Assembly Line Balancing Problem Research Articles

A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Novel mathematical modelling approaches and a new lower bounding scheme for multi-manned assembly line balancing problems with walking workers

This article suggests two novel mathematical modelling approaches and a lower bound calculation method for the number of workstations to balance multi-manned assembly lines with walking workers. While new formulations include more variables, they require fewer constraints than the existing one. The performances of all formulations are compared each other using benchmark test instances from the literature as well as the instances produced in this study. Comparison results showed that one of novel modelling approaches exhibits superior performance than the existing one in all aspects. It has been demonstrated that the proposed model can be applied to practical-sized instances of the problem. Also, the advantages of the proposed lower bounding scheme have been shown.

Solution approach using heuristic and artificial neural networks methods in assembly line balancing problems: A case study in the lighting industry

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

Chance-constrained stochastic assembly line balancing with branch, bound and remember algorithm

Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP.

Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

AbstractAssembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to $$\sim $$ ∼ 2.75$$\times $$ × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

The fuzzy human-robot collaboration assembly line balancing problem

Consideration is given to the assembly line balancing problem (ALBP) in production lines employed collaborative robots. Human–robot collaboration promises high advantages in manufacturing regarding automation, productivity, accuracy as well as flexibility in production. This technology is really challenging but requires redesigning of assembly lines to effectively involve the available human- and robot-resources. As robots can collaborate with humans sharing the assembly tasks in the same workstation, production managers experience an increasingly complex ALBP setting. Moreover, a crucial issue raised within this context is the variance and uncertainty in processing times for manual (human) work. Processing times for human work are subject to the nature and skills of the human worker. We explore the heuristic solution of this NP-hard problem using a problem-specific metaheuristic. Our aim is to investigate the effects of utilizing collaborate robots in manual assembly lines with respect to both production rate and workload smoothing. Simulation experiments on a real-life case study borrowed from the literature show how to tackle the problem under uncertainty (fuzziness) in task processing times.

Human-robot collaboration in assembly line balancing problems: Review and research gaps

Researchers have been working on assembly line optimization problems, especially assembly line balancing, for decades. Integration of Industry 4.0 technologies into assembly lines creates new challenges in optimal resource selection, task allocation, line balance, and scheduling decisions, to name a few. Over the last decade, researchers have developed novel frameworks for conventional assembly line optimization problems that comply with the recent industrial revolution and leading technologies. State-of-the-art Balancing problems in assembly lines with collaborative robots are the core of this study as a key decision in Assembly 4.0. The collaboration of humans and robots in a fenceless environment requires a more comprehensive approach than the traditional task-station assignment in a balancing model. Resource selection/allocation and scheduling are the most common decisions that researchers combine with balancing models. Thus, the studies on all these optimization challenges in the last ten years through the Web of Science and Engineering Village are present in the current review. We analyze the articles’ statistics (e.g., year and journals) and contents to find the research trends and gaps for future research direction in collaborative robotic assembly line optimization which incorporates occupational health and safety.

A GRA-based approach for optimal selection of SALBP-1 assembly line balancing heuristics

Assembly line balancing is a compulsory approach which enables mass production of similar parts with proper efficacy. Multiple heuristics are proposed in literature to solve single model assembly line balancing problems (SALBP-1). Previous work has focused on developing and evaluating assembly line balancing heuristics and apply them to different fields. Other work investigates the factors affecting the performance of particular assembly line balancing heuristic. However, there is still a need for a guideline for selecting the most suitable heuristic for a particular application with insights into multiple performance measures. This research presents a grey relational analysis (GRA)-based approach to aid process designers in selecting the heuristic generating that yields the best line balancing. Specifically, combinations of factors of cycle time, order strength, number of tasks, and time variability are considered as operational setups of the line balancing process. The solution of each setup is solved by each heuristic, and the performance of the solution is evaluated. Then, GRA is applied to rank the heuristics on basis of the resulting grey rational grades. The proposed approach provides useful insights into the combinations suitable for a particular heuristic. In this study, time variability and cycle time are shown to have the greatest impact on the overall performance of the considered heuristics.

The Effect of Learning on Assembly Line Balancing: A Review

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems.

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

PurposeAssembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approachThis paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.FindingsThe experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/valueThis research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

Balancing mixed-model assembly lines for random sequences

Assembly lines are the standard choice for the manufacturing of large products and must combine efficiency with flexibility. Since multiple product variants are assembled on the same line, the variation in the processing time and the production sequence must be accounted for. This is especially hard in the planning phase of assembly lines when the demand is only forecast and the production sequence is not yet known. In this paper, an exact method and heuristic extensions are proposed to tackle the paced assembly-line balancing problem considering a random production sequence. Along with the assignment of tasks, the length of the stations is optimized in a Branch-and-Bound algorithm using Markov chains to evaluate the expected costs, which require the solution of a convex subproblem on each node. Product variants are modelled with a given probability, which can be either independent or correlated. Results of the model can be then further improved by optimizing the sequencing once the orders are known. The exact method can solve instances of small and medium-size containing up to 1012 product variants and the heuristic extensions can give good solutions with up to 1020 products. Results show that the optimization of station lengths is more important for the total cost than the difference between optimal and good task assignment solutions.

Comparison of MILP and CP models for balancing partially automated assembly lines

AbstractThe objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved.

An uncertain multi-objective model for simultaneous design of supply chain and assemblers considering sustainable development: a case study

Supply chain network design (SCND) problems are used to formulate and make strategic decisions on the number of facilities, network facility location and plant capacity, to ensure the optimal delivery of products from manufacturers to end consumers. On the other hand, assembly line balancing (ALB) problems provide details on operational decisions which encompass production levels and inventories and therefore should be flexible enough to react when changes in resource availability and product demands occur. Integration of the SCND and ALB problems has thus been explored to develop a more holistic model and to further improve the efficiency of the supply chain. This study develops a novel SCND-ALB optimization model which considers economic, environmental, and social objectives in determining the optimal design and operational conditions of the supply chain under flexible cycle time of the assembly lines. Furthermore, for the first time a zigzag type belief degree model is integrated to address system uncertainties of the SCND-ALB problem. Two multi-objective solution approaches are then considered to simultaneously account for the three sustainability objectives. A case study on the production of a nozzle is explored to demonstrate the capabilities of the model.

Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem

The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under a given time across all cells, even though there is variability in some parameters. When there are stochastic elements, some approaches use constraint relaxation, intervals for the stochastic parameters, and fuzzy numbers. In this paper, a three-part algorithm is proposed that first solves the balancing problem without considering stochastic parameters; then, using simulation, it measures the effect of some parameters (in this case, the inter-arrival time, processing times, speed of the material handling system which is manually performed by the workers in the cell, and the number of workers who perform the tasks on the machines); finally, the add-on OptQuest in SIMIO solves an optimisation problem to constrain the cycle time using the stochastic parameters as decision variables. A Gearbox instance from literature is solved with 15 tasks and 14 precedence rules to test the proposed approach. The deterministic balancing problem is solved optimally using the open solver GLPK and the Pyomo programming language, and, with simulation, the proposed algorithm keeps the cycle time less than or equal to 70 s in the presence of variability and deterministic inter-arrival time. Meanwhile, with stochastic inter-arrival time, the maximum cell cycle is 72.04 s. The reader can download the source code and the simulation models from the GitHub page of the authors.

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 aircraft final assembly line balancing problem considering resource constraints and parallel task scheduling

In this study, the balancing problem of an aircraft final assembly line (AFAL) is investigated. The decision is to assign all the assembly tasks to a given number of workstations, with the objectives to balance the workstation cycle time and to minimize the total resource investment cost. An integer linear programming model is developed to formulate the problem. A construction and improvement based two-stage heuristic approach is proposed to solve this problem. In the first stage, an initial task assignment is constructed based on rules that are evolved by a multi-dimensional archive of phenotypic elites algorithm. In the second stage, an improvement heuristic, consisting of several customized neighborhood operators, is developed to improve the initial task assignment. The proposed approach is compared with the existing methods and its variants. Computational experiments and a real case study reveal the superiority of our proposed approach.

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.

Review on PCB assembly line balancing – glance

The industrial technique known as assembly line balancing is used to increase the efficiency of balancing in production lines. By increasing line efficiency, the assembly line balance problem reduces the number of workstations. The assembly line balancing technique improves line production by utilizing the priority limits for task assignment in the workstation. By distributing the tasks among the workstations according to priority constraints, assembly line balancing improves the efficiency of the line. Line balance issues are observed to fall under the category of sequence-dependent issues while deciding which part numbers to allocate in the workstation. When choosing which part numbers to allocate in the workstation, line balancing concerns are seen to fall under the category of sequence-dependent issues. Mathematical models, algorithms, and simulation software are just a few of the techniques that have been used in the past to handle assembly line balancing concerns. In order to decrease workstation loads and boost assembly line productivity, an effort has been made to review the PCB manufacturing assembly line balancing problem in the current research study. The goal of the study is to cut down on the workloads and waiting times for each task on a manufacturing line. In order to reduce job waiting times and maximize station workloads, U Type Assembly Line is preferred in this study.

Hybrid particle swarm optimization algorithms for cost-oriented robotic assembly line balancing problems

Purpose The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I. Design/methodology/approach Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed. Findings A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality. Originality/value The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.