Assembly Line Balancing Research Articles

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.

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.

SOLVING THE MULTI-MANNED ASSEMBLY LINE BALANCING PROBLEM WITH DEPENDENT TASK TIMES BY MEANS OF EMPIRICALLY ADJUSTED GREEDY HEURISTICS (EAGH)

ABSTRACT: Multi-manned assembly lines are widely used in manufacturing industries that process a high-volume of large-sized workpieces. At each workstation there are multiple workers simultaneously performing different tasks with the possibility of interfering with each other, leading to an increase in the processing task times. This paper studies this type of problem: the multi-manned assembly line balancing problem with dependent task times (MALBP-DTT). As discussed in recent literature, the HEUR_PART procedure presents the best behaviour for solving MALBP-DTT. In this paper, HEUR_PART is improved by using the Empirically Adjusted Greedy Heuristics (EAGH) procedure along with a new procedure (named “EAGH-CKTL”) that is presented in this paper and is based on using EAGH combined with the cocktail of heuristics concept. EAGH and EAGH-CKTL are used to design new priority rules for solving MALBP-DTT through the HEUR_PART steps. In particular, EAGH-CKTL is applied for building new priority rules that have good performance as part of a cocktail of heuristics. The computational experiments show the efficiency of using both EAGH and EAGH-CKTL in the process of designing efficient priority rules: one of the priority rules designed with EAGH presents better performance than any other rule proposed in the literature for HEUR_PART, while another rule designed with EAGH-CKTL evidences a remarkable improvement in the HEUR_PART results when added to its original cocktail of heuristics. Keywords: combinatorial optimisation; assembly line balancing; multi-manned workstations; dependent task times; EAGH; cocktail of heuristics.

Balancing of cost-oriented U-type general resource-constrained assembly line: new constraint programming models

Balancing of cost-oriented U-type general resource-constrained assembly line: new constraint programming models

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.

A new MIP approach for balancing and scheduling of mixed model assembly lines with alternative precedence relations

ABSTRACT In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling 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.

Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem.

Integrating real-time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving stochastic type-II simple assembly line balancing problem

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively.

A Review on Assembly Line Balancing by Different Approaches

One of the oldest and most common production methods is the assembly line. The assembly line balancing issue is used to quickly build large numbers of a consistent product and is concerned with reducing the number of workstations, decreasing cycle time, increasing workload homogeneity, and increasing work cohesion. Assembly lines were initially created for the mass manufacture of standardized goods at a low cost. A production unit's assembly line consists of a number of employees and equipment. An assembly line is used in a manufacturing facility to produce components. During processing, the product travels along this line. For a flexible production system, assembly lines have changed over time from simple lines with a single model to mixed lines with numerous models, lines with parallel workstations, and U-shaped lines. Planning assembly lines for a system's primary goals includes balancing the assembly line according to the intended production volume each shift and reducing the number of workstations. Various assembly line balance issues (ALBP) have been researched and described in this study. Researchers can greatly benefit from the creation of a mathematical model to address the assembly line balance and scheduling issues.

Analysis of Line Balancing Problem for Production of Fuel Tank Mounting Bracket

Assembly line balancing is a manufacturing technique that establishes an expected production rate to create a specific product in a particular period of time. Six Sigma is a profit-maximizing technique achieved by meeting consumer satisfaction. Cycle time reduction has emerged as a crucial aspect of improvement to generate high production and fulfill client demands. In industrial production, assembly lines play a vital role as flow-line production systems. Assembly lines need a significant capital expenditure to build or redesign them, but with good configuration, cost-effective manufacturing is possible. This study aims to increase assembly line productivity with shorter cycle times. The main stage in enhancing an assembly line's overall performance is to create or reconfigure an assembly system, which is made possible by assembly line balance and related operations analysis.

Genetic Transfer Learning for Optimizing and Balancing of Assembly Lines

In this paper, the genetic transfer learning (GTL) method is proposed for knowledge transfer of sustainable assembly manufacturing systems. Existing methods for the assembly line balancing problem (ALBP), such as genetic algorithms (GAs) suffer from three significant limitations: tedious ‘trial and error’ processes, no utilization of existing system solutions, and no consideration of new constraints on system reconfiguration. To address these problems, we propose GTL to migrate the knowledge of the GA setup in system reconfiguration. The contributions are as follows: 1) transfer pretreatment is performed to formulate knowledge of existing systems and adapt to the new constraints of future systems; 2) the similarity of existing and future systems is defined quantifiably to determine transfer conditions and avoid weak and negative transfer for maximizing knowledge transfer; 3) the transfer strategy is made to determine the method and knowledge to be transferred. The case study of a computer assembly line shows that adopting transfer learning has helped to improve assembly line efficiency and sustainability.

Improvement of Assembly Manufacturing Process through Value Stream Mapping and Ranked Positional Weight: An Empirical Evidence from the Defense Industry

This study aims to improve the assembly manufacturing process to solve the workload imbalances by combining value stream mapping (VSM) and ranked positional weight (RPW). An empirical study was conducted in a defense manufacturing firm located in Indonesia. The study specifically focused on 155 components and 56 tasks distributed among 43 assembly workstations in one weapon product. The results of the analysis showed a significant reduction in the total cycle time, from 5121 s (85.35 min) to 3620 s (60.33 min), or a decrease of 29%. Additionally, the study found improvements in the balance of the assembly line as measured by balance delay, line efficiency, and smoothness index performance indicators. The application of VSM and RPW in this study is unique in the context of the defense industry, as it provides empirical analysis on cycle time and assembly line balance, which is rarely studied. The results of this study contribute to the advancement of literature in the field and provide valuable insights for other organizations in the defense industry and other manufacturing industries. By improving the efficiency and balance of the weapon assembly line, this study has the potential to increase productivity and reduce waste.

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

PurposeThis study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously.Design/methodology/approachTTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions.FindingsThe computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm.Originality/valueMOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence.

Robotic stochastic assembly line balancing

Robotic stochastic assembly line balancing

Integrated mixed-model assembly line balancing and parts feeding with supermarkets

In the automotive industry, decentralized in-house logistic areas called supermarkets are used for parts feeding to mixed-model assembly lines. In the literature, generally, the number of supermarkets and their locations are determined according to a previously balanced assembly line. As a result, supermarket locations have to be completely dependent on the line balance. There may be more than one solution that gives the same performance criterion value in the line balancing. Among these alternative solutions, it is more reasonable to use a solution that provides lower logistics costs than others. For this purpose, in this study, the mixed-model assembly line balancing problem and the supermarket location problem are considered simultaneously. Accordingly, in order to minimize the total costs of the assembly line and supermarkets, a mixed-integer mathematical model is developed, and the developed model is solved by using constraint programming (CP). In addition, a new approach based on Ant Colony Optimization (ACO) and Simulated Annealing (SA) is presented for large-sized problems. An illustrative example problem is solved using both the developed model and the proposed algorithm. The effectiveness of the proposed approach is tested through a new data set of test problems presented to the literature for a computational experiment. The experimental results show that the total costs are reduced meaningfully and a more realistic and applicable structure is achieved by the proposed approach.

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