Mixed-model Assembly Line Balancing Problem Research Articles

Matheuristics for mixed-model assembly line balancing problem with fuzzy stochastic processing time

Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, we model task processing time as fuzzy stochastic variables (FRVs) due to the inherent uncertainties and variations in the manufacturing environment. Additionally, we introduce a ranking method for FRVs and propose a mathematical model to address MALBP. The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solving this problem. Finally, matheuristic algorithms combine a metaheuristic such as the popular Genetic Algorithm (GA), Particle Swarm Optimization (PSO), or the Discretized Red Fox Optimization (DRFO) algorithm with the Mixed-Integer Programming (MIP) model to generate the best solution in a reasonable time. Our comparative results demonstrate that the GA-MIP combination outperforms the others in both objective value and computational time.

Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

Hierarchical and two-stage framework for the paced mixed-model assembly line balancing and sequencing problem considering ergonomic risk

Paced mixed-model assembly lines are popular with various manufacturing enterprises. However, they face the weakness that they are at risk of line stoppages owing to the occurrence of workstation work overload situations. Moreover, the consideration of workers' health and ergonomic risk on manual assembly lines is a necessity required by legislation. Therefore, this article addresses mixed-model assembly line balancing and sequencing taking the problem of ergonomic risk into consideration and manages work overloads using a side-by-side policy with utility workers. To solve these problems, this article proposes an hierarchical and two-stage framework. The optimization goals are to minimize the number of workstations and utility workers. Furthermore, this article integrates an iterated greedy algorithm into a genetics algorithm to obtain global exploration and local exploitation ability. Finally, a divide-and-conquer strategy is proposed to meet the challenge of solving a large-scale problem. Experimental results show the effectiveness of the mechanism proposed in this article.

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

A linear physical programming model for assembly line balancing problem

This paper deals with the mixed-model assembly line balancing problem. This type of line is applied to more than one similar model of a product in an intermixed order. Despite their widespread use, these lines have received little attention in the literature. Metaheuristics, heuristics and mathematical programming techniques are developed to solve these types of assembly line balancing problems. However, linear physical programming method has never used. In this paper, a linear physical programming model is proposed for balancing a mixed-model assembly line. The performance of the proposed model is applied to a numerical example to analyze the usage of the methodology. Five objectives are considered in the model and the outperformance of the methodology is demonstrated by comparing it to a different approach. According to the results, it has been seen that the proposed linear physical programming model is practical and useful approach for mixed-model assembly line balancing problems.

Examining Rebalancing Considers of the Mixed-Model Assembly Line Type-II for the Straight and U-shaped Layout Using the Taguchi Method

Facing the increasingly competitive market environment, assembly lines are gained attention because of the assembly process expenditure consumes more than 20 % of the manufacturing cost, and also improve its products to meet demand. In this paper, an attempt has been made to develop worker-task assigned to workstation heuristic (W-TAWH) approach for solving mixed-model assembly line balancing problem (M-MALBP), in which the assembly tasks have to be assigned to workstations, and each workstation needs to select only one of the available operators to perform the assigned tasks corresponding to the straight and U-shaped assembly lines with the objective minimum cycle time. The performance of the developed approach was evaluated and validated through applying the practical case in the Dayla State Company for Electrical Industries (DSCEI) particularly in the electric transformer factory (ETF). Taguchi method was used to evaluate the contribution of each one of the considerations in the assembly line are solution approach, layout, sequence vector (SV), and workstation numbers. All results proved the stated considerations have impact significant in any trying to re-balance the assembly line.

Solving Mixed-Model Assembly Lines Using a Hybrid of Ant Colony Optimization and Greedy Algorithm

The assembly line balancing problem deals with the assignment of tasks to work stations. Mixed-model assembly line problem is a type of assembly line balancing problem at which two or more models of the same product are assembled sequentially at the same line. To achieve optimality and efficiency of solving this problem, tasks at each work station have to be well balanced satisfying all constraints. This paper deals with the mixed-model assembly line balancing problem (MALBP) in which the objective is to minimize the cycle time for a given number of work stations. The problem is solved by using a hybrid of an ant colony optimization and a greedy algorithm (Ant-Greedy). MATLAB Software is used to perform the proposed method. Then, the proposed method is applied to a real case problem found in the literature for the assembly line of automatic changeover in the Electronic Industries Company in Iraq. The results of the proposed method are compared with the performance of the Merging Shortest and Longest Operation (MMSLO) method. The comparison shows that the Ant-Greedy optimization method is more efficient, where the efficiency increased from 93.53% for MMSLO method to 97.26% for the Ant-Greedy method.

Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem which requires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP is conducted using four popular metaheuristics for this problem, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Three categories of test problem (small, medium, and large) were used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the metaheuristic algorithms performances to optimize the designated objective functions. The results reveal that ACO algorithm performed better in term of finding the best fitness functions when dealing with a large number of tasks. Averagely, it has produces better solution quality than PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO more superior in term of processing time compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%. Hence, future research directions such as using the actual manufacturing assembly line data to test the algorithm performances are likely to happen.

Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human–robot collaboration

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms.

Mixed-Model Assembly Line Balancing in The Process of Assembling Trimming Area to Minimize Workstation Using RPW-MVM Method

The development of an industry that continues to move forward coupled with global competition and openness demands that the company continues to evolve and always make improvements in improving the performance of its production process. XYZ Corp is an automotive company based in Germany that produces premium cars, the car assembly process groove at XYZ Corp is a trimming line, mechanical line and finishing line, XYZ Corp produces A-model, B-model and C-model cars. At this time XYZ Corp not achieving the production target due to the car assembly cycle time on the trimming Line 1 area exceeds the specified takt time. Assembly line balancing is required in the trimming area using Mixed-Model Assembly Line Balancing Problem (MALBP) approach to minimize the number of workstation, in the Trimming area assembly line balancing study using the Ranked Positional Weighted with Moving Target (RPW-MVM) method. Alocation constraint should be added due to machine restrictions that cannot be moved. After assembly line balancing, there was a decrease in the number of workstations to 14 workstations with a line efficiency of 86% and balancing efficiency of 97%.

Recent trend in mixed-model assembly line balancing optimization using soft computing approaches

Purpose This paper aims to review and discuss four aspects of mixed-model assembly line balancing (MMALB) problem mainly on the optimization angle. MMALB is a non-deterministic polynomial-time hard problem which requires an effective algorithm for solution. This problem has attracted a number of research fields: manufacturing, mathematics and computer science. Design/methodology/approach This paper review 59 published research works on MMALB from indexed journal. The review includes MMALB problem varieties, optimization algorithm, objective function and constraints in the problem. Findings Based on research trend, this topic is still growing with the highest publication number observed in 2016 and 2017. The review indicated that the future research direction should focus on human factors and sustainable issues in the problem modeling. As the assembly cost becomes crucial, resource utilization in the assembly line should also be considered. Apart from that, the growth of new optimization algorithms is predicted to influence the MMALB optimization, which currently relies on well-established algorithms. Originality/value The originality of this paper is on the research trend in MMALB. It provides the future direction for the researchers in this field.

Balancing mixed-model assembly systems in the footwear industry with a variable neighbourhood descent method

This paper addresses new Mixed-model Assembly Line Balancing Problems (MALBP) in a real industrial context, the stitching systems of a footwear company. The work is part of large ongoing projects with this industry, and the main purposes are minimising the number of required workstations and smoothing the operators’ workload. The company has invested in new flexible automated assembly systems, which accommodate dozens of workstations and many moving boxes. Footwear components are inside boxes (with various quantities) which can move from the warehouses to a convenient workstation or between any workstations (in any order). This is a significant and distinct feature of the MALBP, together with the fact that the assignment of different skilled operators and machines is achieved simultaneously. An optimisation model is developed, in part to facilitate the understanding of the situation and to solve small-size instances. Due to the complexity of the problems, we had to devise an approximate method, based on the Variable Neighbourhood Descent (VND) metaheuristic and integrating an adaptation of the Ranked Positional Weighted (RPW) method. The adapted RPW method is used to create initial feasible solutions, while preassigning special operators and machines. After choosing good initial solutions, VND is applied to improve their quality. The new contributed method, named as RPW-VNDbal, is tested with medium and large instances, in two distinct stitching systems. A Lower Bound of the objective function and Simulation contribute to evaluate the solutions and their practicability. The results implemented by the project team, show that the RPW-VNDbal method is fast enough and offers better solutions than those implemented by the experienced operation managers of the company.

MILP models and metaheuristic for balancing and sequencing of mixed-model two-sided assembly lines

Mixed-model assembly lines are becoming increasingly popular due to flexibility of producing customised products. In a mixed-model assembly line, line balancing and model sequencing problems are tightly interrelated and very important for efficiency. This paper proposes a new assembly line configuration based on paced mixed-model two-sided assembly lines where balancing and sequencing problems are considered simultaneously. Minimal work has been reported considering both problems simultaneously for this type of assembly line configuration. Two mixed-integer linear programming (MILP) models are developed and a restarted SA algorithm with new encoding, decoding and neighbourhood procedures is developed. The parameters of the proposed algorithm are selected based on a statistical technique and the performance of it is tested on a set of new benchmark problems. The computational results demonstrate the effectiveness of the MILP models and the high efficiency of the proposed algorithm. The proposed algorithm outperforms the comparative original SA algorithm. [Received 22 July 2016; Revised 1 December 2016; Accepted 14 February 2017]

Combinatorial Benders cuts for assembly line balancing problems with setups

The classical assembly line balancing problem consists of assigning assembly work to workstations. In the presence of setup times that depend on the sequence of tasks assigned to each workstation, the problem becomes more complicated given that two interdependent problems, namely assignment and sequencing, must be solved simultaneously. The hierarchical nature of these two problems also suggest a natural decomposition of the problem. This paper adopts such an approach and describes an exact algorithm based on Benders decomposition to solve both simple and mixed-model assembly line balancing problems with setups. The algorithm is tested on a set of benchmark instances and numerically compared against a mixed-integer linear programming formulation of the problem solved using a commercial optimizer.

Optimization of Cycle Time in an Assembly Line Balancing Problem

Assembly line is a sequential work flow production systems which are still typical in the mass production of standard products. In mixed model assembly line system, it can produce the production sequentially by mixing more than one product on the same line. Different range of products are produced on the same line are quiet similar to the main productThis paper proposes an approach to the mixed model assembly line balancing problem (MALBP) with parallel workstations. Different methods are developed to solve assembly line balancing problems. The existing methods to solve assembly line balancing problem cannot be applied to combinatorial type problems. When we use heuristics methods for a combinatorial type problem which has more number of work elements, it may not give an optimal solution and become tedious. So this paper is aimed to propose a method to solve such type of complex line balancing problems.

Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem

This paper deals with mixed-model assembly line balancing and worker assignment problem (MMALBWAP). Mixed-model assembly lines allow the simultaneous assemble of a set of products on a single assembly line and have achieved great attention during last decades. The worker assignment problem deals with assigning workers to workstations considering their abilities and operating costs. The proposed model in this paper considers two incoherent objectives. The first objective aims to minimize the total cycle time. From one side different models of product have different operating task times and on the other hand different worker skills make more varieties in operating times, therefore minimizing cycle time in these problems seems so important. Simultaneous with cycle time the operating costs related to workers is the second objective of interest to be minimized. In order to solve this multi-objective problem a goal programming approach is utilized and because of high complexity of the problem, an evolutionary algorithm named imperialist competitive algorithm (ICA) is developed. In order to evaluate the efficiency of proposed algorithm, the experimental results obtained are compared with a genetic algorithm.

Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times

The buffer allocation problem (BAP) and the assembly line balancing problem (ALBP) are amongst the most studied problems in the literature on production systems. However they have been so far approached separately, although they are closely interrelated. This paper for the first time considers these two problems simultaneously. An innovative approach, consisting in coupling the most recent advances of simulation techniques with a genetic algorithm approach, is presented to solve a very complex problem: the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times, parallel workstations, and buffers between workstations. An opportune chromosomal representation allows the solutions space to be explored very efficiently, varying simultaneously task assignments and buffer capacities among workstations. A parametric simulator has been used to calculate the objective function of each individual, evaluating at the same time the effect of task assignment and buffer allocation decisions on the line throughput. The results of extensive experimentation demonstrate that using buffers can improve line efficiency. Even when considering a cost per unit buffer space, it is often possible to find solutions that provide higher throughput than for the case without buffers, and at the same time have a lower design cost.

Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times

In the paper, an innovative approach to deal with the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times and parallel workstations is presented. At the current stage of research, advances in solving realistic and complex assembly line balancing problem, as the one analyzed, are often limited by the poor capability to effectively evaluate the line throughput. Although algorithms are potentially able to consider many features of realistic problems and to effectively explore the solution space, a lack of precision in their objective function evaluation (which usually includes a performance parameter, as the throughput) limits in fact their capability to find good solutions. Traditionally, algorithms use indirect measures of throughput (such as workload smoothness), that are easy to calculate, but whose correlation with the throughput is often poor, especially when the complexity of the problem increases. Algorithms are thus substantially driven towards wrong objectives. The aim of this paper is to show how a decisive step forward can be done in this filed by coupling the most recent advances of simulation techniques with a genetic algorithm approach. A parametric simulator, developed under the event/object oriented paradigm, has been embedded in a genetic algorithm for the evaluation of the objective function, which contains the simulated throughput. The results of an ample simulation study, in which the proposed approach has been compared with other two traditional approaches from the literature, demonstrate that significant improvements are obtainable.

Efficient Multi-objective Optimization Method for the Mixed-model-line Assembly Line Design Problem

This paper presents a mathematical model and an adaptation of the Strength Pareto Evolutionary Algorithm II (SPEA2) for the Mixed-Model Assembly Line balancing and equipment selection problem. The SPEA2 was enriched with a task and equipment reassignment procedure and aims at supporting the planners to find better solutions in the earliest phases of a production system planning project.

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. The general approach to determine the appropriate parameter combination of GA depends on too many trials of different combinations, and the best one of them that produces good results is selected for the programme, which would be used for problem solving. A few researchers studied on the parameter optimisation of GA. In this paper, response surface-dependent parameter optimisation is proposed to determine the optimal parameters of GA. Results are tested for benchmark problems that are most common in mixed-model assembly line balancing problems of type-I.