约束四类,针对多类约束下U型装配线平衡问题建立了整数规划模型,通过...     

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.     

Mixed model assembly system with multiple secondary feeder lines: layout design and balancing procedure for ATO environment

The constant research for efficiency and flexibility has forced assembly systems to change from simple/single assembly lines to mixed model assembly lines, while the necessity to reduce inventory has led the transition from single to multi-line systems, where some components are assembled in secondary lines, called feeder lines, connected to the main one by a ‘pull philosophy’. A possible approach to configure such an assembly system is to balance the main line first and use the retrieved cycle time to balance each feeder line separately, which is a questionable solution, especially if operators can perform tasks on both the feeder and the main line. Moreover for its complexity the mixed model balancing problem is usually solved transforming it into a single model by creating a single ‘virtual average model’, representative of the whole production mix. The use of a virtual average model assumes that the processing times of some models are higher or lower than the cycle time, which creates overload/idle time at the stations. This approach, especially in complex multi line production systems, largely reduces the assembly line productivity and increases the buffers dimensions. This paper faces the mixed model assembly line balancing problem in the presence of multiple feeder lines, introducing an innovative integrated main-feeder lines balancing procedure in case of unpaced assembly systems. The proposed approach is compared with the classical one and validated through simulation and industrial applications.     

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.     

Task proximity index: a novel measure for assessing the work-efficiency of assembly line balancing configurations

Assembly line balancing (ALB) aims at optimally partitioning the total work required to assemble a product to workstations. In this problem, precedence constraints must be observed on the sequence with which the distinct tasks of the assembly process may be carried out. In contrast to the strict mathematical posture employed in conventional ALB approaches, this paper provides a deep insight on the origin of precedence diagram which illustrates precedence constraints among the tasks. Our review on assembly planning methods reveals that these constraints are deduced from the geometry of the components to be attached, their position in the assembled product and even planner's expertise on the rational order with which the tasks are to be performed. Accordingly, it is explicitly shown through a case study of a real industrial product that the topology of tasks on the precedence diagram may be utilised to obtain more work-efficient ALB configurations. To this end, a metric system is developed to quantify the proximity of the tasks on the precedence diagram and a novel measure, namely the task proximity index, is proposed for assessing ALB solutions from a practical viewpoint.     

Improving the equality of workload assignments in assembly lines

Most heuristic and optimal methods for assembly line balancing are designed either to minimize the number of workstations required given a production rate, or to maximize the production rate given a fixed number of workstations. This singular focus on line design efficiency typically results in an imbalanced allocation of tasks among the stations. This paper presents a new procedure that rebalances a given solution to level the allocation of tasks. Characteristics of dominant levelled solutions are identified and incorporated into this heuristic procedure. Computational results indicate that the procedure is very effective in levelling workloads.     

LB-ALBP: the lexicographic bottleneck assembly line balancing problem

The classic assembly line balancing problem (ALBP) basically consists of assigning a set of tasks to a group of workstations while maintaining the tasks’ precedence relations. When the objective is to minimise the number of workstations m for a given cycle time CT, the problem is referred to as ALBP-1; if the objective is to minimise CT given m, then the problem is called ALBP-2. The only objective in ALBP-2 is to minimise CT, i.e., the workload of the most heavily loaded workstation (the bottleneck). However, considering the second-biggest, third-biggest, etc. workloads, can be important. Distributing a workload among six workstations as 10, 10, 10, 4, 3, 3, is not the same as distributing it as 10, 6, 6, 6, 6, 6. The CT value is the same, but the second distribution is beyond question more reliable and balanced. In this paper, we present and formalise a new assembly line balancing problem: the lexicographic bottleneck assembly line balancing problem (LB-ALBP). The LB-ALBP hierarchically minimises the workload of the most heavily loaded workstation (CT), followed by the workload of the second most heavily loaded workstation, followed by the workload of the third most heavily loaded workstation, and so on. We present two mixed-integer linear programming (MILP) models designed to solve the LB-ALBP optimally, together with three heuristic procedures based on these MILPs.     

Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS

In this paper, we propose a multi-objective differential evolution algorithm (MODEA) to solve the multi-objective simple assembly line balancing problem type-2 (SALBP-2). This problem arises when in an existing assembly line, changes in the production process or demand structure take place and the organisation wants to produce the optimum number of items using a fixed number of workstations, which is associated with optimally assigning the tasks to an ordered sequence of stations such that the precedence relations are not violated and some measures of performance are optimised. The two considered objectives are: minimising the cycle time and the smoothness index of the assembly line. To that purpose, we develop a MODEA which unlike the existing algorithms deals with the considered objectives separately in selecting the next population members by proposing a new acceptance scheme based on the Pareto dominance concept and a new evaluation scheme based on TOPSIS. Also, by using the Taguchi method, we tune the effective factors of the developed algorithm. Then its efficiency is tested over available assembly line balancing benchmarks and compared to a new algorithm provided recently in the bi-objective SALBP-2 literature. Computational experiments indicate that the developed algorithm outperforms the existing meta-heuristic over a large group of benchmarks.     

Sequencing procedure for balancing the workloads variations in case of mixed model assembly system with multiple secondary feeder lines

In order to increase flexibility and reduce costs, assembly systems are changing from simple mixed-model assembly lines, to multi-lines systems, where some components are assembled in secondary lines, called feeder lines, connected to the main line by a ‘pull philosophy’. The production of different models in such a complex multi-lines assembly system, where the tasks to perform could be very different from model to model, impacts the production with very high workload time variations, with the consequence of lack of productivity. If operators can perform tasks on different stations on both the feeder and the main lines these time variations can generally be absorbed. But if working across the stations is not possible (closed stations), the balancing of these workload time variations becomes critical. In such contexts the consolidated approach, that allows to configure mixed-model-multi lines assembly system with a single average model representative of the whole mix, can be very limited and is unable to bring substantial results. This paper aims to introduce an innovative sequencing model for mixed-model-multi-lines system, in the case of closed stations, in which it is possible to obtain, after a first long term configuration, a short term balancing, using an appropriate sequencing, for a given production mix and characteristics of the assembly system. The proposed procedure is applied to un-paced assembly lines and validated through simulation and industrial applications.     

Balancing Multi-Manned Assembly Lines With Walking Workers: Problem Definition,Mathematical Formulation,and an Electromagnetic Field Optimisation Algorithm

Assembly lines are widely used in industrial environments that produce standardised products in high volumes. Multi-manned assembly line is a special version of them that allows simultaneous operation of more than one worker at the same workstation. These lines are widely used in large-sized product manufacturing since they have many advantages over the simple one. This article has dealt with multi-manned assembly line balancing problem with walking workers for minimising the number of workers and workstations as the first and second objectives, respectively. A linear mixed-integer programming formulation of the problem has been firstly addressed after the problem definition is given. Besides that, a metaheuristic based on electromagnetic field optimisation algorithm has been improved. In addition to the classical electromagnetic field optimisation algorithm, a regeneration strategy has been applied to enhance diversification. A particle swarm optimisation algorithm from assembly line balancing literature has been modified to compare with the proposed algorithm. A group of test instances from many precedence diagrams were generated for evaluating the performances of all solution methods. Deviations from lower bound values of the number of workers/workstations and the number of optimal solutions obtained by these methods are concerned as performance criteria. The results obtained by the proposed programming formulations have been also compared with the solutions obtained by the traditional mathematical model of the multi-manned assembly line. Through the experimental results, the performance of the metaheuristic has been found very satisfactory according to the number of obtained optimal solutions and deviations from lower bound values.     

Solving Large Single-Model Assembly Line Balancing Problems—A Comparative Study

A comparative study is made between three methods for balancing single-model assembly lines. These include: 10-SP, which selects the best of ten single-pass solutions; ARCUS—a random multi-pass method, and MALB, an iterative backtracking method. The assembly tasks contain up to 140 work elements with widely differing precedence structures. Measurements of the Balance Delay are used for comparing the solution efficiencies and computation times are also compared. The experiments show significant differences between the three line balancing methods and that these differences are accentuated for certain conditions of task size, precedence structure and the imposed balancing requirements. It is shown that 10-SP is yet another simple but effective technique for balancing single-model assembly lines.