Heuristic production line balancing problem solution with MATLAB software programming

The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and meta-heuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high-quality solutions (up to 85.94% optimum solutions was obtained). Also, the results were statistically justified by comparing with multi-rule multi-objective simulated annealing (MRMOSA), priority-based genetic approach (PriGA), two-phased genetic approach (2P-GA), and assignment genetic approach (MA-GA).  Â

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

ABSTRACTIn the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real‐world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation‐based optimization to maintain the average cycle time close to the deterministic one. A real‐life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter‐arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter‐arrival time is set to 270 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.

With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significantly impede its efficiency. In leveraging such information as part of the optimization problem, a contagious artificial immune network (CAIN) approach is proposed to simultaneously address ALB efficiency and bottleneck resources while achieving a truly balanced production line. A computational experiment conducted on benchmark data sets has demonstrated a proof-of-concept, where leveraging knowledge-intensive optimization approach had successfully produced high-quality solutions up to 100% improvement with statistically significant justification. Such findings may play an essential determinant in the manufacturing industry, whether being relevant or left out in the era of increasingly being information-driven.

This study suggests a mathematical modelling approach to sub-assembly detection and assembly line balancing problems. The sub-assembly detection problem involves two issues as follows: 1 determine which components of products are assembled in sub-assembly area; 2 determine which components of the products are assembled directly in the line. In assembly line balancing problem tasks are assigned to the workstations. The main motivation of this study is to determine the best compromised assembly line structure by solving the sub-assembly detection and the line balancing problems jointly. A multi-objective mathematical model is developed to formulate the above mentioned problems in which two conflicting costs of sub-assembly generation and assembly line labour cost are involved. Since the applications in real life are imprecise, fuzzy goal programming is adapted to the developed model to determining the vague goal. Then the model is implemented in a real life automotive manufacturers assembly line balancing.

Assembly line balancing is a hard problem concerned with the assignment of individual work elements or tasks to workstations so as to minimize the assembly cost as much as possible. Decisions associated with line balancing have a direct influence on the costs of production. Basic heuristic algorithms have been widely used to solve the assembly line balancing problem (ALBP). However, to solve hard, large-scale ALBPs, efficient decision tools are essential. Therefore, developing efficient algorithms that can yield optimal or near-optimal solutions is of utmost importance. In this chapter, a hybrid grouping genetic algorithm is developed to address complex problems. The algorithm incorporates basic heuristics, unique genetic operators, and other techniques to enhance the optimization search process. Comparative computational results based on established test problems are presented. The results show that the hybrid grouping genetic algorithm is more efficient and effective than competitive algorithms. The algorithm can be used to assist decision makers in assembly line balancing decisions, with minimal computational requirements.

Gökçen et al. (2006) have proposed several procedures and a mathematical model on single-model (product) assembly line balancing (ALB) problem with parallel lines. In parallel ALB problem, the goal is to balance more than one assembly line together. In this paper, a network model for parallel ALB problem has been proposed and illustrated on a numerical example. This model is a new approach for parallel ALB and it provides a different point of view for interested researchers.

The problem of assembly line balancing is to assign different tasks to individual workstations for ensuring the sum of task times at any station not exceeding the station time. Standard minute time is generally used in the clothing industry as a predictor of sewing speed and production efficiency. In the clothing industry, the standard minute time derived from the work study methods is generally assumed as a constant for line balancing. However, a lot of factors cause variations on operational time of the same task such as the fabrics and sub‐materials, performance of the machinery, working environment and quality level of the product. With the aid of an illustrating example selected from a men’s shirt manufacturing factory, the effect of time variations for assembly line balancing has been studied in this paper.

Enumerations and stability analysis of feasible and optimal line balances for simple assembly lines

The process of optimally assigning the timed tasks required to assemble a product to an ordered sequence of workstations is known as the Assembly Line Balancing (ALB) problem. Typical approaches to ALB assume a strict mathematical posture and mostly treat it as a combinatorial optimization problem with the objective of minimizing the idle time across the workstations, while satisfying precedence constraints. The actual nature of the tasks assigned is seldom taken into consideration. While this approach may yield satisfactory cycle time results on paper, it often leads to inconvenient task assignments in an actual work environment. It has been postulated in the literature that assigning groups of related tasks to the same workstation may lead to assembly lines that exhibit increased robustness in real-world situations at the expense of a slightly increased cycle time. The prototypical example of such an approach, Agrawal’s Largest Set Rule (LSR), utilizes backward work relatedness to assign a set of cohesive tasks to the proper workstation. In this paper, we enhance the performance of the original LSR algorithm through the concept of bi-directional work relatedness, where backward and forward relationships are taken into consideration for task assignments. The proposed concept leads to comparable cycle time and improved work relatedness. Applying this novel concept to a benchmark ALB problem demonstrates the feasibility and applicability of the proposed approach.

Firstly, aimed at the existing problem of reconfigurable assembly line (RAL) balancing, the characteristics of RAL are analyzed and the special requirements of balancing are studied. Secondly, the mathematical model is proposed based on two factors, which are integrated including the cycle time and the assembly line efficiency. Then a new hybrid genetic algorithm is developed for finding optimal solution of the problems. Finally, example verification is carried out, and which indicates the validity of the proposed algorithm.

Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.

In an assembly system, balancing the line and designing the part feeding setting play a fundamental role in guaranteeing efficiency and effectiveness. Although these two aspects are interrelated, they are usually performed subsequent to each other, with the part feeding decisions only being made once the line has already been balanced. However, the simultaneous solving of the assembly line balancing and part feeding problems can lead to interesting benefits. In this paper, an innovative approach for solving the Joint Assembly Line Balancing and Feeding Problem (JALBFP) is proposed, to achieve an integrated balancing of assembly tasks and parts picking/kitting and a fully synchronised assembly-feeding system. The aim is to minimise the operational system cost, including workforce, space and transportation costs. The Mixed Integer Linear Programming (MILP) model is applied to several instances and two case studies and compared to a hierarchical approach. The proposed model leads to a significant improvement in reducing the number of assembly stations required and the inventory level at the line, where space is notoriously scarce, also avoiding workforce oversizing in the feeding system. The strength of the model relies on its formulation and on its versatility, which makes it applicable to different assembly sectors.

Assembly line balancing (ALB) problem has evolved in lined with the manufacturing advancement. Previous research in ALB mostly assumed that all workstations are having similar capabilities including the machines, tools and worker skills. Recently, researchers started to consider the ALB with resource constraint (ALB-RC) such as machine and worker. This paper aim to evaluate new rank-based crossovers to optimize real-life ALB-RC problem. Prior to this work, the authors had proposed rank-based crossover type I and II (RBC-I and II) to enhance the performance of Genetic Algorithm (GA) in optimizing ALB-RC problem. An industrial case study has been conducted in electronics industry. The results of industrial case study confirmed that the proposed ALB-RC model is capable to be used for the real industrial problem. At the same time, the result indicated that the GA with rank-based crossover is capable to optimize real-life problem. As a comparison, the number of workstation, resources and workers had reduced between 10 – 15% for the optimised layout using GA with RBC, compared with the original layout in the case study problem

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. This paper presents an approach, based on Particle Swarm Optimization (PSO) algorithm, to effectively address the assembly line balance problem with the complicating factors of parallel workstations. The method of linearly decreasing the inertia weight of the PSO (LDWPSO) is used in this paper to compare with the standard PSO. Experimental results show that the LDWPSO algorithm is superior to the standard PSO on some assembly line balancing problem.