Solving Assembly Line Balancing Problems Research Articles

An integrated ergonomic risk assessment framework based on fuzzy logic and IVSF-AHP for optimising ergonomic risks in a mixed-model assembly line

This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights for ergonomic risk assessment methods using the interval-valued spherical fuzzy analytical hierarchy process. In the second stage, a fuzzy logic model for integrated ergonomic risk assessment is constructed based on these weights, and the integrated ergonomic risk score is determined. In the third stage, a mathematical model is formulated to minimise the cycle time while balancing the ergonomic risk level. A case study conducted in a wire harness factory validated the effectiveness of the proposed approach, showing a 10–11% improvement in line efficiency and a 12–25% enhancement in ergonomic risk balancing performance. These findings underscore the potential benefits of implementing this approach, which can significantly improve occupational safety and overall performance.

A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors

The assembly line balancing problem (ALBP) is an eminent NP-hard topic that is discussed in mass production systems with low diversity. Primarily, two types of ALBPs are discussed in the literature as type I, which aims to find the minimum number of workstations for a given cycle time, and type II, which assigns some tasks to a given number of workstations such that the maximum workstation load is minimized. To solve ALBPs, various exact, heuristic, and metaheuristic methods have been proposed. However, these methods lose their efficiency when handling large-size problems. Therefore, researchers have focused on proposing heuristic and metaheuristic algorithms to solve large-size problems, especially when they deal with real-life case problems in the industry. This study aims to present a novel and competitive exact method for solving ALBP type II based on the lexicographic order of vectors for feasible solutions. To evaluate the performance of the developed method, a group of highly used standard test problems in the literature is utilized, and the results are compared and discussed in detail. The computational results in this study specify that the developed solution approach performs efficiently and yields the best global solution of all the ALB test problems, which proves the proposed method's potential and its competitive advantage.

Optimization of Cycle Time in an Assembly Line Balancing Problem

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

A simulated annealing algorithm for multi-manned assembly line balancing problem

Assembly line balancing problems with multi-manned workstations usually occur in plants producing high volume products (e.g. automotive industry) in which the size of the product is reasonably large to utilize the multi-manned assembly line configuration. In these kinds of assembly lines, usually there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. However, owing to the high computational complexity, it is quite difficult to achieve an optimal solution to the balancing problem of multi-manned assembly lines with traditional optimization approaches. In this study, a simulated annealing heuristic is proposed for solving assembly line balancing problems with multi-manned workstations. The line efficiency, line length and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with a numerical example problem, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. Results show that the proposed algorithm performs well.

Soft Computing in Optimizing Assembly Lines Balancing

As part of manufacturing systems, the assembly line has become one of the most valuable researches to accomplish the real world problems related to them. Many efforts have been made to seek the best techniques in optimizing assembly lines. Problem statement: Since it was published by Salveson in 1955, some methods and techniques have been developed based on mathematical modeling. In recent years, some researches in Assembly Line Balancing (ALB) have been conducted using Soft Computing (SC) approaches. However, there is no comprehensive survey studies conducted regarding the use of SC in ALB problems, which is became the aim of this study. Approach: This study reviewed published literatures and previous related works that applied SC in solving ALB problems. Main outcomes: This study looks into the suitability of SC approaches in several types of ALB problems. Furthermore, this study provides the classification of ALB problems that can facilitate distinguishing those problems as fields of research. Result: This study found that Genetic Algorithms (GAs) are predominantly applied to solve ALB problems compared to other SC approaches. This high suitability in ALB refers to GAs' main characteristics that include its robustness and flexibility. These SC approaches have mostly been applied to simple ALB problems, which are not problems that are covered in a real complex manufacturing environment. Conclusion/Recommendations: This study recommends that future researches in ALB should be conducted with regard to other issues, beyond the simple ALB problems and more practical to the industries. Besides the advantages of GAs, there are still opportunities to use other SC approaches and the hybrid-systems among them that could increase the suitability of these approaches, especially for multi-objective ALB problems. This study also recommends that human involvement in ALB needs to be considered as a problem factor in ALB.

A Multi-Objective Genetic Algorithm for Solving Assembly Line Balancing Problem

In this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffmann precedence matrix, immediate update first fit, and rank and assign heuristic methods. For comparative evaluation, 20 networks are collected from open literature, and are used with five different cycle times. All the six heuristics and the genetic algorithm are coded in C++ language. It is found that the proposed genetic algorithm performs better in all the performance measures than the heuristics. However, the execution time for the GA is longer, because the GA searches for global optimal solutions with more iterations.

Genetic algorithm for assembly line balancing

Research on single-model assembly line balancing has produced several good algorithms for solving large problems. The majority of these algorithms generate just one solution to the problem, whereas the real line design faces the need to investigate alternative solutions, where preferences for work allocation to stations are considered, or constraints other than technological precedence are taken into account. The MUST algorithm suggested by Dar-El and Rubinovitch is one of the few algorithms that provides such diversity of solutions. Heuristics enable MUST to solve relatively large line-balancing problems. However, when the initial problem has relatively few constraints, the time and memory requirements needed by MUST may become excessive. This paper describes the development and testing of a Genetic Algorithm for the generation of multiple solutions to the assembly line balancing (ALB) problem. The results are compared with MUST results for different classes of problems. Good results are achieved by combining the genetic approach with a simple local optimization procedure. This procedure performs much faster than MUST for problems with large number of stations and high flexibility ratio. Different crossover and mutation procedures are tested and evaluated, in order to recommend these which are most effective for solving ALB problems.