U-shaped Assembly Line Balancing Problems Research Articles

Enhanced beam search heuristic for U-shaped assembly line balancing problems

U-shaped assembly lines have drawn increasing attention from the modern assembly line industry owing to their high flexibility. This work develops an enhanced beam search heuristic algorithm to solve types 1 and 2 U-shaped assembly line balancing problems (UALBP-1 and UALBP-2). The proposed beam search heuristic is enhanced by utilizing: (1) five lower bounds and four dominance rules to prune the dominated partial solutions; (2) a new station load selection criterion to select station load with a smaller lower bound, less idle time and fewer ‘easy’ tasks; and (3) a new method to extend partial solutions based on heuristic information to encourage full station load. Extensive numerical study is carried out to test and evaluate the performance of the proposed algorithm. Computational results demonstrate that the proposed method outperforms the state-of-the-art methods for both UALBP-1 and UALBP-2 by achieving 33 and 31 new optimal solutions for UALBP-1 and UALBP-2, respectively.

Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times

Awareness of the importance of U-shaped assembly line balancing problems is all on the rise. In the U-shaped assembly line, balancing is affected by the uncertainty associated with the assembly task times. Therefore, it is crucial to develop an approach to respond to the uncertainty caused by the task times. When the great majority of existing literature related to uncertainty in the assembly line is considered, it is observed that the U-shaped assembly line balancing problem under uncertainty is scarcely investigated. That being the case, we aim to fill this research gap by proposing a robust counterpart formulation for the addressed problem. In this study, a robust optimization model is developed for the U-shaped assembly line worker assignment and balancing problem (UALWABP) to cope with the task time uncertainty characterized by a combined interval and polyhedral uncertainty set. A real case study is conducted through data from a company producing water meters.

A Water-Flow Like Algorithm for Solving U-Shaped Assembly Line Balancing Problems

The problem of assigning assembly tasks to the stations arranged along a material handling device is known as assembly line balancing. This paper aims to address the U-shaped assembly line balancing problem (UALBP) which arises when a U-shaped assembly line has to be configured. It is widely known that this problem is NP-hard. Accordingly, different meta-heuristics based on a single solution (such as Simulated Annealing) or a population of solutions (such as Genetic Algorithms) have been proposed in the literature. Meanwhile, it has been argued that either of these meta-heuristics with a fixed number of solutions cannot maintain efficient search progress and thus can lead to premature convergence. Thus, this study aims at adopting a novel meta-heuristic algorithm with dynamic population sizes, namely Water Flow-like Algorithm (WFA), inspired by the behaviour of water flows in nature, to address the UALBP. The line efficiency and variation of workload are considered as the primary and the secondary objective, to be optimized, respectively. To verify the efficiency and robustness of the proposed WFA, a real case study taken from an automobile manufacturer as well as a set of standard problems are solved and the results compared with the existing approaches in the literature. The computational results demonstrate the superiority of the WFA, particularly in addressing medium to large-sized problems.

A novel meta-heuristic approach to solve fuzzy multi-objective straight and U-shaped assembly line balancing problems

The consideration of this study is devoted to deal with the straight and U-shaped assembly line balancing problems (ALBPs). The ALBP involves allocation of required tasks to a set of workstations, so that objective functions being optimized are subjected to set of constraint. While many efforts have been dedicated in the literature to develop deterministic model of the assembly line, the attention is not considerably paid to those in uncertain circumstances. In this paper, along with proposing a novel fuzzy model for ALBP, triangular fuzzy numbers are deployed with to respect vagueness and uncertainty subjected to the task processing times. For this purpose, two conflicting objectives are considered simultaneously with regard to set of constraints, so that the efficiency of the line has to be maximized. To solve the problem, a modified NSGA-II, which utilized a new repairing mechanism, is proposed in response to the need of appropriate method treating such complicated problems. The validity of the proposed model and algorithm is evaluated and proved though a benchmark test problem. The obtained results reveal that in contrast to benchmark that applied an exact solution procedure, the proposed algorithm is capable of delivering the astonishing solutions in a more effective procedure. Along with the use of NSGA-II, in this study, three well-known meta-heuristic algorithms, namely PESA-II, NSACO and NPGA-II, are also employed for solving the problem in order to evaluate the effectiveness of the proposed algorithm, so that the results demonstrate the high performance for the NSGA-II over them. Finally, in light of the obtained results, this study offers an efficient framework enabling the decision maker to handle uncertainty in ALBPs along with the use of an efficient algorithm to solve them.

Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms.

An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems

Abstract Due to immense role of an efficient assembly line in today’s manufacturing systems, the consideration of this paper is devoted to address the straight and U-shaped assembly line balancing problems. Assembly Line Balancing Problem (ALBP) considers conflicting objective functions that should be optimized simultaneously subject to set of constraints. To this end, this paper spends endeavor to develop a new fuzzy linear programming model. Having dealt with uncertain nature of the real production system, triangular fuzzy numbers (TFNs) are employed in order to represent uncertainty and vagueness associated with the task processing times. The proposed model can be regarded as a basis of fuzzy programming for further practical development in assembly line problems. To solve the problem, an efficient Multi Objective Genetic Algorithm (MOGA) is proposed. For this purpose, having respected several special characteristics of both fuzzy straight and U-shaped assembly line balancing problems for algorithm segments, including initial generation, encoding and decoding schemes, and GA’s operations, an new approach is proposed to promote population diversity and search efficiency. Finally, the proposed algorithm is evaluated though several benchmarks as well as that of exact method. The obtained results proved high efficiency of our method over other approaches suggested in the literature.

A novel competitive hybrid approach based on grouping evolution strategy algorithm for solving U-shaped assembly line balancing problems

Assembly line balancing problems (ALBPs) are among the well-known problems in manufacturing systems that belong to NP-hard class of problems. In the literature, there are various metaheuristic methods proposed to solve different models of such a problem under various assumptions. This research considers the U-shaped ALBP and proposes a hybrid solution method based on grouping evolution strategy algorithm. To develop a competitive approach, two most popular constructive methods of solving ALBP including the ranked positional weight method, and COMSOAL algorithm are modified and improved. We investigate the effectiveness of the proposed improvements and evaluate the performance of the proposed approach via solving a number of existing problems in the literature and compare the results with some current methods in the literature. Computational results indicate that the proposed approach for solving U-shaped ALBP test problems performs efficiently and is able to obtain the global optimal solution of the most of high dimensional problems.

An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.

On the MILP model for the U-shaped assembly line balancing problems

U-shaped assembly lines are an important configuration of modern manufacturing systems due to their flexibility to adapt to varying market demands. In U-shaped lines, tasks are assigned after their predecessors or successors. Some MILP models have been proposed to formulate the U-shaped assembly line balancing problem using either–or constraints to express precedence relationships. We show that this modeling approach reported in the literature may often find optimal solutions that are infeasible and verify this on a large set of benchmark problems. We present a revision to this model to accurately express the precedence relationships without introducing additional variables or constraints. We also illustrate on the same benchmark problems that our revision always reports solutions that are feasible.

Bacterial Foraging Optimization Algorithm for assembly line balancing

Assembly line balancing is the problem of assigning tasks to workstations by optimizing a performance measure while satisfying precedence relations between tasks and cycle time restrictions. Many exact, heuristic and metaheuristic approaches have been proposed for solving simple straight and U-shaped assembly line balancing problems. In this study, a relatively new optimization algorithm, Bacterial Foraging Optimization Algorithm (BFOA), based heuristic approach is proposed for solving simple straight and U-shaped assembly line balancing problems. The performance of the proposed algorithm is evaluated using a well-known data set taken from the literature in which the number of tasks varies between 7 and 111, and results are also compared with both an ant-colony-optimization-based heuristic approach and a genetic-algorithm-based heuristic approach. The proposed algorithm provided optimal solutions for 123 out of 128 (96.1 %) test problems in seconds and is proven to be promising.

Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm

Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered; minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.

Cellular Genetic Algorithm with Communicating Grids for Assembly Line Balancing Problems

This paper presents a new approach with cellular multigrid genetic algorithms for the I-shaped and U-shaped assembly line balancing problems, including parallel workstations and compat ...

Simple and U-type Assembly Line Balancing by Using an Ant Colony Based Algorithm

In this paper, an Ant Colony Optimization (ACO) based heuristic algorithm is proposed for solving simple (straight line) and U-shaped assembly line balancing problems (ALBP). The paper makes one of the first attempts to show how ACO heuristic can be used to solve U-shaped ALBP. A new algorithm is proposed in this paper that seamlessly integrates COMSOAL algorithm, Ranked Positional Weight Heuristic (RPWH) and an ACO based heuristic in order to obtain good solutions to simple and U-shaped ALBPs. The result of the computational study has shown that the proposed algorithm is effective in solving simple/U-shaped line balancing problems.

U-shaped assembly line balancing problem with genetic algorithm

This paper presents a multi-objective genetic algorithm (moGA) to solve the U-shaped assembly line balancing problem (UALBP). As a consequence of introducing the just-in-time (JIT) production principle, it has been recognized that U-shaped assembly line systems offer several benefits over the traditional straight line systems. We consider both the traditional straight line system and the U-shaped assembly line system, thus as an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.

U-OPT: An analysis of exact U-shaped line balancing procedures

The U-shaped assembly line-balancing problem can be solved using optimization procedures or algorithms, including branch-and-bound procedures. This paper considers design elements that should be included in these solution methods for solving the U-shaped assembly line-balancing problem. New solution procedures are proposed and compared experimentally with several existing procedures using a variety of problem sets from the literature. The results show that the substantial improvement in the efficacy of the new solution procedures over existing methods is due primarily to the newly developed 'Paired Tasks' lower bound. Results also show the relative importance of various design elements comprising a branch-and-bound procedure.