U-type Assembly Line Research Articles

MULTI OBJECTIVE MODEL FOR BALANCING U-TYPE ASSEMBLY LINE WITH PERMANENT AND TEMPORARY WORKERS

Assembly line balancing is one of the most important management decisions for reducing production costs. So far, many researches have been done in this area. In this research, we expand the U-type line-balancing using goal programming for multi objective model with two goals, i.e., minimized the number of temporary workers and number of cycle time in each station. Additionally, an example to illustrate the model, as well as some analyses is presented. Different amount of time for temporary worker to accomplish their tasks were generated. For some cases, the U-type assembly line is more efficient than the straight one. However, the goal 1 and goal 2 are conflicted

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.

Ant colony optimization for the single model U-type assembly line balancing problem

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them.

A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.

Simple and U-type assembly line balancing problems with a learning effect

In this paper, we introduced learning effect into assembly line balancing problems. In many realistic settings, the produced worker(s) (or machine(s)) develops continuously by repeated the same or similar activities. Therefore, the production time of product shortens if it is processed later. We show that polynomial solutions can be obtained for both simple assembly line balancing problem (SALBP) and U-type line balancing problem (ULBP) with learning effect.

Stochastic U-line balancing using genetic algorithms

The advantages of U-type lines are very well known in industry. They offer improved productivity and quality, and are considered as one of the better techniques in implementing just-in-time (JIT) systems. There is a growing interest in the literature to organize traditional assembly lines as U-lines for improved performance. U-type assembly line balancing is an extension of the traditional line balancing problem, in which tasks can be assigned from both sides of the precedence diagram. Although there are many studies in the literature for the design of traditional straight assembly lines, the work on U-type lines is limited. Moreover, in most of the previous studies, task times are assumed to be deterministic. In this paper, a new multiple-rule-based genetic algorithm (GA) is proposed for balancing U-type assembly lines with stochastic task times.

A shortest route formulation of simple U-type assembly line balancing problem

In this paper, a shortest route formulation of simple U-type assembly line balancing (SULB) problem is presented and illustrated on a numerical example. This model is based on the shortest route model developed in [Manage. Sci. 11 (2) (1964) 308.] for the traditional single model assembly line balancing problem.