Assembly Line Balancing Problem Of Type Research Articles

Development of Differential Evolution Algorithm for Simple Assembly Line Balancing Problem Type 1

The Simple Assembly Line Balancing Problem Type 1 (SALBP-1) is a widely embraced method in the industry for its simplicity in organizing production processes and enhancing efficiency. Consequently, a Differential Evolution Algorithm (DDE) using a backward task sequence was developed in this study to assist in production process management by determining the optimal number of stations. The efficacy of this method was assessed by juxtaposing it with heuristic approaches, including Longest Operation Time (LOT), Most Following Tasks (MFT), Ranked Positional Weight (RPW), Shortest Operation Time (SOT), Fewest Following Tasks (FFT), Ant Colony Optimization (ACO), Differential Evolution (DE), and Immune Genetic Algorithm (IGA). The findings reveal that DDE outperforms LOT, MFT, RPW, SOT, and FFT in discovering superior solutions and consistently matches solutions achieved by ACO, DE, and IGA methods across all problems. Notably, the DDE method exhibits a shorter time frame for solution discovery.

Fitness landscape analysis of the simple assembly line balancing problem type 1

As the simple assembly line balancing problem type 1 (SALBP1) has been proven to be NP-hard, heuristic and metaheuristic approaches are widely used for solving middle to large instances. Nevertheless, the characteristics (fitness landscape) of the problem’s search space have not been studied so far and no rigorous justification for implementing various metaheuristic methods has been presented. Aiming to fill this gap in the literature, this study presents the first comprehensive and in-depth Fitness Landscape Analysis (FLA) study for SALBP1. The FLA was performed by generating a population of 1000 random solutions and improving them to local optimal solution, and then measuring various statistical indices such as average distance, gap, entropy, amplitude, length of the walk, autocorrelation, and fitness-distance among all solutions, to understand the complexity, structure, and topology of the solution space. We solved 83 benchmark problems with various cycle times taken from Scholl’s dataset which required 83000 local searches from initial to optimal solutions. The analysis showed that locally optimal assembly line balances in SALBP1 are distributed nearly uniformly in the landscape of the problem, and the small average difference between the amplitudes of the initial and optimal solutions implies that the landscape was almost plain. In addition, the large average gap between local and global solutions showed that global optimum solutions in SALBP1 are difficult to find, but the problem can be effectively solved using a single-solution-based metaheuristic to near-optimality. In addition to the FLA, a new mathematical formulation for the entropy (diversity) of solutions in the search space for SALBP1 is also presented in this paper.

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations. A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

Simple Assembly Line Balancing Problem Type 2 By Variable Neighborhood Strategy Adaptive Search: A Case Study Garment Industry

This article aims to minimize cycle time for a simple assembly line balancing problem type 2 by presenting a variable neighborhood strategy adaptive search method (VaNSAS) in a case study of the garment industry considering the number and types of machines used in each workstation in a simple assembly line balancing problem type 2 (SALBP-2M). The variable neighborhood strategy adaptive search method (VaNSAS) is a new method that includes five main steps, which are (1) generate a set of tracks, (2) make all tracks operate in a specified black box, (3)operate the black box, (4) update the track, and (5) repeat the second to fourth steps until the termination condition is met. The proposed methods have been tested with two groups of test instances, which are datasets of (1) SALBP-2 and (2) SALBP-2M. The computational results show that the proposed methods outperform the best existing solution found by the LINGO modeling program. Therefore, the VaNSAS method provides a better solution and features a much lower computational time.

Workers’ rest allowance and smoothing of the workload in assembly lines

Ergonomic aspects have a crucial role in manual assembly systems. They impact on the workers’ health, final product quality and productivity. For these reasons, there is the necessity to integrate them into the assembly line balancing phase as, whereas, only time and cost variables are considered. In this study, human energy expenditures are considered as ergonomic aspects and we integrate them, for the first time, into the assembly line balancing problem type 2 through the rest allowance evaluation. We consider as an objective function the minimization of the smoothness index. Firstly, a new optimal method based on mixed integer linear programming and a new linearization methodology are proposed. Then, a heuristic approach is introduced. To complete the study, a computational experimentation is presented to validate the mathematical model and to compare the methodologies proposed in terms of computational time, complexity and solution. Additionally, we provide a detailed analysis of the impact that rest allowance evaluation can have on productivity comparing the results obtained, taking into account the rest allowance integration before, during and after the assembly balancing process.

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

This paper presents a mathematical model for Simple Assembly Line Problem Type 1 (SALBP-1) with resource constraints; machine and worker. The existing model of SALBP-1 assumes that all the workstations have similar capability, while in reality the workstation has different capability because of limitation in term of machines and worker skills. The proposed model is aimed to mathematically represent the SALBP-1 with resource constraints. Besides that, three objective functions which to minimize number of workstation, machine used and number of worker are also presented. The machine considered to have different types of machine needed to produce a product in an assembly line while worker are considered to have different abilities and skills. The model is then illustrated and validated using some examples. The proposed model for SALBP-1 with machine and worker constraints is able to minimize the resources in assembly. Therefore, the assembly cost can be reduced.

Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types

This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.

A simulated annealing approach for multi-manned assembly line balancing problem type II

Multi-manned assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production lines, usually there are multimanned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones in order to meet the market demand. In this paper, such a class of assembly line balancing problems, named multi-manned assembly line balancing problems type II, has been addressed. Since the problem is NP-hard, a meta-heuristic approach based on a simulated annealing algorithm has been developed to solve the problem. The performance of the proposed algorithm has been tested on a set of test problems taken from the literature; the results show that the algorithm performs well.

Heuristics and lower bounds for the simple assembly line balancing problem type 1: Overview, computational tests and improvements

Assigning tasks to work stations is an essential problem which needs to be addressed in an assembly line design. The most basic model is called simple assembly line balancing problem type 1 (SALBP-1). We provide a survey on 12 heuristics and 9 lower bounds for this model and test them on a traditional and a lately-published benchmark dataset. The present paper focuses on algorithms published before 2011.We improve an already existing dynamic programming and a tabu search approach significantly. These two are also identified as the most effective heuristics; each with advantages for certain problem characteristics. Additionally we show that lower bounds for SALBP-1 can be distinctly sharpened when merging them and applying problem reduction techniques.

SALOME: A Bidirectional Branch-and-Bound Procedure for Assembly Line Balancing

In this article, we report on new results for the well-known Simple Assembly Line Balancing Problem Type 1. For this NP-hard problem, a large number of exact and heuristic algorithms have been proposed in the last four decades. Recent research has led to efficient branch-and-bound procedures. Based on an analysis of their specific strengths, a new algorithm (Salome) is developed. Its main characteristic is a new branching strategy (local lower-bound method) and a bidirectional branching rule. Furthermore, new bounding and dominance rules are included. Computational experiments on the basis of former data sets, as well as a new, more challenging one, show that Salome outperforms the most effective existing procedures for solving this problem.

Maximizing the production rate in simple assembly line balancing — A branch and bound procedure

In this paper, a branch and bound procedure for the Simple Assembly Line Balancing Problem Type 2 (SALBP-2) is described. This NP-hard problem consists of assigning tasks to a given number of work stations of a paced assembly line so that the production rate is maximized. Besides, possible precedence constraints between the tasks have to be considered. Existing solution procedures for SALBP-2 are mainly based on repeatedly solving instances of the closely related SALBP-1, which is to minimize the number of stations for a given production rate. The proposed branch and bound procedure directly solves SALBP-2 by using a new enumeration technique, the Local Lower Bound Method, which is complemented by a number of bounding and dominance rules. Computational results indicate that the new procedure is very efficient.