Two-sided Assembly Line Balancing Research Articles

Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer

Research on the assembly line balancing problem has been continuously incorporated the characteristics of actual production lines without considering the mixed-model two-sided assembly line with unpaced synchronous transfer, which is common in body shops of cars or trucks. This study attempts to model a mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer, and designs a genetic algorithm to solve this problem. First, to describe the process whereby model sequence and its execution, station and its number, and the process time caused by task allocation lead to the variable output of an unpaced synchronous line, a model without an observation point restriction is proposed. Based on this, a mathematical model of a balancing and sequencing problem of a mixed-model two-sided assembly line with an unpaced synchronous transfer is established, in which the balancing problem is aimed at Type-II. Then, an improved genetic algorithm adapted to the complexity of the problem is proposed. The algorithm designs a combination and evaluation mechanism to ensure that the solution populations of the balancing and sequencing problems are sufficiently combined and evaluated in each iteration to improve efficiency. The proposed algorithm is illustrated using a small numerical example. Finally, the algorithm is experimentally investigated using a set of test cases and several comparison algorithms. The experimental results show that the combination and evaluation mechanism is suitable for these problems, especially for large-sized cases, and that the proposed algorithm is effective and has better performance than the comparison algorithms.

An Integrated Method for Solving the Two-Sided Assembly Line Balancing Problems

The problems of parallel two-sided assembly lines balancing (TALB) are nonpolynomial (NP-hard) problems that belong to the field of combinatorial optimization. In most cases, these problems can be resolved with algorithms. To this end, this research adopts colored Petri nets (CPN) and a minimal spanning tree (MST) to address the type I problems for two-sided assembly line balancing problem (TALBP). This research proposes a three-step method. In the first step, a reachability tree chart is built to show potential tasks order assignment. In the second step, the color firing mechanism is employed to determine if the colored conditions are satisfied. The final step uses a MST and a least-time prioritized rule to select the proper tasks for assignment efficiently. These steps will be performed repetitively until all the tasks are properly assigned. Since the whole assignment process is based on graphical and paradigm methods, it can be adopted to lessen the burdens of complex calculations. The end of the research will present the comparison results between the examples from the literature and the method proposed in this research. As indicated by the research findings, employing CPN and an MST for TALBPs (type-I) can obtain even favorable and consistent results over legacy methods, and further optimize workstation design and enhance balance efficiency.

An evolutionary genetic algorithm for a multi-objective two-sided assembly line balancing problem: a case study of automotive manufacturing operations

ABSTRACT Assembly lines are often indispensable in factories, and a good assembly-line balancing model is very important for manufacturers to maximize their profit using limited resources in a competitive environment. To maintain a productive assembly line, multiple objectives with different importance must be considered at the same time. In this paper, a two-sided assembly-line balancing problem (TALBP) with multiple objectives is examined. A fuzzy multi-objective linear programming-weighted model (FMOLP-W) for solving the TALBP is constructed first with the consideration of the importance weights of the line balancing performance factors, including minimizing the number of workstations, minimizing cycle time, maximizing line efficiency, minimizing smoothness index and minimizing workstation idle time. An evolutionary genetic algorithm (GA) is proposed next to tackle large-scale problems when the problems are too complex to be solved by the FMOLP-W.

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Local search methods for type I mixed-model two-sided assembly line balancing problems

Two-sided assembly lines are widely utilized to assemble large-sized products such as cars and trucks. Recently, these types of assembly lines have been applied to assemble different types of products due to a large variety of customer demands and strong market competition. This paper presents two simple local search methods, the iterated greedy algorithm and iterated local search algorithm, to deal with type I mixed-model two-sided assembly line balancing problems. These two algorithms utilize new precedence-based local search functions with referenced permutation and two neighborhood structures to emphasize intensification while preserving high search speed. Additionally, these local search methods are enhanced by utilizing the best decoding scheme amongst nine candidates and a new station-oriented evaluation to guide the search direction. New lower bound calculations are also presented to check the optimality of the achieved solutions. Eleven recent and high-performing metaheuristic algorithms are re-implemented to test the performance of the proposed algorithms. A comprehensive study on a set of benchmark problems demonstrates the advantages of the improvements and the superiority of the two proposed methods. Experimental results show that the proposed algorithms obtain 23 new upper bounds compared with two recently published algorithms, among which 19 cases are proven to be optimal for the first time.

Solving Multi-Objective Two-Sided Assembly Line Balancing Problems by Harmony Search Algorithm Based on Pareto Entropy

Two-sided assembly lines are designed to produce large and complex products, where workers can perform on both sides at the same time. This paper establishes a mathematical model for the multi-objective two-sided assembly line balancing problems with additional constraints (MOATALBP). The model considers both workers skills and the balance of the assembly line, aiming to maximize efficiency and minimize workers cost and smoothness index. A harmony search algorithm (HS) based on Pareto entropy (PE-MHS) is proposed to solve MOATALBP. The difference entropy of Pareto solutions is employed to adjust the algorithm parameters to enhance the optimization ability of PE-MHS. Moreover, a fine-tuning operation combining insertion and inverse sequence is utilized to avoid the algorithm from falling into local optima. Ultimately, non-dominated sorting ensures a set of well-distributed Pareto solutions. The experimental results of different problems indicate that the proposed algorithm can achieve better solutions than three classical algorithms (NSGAII, SPEA2 and HS) for the MOATALBP.

Optimisation of Two-sided Assembly Line Balancing with Resource Constraints Using Modified Particle Swarm Optimisation

Two-sided Assembly Line Balancing (2S-ALB) is important in assembly plants that produce large-sized high-volume products, such as in automotive production. The 2S-ALB problem involves different assembly resources such as worker skills, tools, and machines required for the assembly. This research modelled and optimised the 2S-ALB with resource constraints. In the end, besides having good workload balance, the number of resources can also be optimised. For optimisation purpose, Particle Swarm Optimisation was modified to reduce the dependencies on a single best solution. This was conducted by replacing the best solution with top three solutions in the reproduction process. Computational experiment result using 12 benchmark test problems indicated that the 2S-ALB with resource constraints model was able to reduce the number of resources in an assembly line. Furthermore, the proposed modified Particle Swarm Optimisation (MPSO) was capable of searching for minimum solutions in 11 out of 12 test problems. The good performance of MPSO was attributed to its ability to maintain the particle diversity over the iteration. The proposed 2S-ALB model and MPSO algorithm were later validated using industrial case study. This research has a twofold contribution; novel 2S-ALB with resource constraints model and also modified PSO algorithm with enhanced performance.

An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems

Two-sided assembly lines are often used in assembly of large-sized products, such as automobiles, buses and trucks. Compared to the traditional one-sided assembly line, two-sided assembly line has advantages of shorter line and higher utilization of fixture. However, normal balancing method is not applicable to solve the two-sided assembly line balancing problem since the constraint conditions become more complicated. On the other hand, artificial fish swarm algorithm is a relatively new member of swarm intelligence based on swarm behaviors that were inspired from social behaviors of fish swarm in nature. As a typical application of behaviorism in artificial intelligence, artificial fish swarm algorithm can search for the global optimum. So it is a good candidate for developing new search algorithm for solving optimization problems in operational research. In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station. Through extensive computational experiments, the performance of the proposed artificial fish swarm algorithm is examined. The experimental results validate the effectiveness and efficiency of the proposed method.

A genetic algorithm approach for balancing two-sided assembly lines with setups

PurposeThis paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations.Design/methodology/approachThe complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup).FindingsAlgorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results.Originality/valueThe sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model.

Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times

Two-sided robotic assembly lines are employed to assemble large-sized high-volume products, where robots are allocated to the workstations to perform the tasks and human workers are replaced for achieving lower cost and greater flexibility in production. In the two-sided robotic assembly lines, setup times are unavoidable and it has been ignored in most of the reported works. There has been limited attention on this till date. This paper focusses on the robotic two-sided assembly line with consideration of sequence-dependent setup times and robot setup times. A new mixed integer linear programming model is developed with the objective of optimizing the cycle time. Due to the NP-hard nature of the considered problem, this paper proposes a set of metaheuristics to solve this considered problem, where two main scenarios with low and high setup time’s variability are considered. Computational results verify that this new model is capable to achieve the optimal solutions for small-size instances whereas the simple adoption of the published mathematical model might produce wrong solutions for the considered problem. A comprehensive study with 13 algorithms demonstrates that the two variants of artificial bee colony algorithm and migrating bird optimization algorithm are capable to achieve the optimality for small-size instances and to obtain promising results for large-size instances.

Improved moth flame optimization algorithm to optimize cost-oriented two-sided assembly line balancing

Purpose This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs. Design/methodology/approach I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented. Findings The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods. Originality/value The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.

Balancing of mixed-model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm

Mixed-model assembly lines allow the production of different product variants in mass quantities on the same assembly line. In studies addressing mixed-model assembly with two-sided lines, assembly line (work)stations are classified as left-side or right-side stations depending on the operation side to which they are allocated. However, underground stations are also utilized in industry to perform tasks that need to be done underneath the product being assembled on the line. This paper introduces and mathematically formulates a mixed-model, two-sided assembly line balancing problem considering underground stations. The precedence relationships between tasks being performed in the three types of stations are defined and considered in the model. A numerical example is solved in GAMS (with CPLEX solver) and the detailed balancing solution is provided. A new ant colony optimization algorithm, in which the parameters are optimized using response surface methodology, is also developed to solve real-world problems. A total of 78 test problems are derived from the literature and their lower bounds are calculated to test the performance of the ACO algorithm. ACO finds optimum solutions for the majority of small and medium-sized test problems. In comparing the ACO results to the lower bounds for the large-sized problems, ACO finds near -optimum solutions in majority of the test cases.

Two-Sided assembly line balancing to minimize number of workstation with considering the relationships between tasks

There are many problems in assembly line. Some of the problems are assembly line reliability which relates to maintenance and unbalance workload. As a result, production line is not efficient and production plan is not fulfilled. Previous research on two-sided assembly line balancing problem has proprosed improvement method by considering index relationship between tasks. There are only two factors that is taken into account in this research, they are: distance factor of the implementation of the task (distance factor) and tools used to perform the task (tool factor). Therefore, this paper aims to develops conceptual model by considering additional factors that affecting Assembly Line Tasks Consistency (ATC). The additional factors that are considered are as follow: distance factor, tool factor, motion factor, layout factor, and skill factor. The purpose of this conceptual model is to increase productivity by minimizing number of workstations. Linear combination are used to find a combination solution of a relationship between task to complete a two-sided assembly line balancing problem. Improvement of proposed assembly line are measured according to Balance Delay (BD) and Line Efficiency (LE).

Priority rules-based algorithmic design on two-sided assembly line balancing

The two-sided assembly line becomes very popular in recent years. In this paper, a priority rules-based algorithmic design is developed for optimizing two-sided assembly line. Five elementary rules and 90 composite rules are tested on the benchmark data sets and their performance are provided. Two enumerative principles, which are specific to two-sided assembly lines are proposed to enhance the performance of the rules. Further, priority rules are embedded into a bounded dynamic programming framework to form a deterministic algorithm where the use of a bound can reduce the solution space as the algorithm is advanced stage-by-stage. These approaches offer distinct advantages over the methods proposed in the literature, such as less fine-tuning effort and more stable results. Computational results show that the novel algorithm can generate good solutions efficiently, especially in large sized problems.

One- and two-sided assembly line balancing problems with real-world constraints

In this study, we consider balancing problems of one- and two-sided assembly lines with real-world constraints like task or machine incompatibilities. First, we study the one-sided assembly line balancing problem (ALBP) with a limited number of machine types per workstation. Using a genetic algorithm (GA), we find optimal results for real-world instances. A set of larger test cases is used to compare two well-established solution approaches, namely GA and tabu search (TS). Additionally, we apply a specific differential evolution algorithm (DE), which has recently been proposed for the considered ALBP. Our computational results show that DE is clearly dominated by GA. Furthermore, we show that GA outperforms TS in terms of computational time, if capacity constraints are tight. Given the algorithm’s computational performance as well as the fact that it can easily be adapted to additional constraints, we then use it to solve two-sided ALBP. Three types of constraints and two different objectives are considered. We outperform all previously published methods in terms of solution quality and computational time. Finally, we are the first to provide feasible test instances as well as benchmark results for fully constrained two-sided ALB.

Modelling of Two-sided Assembly Line Balancing Problem with Resource Constraints

Two-sided assembly line balancing (2S-ALB) problems is practically useful in improving the production of large-sized high-volume products. Many published papers have proposed various approaches to balance this well-known ALB problem. However, little attention is given in formulating the 2S-ALB problems. In this paper, 2S-ALB is modelled with four different objective functions comprising minimization of workstations, mated- workstation, idle time and resource constraints. In different with existing model, this paper also considers resource constraint with a mathematical modelling formulation in solving the 2S- ALB problems. The modelling procedures are present for each objective functions with a simple 2S-ALB example problem. Then, the anticipated performance solution is obtained from the test problem.

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly process. Robots help to replace human labor and execute tasks efficiently at each workstation in the assembly line. From the literature, it is concluded that not much work has been conducted on two two-sided robotic assembly line balancing problems. This article addresses the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A mixed-integer programming model of the proposed problem is developed which is solved by the CPLEX solver for small-sized problems. Due to the problems in non-polynomial--hard nature, a co-evolutionary particle swarm optimization algorithm is developed to solve it. The co-evolutionary particle swarm optimization utilizes local search on the global best individual to enhance intensification, modification of global best to emphasize exploration, and restart mechanism to escape from local optima. The performances of the proposed co-evolutionary particle swarm optimization are evaluated on the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm is compared with five other well-known metaheuristics, and computational and statistical results demonstrate that the proposed co-evolutionary particle swarm optimization outperforms most of the other metaheuristics for majority of the problems considered in the study.

A review of two-sided assembly line balancing problem

Assembly line balancing (ALB) is concerned with assigning tasks within an assembly line to meet the required production rate for optimization purposes. On the other hand, two-sided ALB performs double-sided assembly operation on a single assembly line. In this paper, we have focused the survey on two-sided assembly line balancing (2S-ALB) research problems. The numerous factors mentioned in 2S-ALB literature were actually based on problem resolutions, and this paper will quote any preferred literature considering the frequent citation. In particular, this review explores in detail the ALB problems, optimization methods, objective functions, and specific constraints used in solving 2S-ALB problems. Among the purposes of ALB problems is that it traditionally focuses on simple ALB with various engaging approaches. General ALB comes second because of its complexity and nondeterministic polynomial (NP)-hard-classified problems. However, due to the current manufacturing issues, GALB problems, such as 2S-ALB, are forced to be examined and this comprehensive literature will specify anything necessary for the optimization purposes. Finally, future research direction has been discovered and put forward as the suggestion.