Mixed-model Assembly Line Balancing Problem Research Articles

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. The general approach to determine the appropriate parameter combination of GA depends on too many trials of different combinations, and the best one of them that produces good results is selected for the programme, which would be used for problem solving. A few researchers studied on the parameter optimisation of GA. In this paper, response surface-dependent parameter optimisation is proposed to determine the optimal parameters of GA. Results are tested for benchmark problems that are most common in mixed-model assembly line balancing problems of type-I.

PSONK: PARTICLE SWARM OPTIMIZATION WITH NEGATIVE KNOWLEDGE FOR MULTI-OBJECTIVE U-SHAPED ASSEMBLY LINES BALANCING WITH PARALLEL WORKSTATIONS

Particle swarm optimization (PSO) is a metaheuristic method inspired by the swarming behavior observed in flocks of birds. This paper proposes a novel PSO method, namely the PSO algorithm with negative knowledge (PSONK), to solve the multi-objective mixed-model assembly line balancing (MULB) problem with parallel workstations. PSONK employs the knowledge of relative positions of different particles as opposed to traditional PSO in generating new solution strings. The knowledge about poor solutions is also utilized to avoid the pairs of adjacent tasks appeared in the poor solutions from being selected as parts of the new solution strings in the next generation. The concept of Pareto optimality is employed to allow the conflicting objectives to be optimized simultaneously. Experimental results show clearly that PSONK is a promising algorithm. In addition, PSONK embedded with an appropriate local search (M-PSONK) can result in improved performances.

A new hybrid genetic algorithm to solve more realistic mixed-model assembly line balancing problem

Continuous and unexpected changes in demands of customised products force companies to produce various types of products, concurrently. One of the intelligent methods to satisfy various customer demands and compete with rivals in today’s business environment is to assemble diverse models on the same assembly line, simultaneously. So, mixed-model assembly line balancing problem with parallel workstations and zoning constraints is studied in this paper. Firstly, relevant studies in the literature were presented in a summary. Then, solutions have been sought with hybrid genetic algorithm that is obtained from the combination of modified Comsoal method and genetic algorithm. Computational experiments were carried out on 20 benchmark problems to demonstrate the superiority of the proposed algorithm. The obtained results were compared with the results of pure genetic algorithm and other previous researches. Obviously, it has been observed that proposed algorithm has promising solution capacity especially on large-sized mixed-model assembly line balancing problems.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.

Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge

Particle swarm optimisation (PSO) is an evolutionary metaheuristic inspired by the swarming behaviour observed in flocks of birds. The applications of PSO to solve multi-objective discrete optimisation problems are not widespread. This paper presents a PSO algorithm with negative knowledge (PSONK) to solve multi-objective two-sided mixed-model assembly line balancing problems. Instead of modelling the positions of particles in an absolute manner as in traditional PSO, PSONK employs the knowledge of the relative positions of different particles in generating new solutions. The knowledge of the poor solutions is also utilised to avoid the pairs of adjacent tasks appearing in the poor solutions from being selected as part of new solution strings in the next generation. Much of the effective concept of Pareto optimality is exercised to allow the conflicting objectives to be optimised simultaneously. Experimental results clearly show that PSONK is a competitive and promising algorithm. In addition, when a local search scheme (2-Opt) is embedded into PSONK (called M-PSONK), improved Pareto frontiers (compared to those of PSONK) are attained, but longer computation times are required.

Mixed-model assembly line balancing using a multi-objective ant colony optimization approach

Mixed-model assembly lines are production systems at which two or more models are assembled sequentially at the same line. For optimal productivity and efficiency, during the design of these lines, the work to be done at stations must be well balanced satisfying the constraints such as time, space and location. This paper deals with the mixed-model assembly line balancing problem (MALBP). The most common objective for this problem is to minimize the number of stations for a given cycle time. However, the problem of capacity utilization and the discrepancies among station times due to operation time variations are of design concerns together with the number of stations, the line efficiency and the smooth production. A multi-objective ant colony optimization (MOACO) algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the MOACO algorithm is an efficient and effective algorithm which gives better results than other methods compared.

A novel two-stage genetic algorithm for a mixed-model U-line balancing problem with duplicated tasks

A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of Operational Research, 174:492–508, 2006) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.

Balancing and sequencing of parallel mixed-model assembly lines

In this paper, a simulated annealing approach is developed for the parallel mixed-model assembly line balancing and model sequencing (PMMAL/BS) problem which is an extension of the parallel assembly line balancing (PALB) problem introduced by Gökçen et al. (2006). In PALB, the aim is to balance more than one assembly line together. Balancing of the lines simultaneously with a common resource is very important in terms of resource minimisation. The proposed approach maximises the line efficiency and distributes the workloads smoothly across stations. The proposed approach is illustrated with two numerical examples and its performance is tested on a set of test problems. The computational results show that the proposed approach is very effective for PMMAL/BS.

An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time

Mixed-model assembly lines (mALs) are becoming more and more important by producing different models of the same product on an assembly line. How to calculate the cycle time based on demand of different models also making problem more difficult. According to different work experiences and skill level, the processing time of a given task and the operating costs such as wages differ among workers. Appointing the proper worker to the proper station and assigning the suitable task to the suitable station in order to decrease the cycle time, increase the line efficiency, and reduce the total cost make the problem more complex. This paper proposes a new concept for calculating the cycle time based on demand ratio of each model and another one for calculating the human resource cost. A generalized Pareto-based scale-independent fitness function genetic algorithm (gp-siffGA) is described for solving mixed-model assembly lines balancing (mALB) problems to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. The gp-siffGA uses Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. Comparisons with existing multiobjective genetic algorithms demonstrate that our approach efficiently solves mALB problems.

Hybrid Behavior Ant Colony Optimization for Mixed-model Assembly Line Balancing Problem

For the purpose of solving mixed-model assembly line balancing problem (MMALBP), MMALBP is transformed into a single-model assembly line balancing problem with a combined precedence diagram. A hybrid behavior ant colony optimization with pheromone summation rules for MMALBP is proposed. The proposed algorithm makes use of the trail information which is deposited between the task and the task selected position, and pheromone summation rules are adopted. Hybrid search mechanism, which comprehensively considers utilization, exploration and random search, are adopted. Global pheromone trail update and local pheromone trail update are considered. The heuristic information is set to the position weight for tasks of MMALBP, which collaboratively considers the operation time of assembly task and the number of follow-up tasks. Finally, example verification is carried out, and which indicates the validity of the proposed algorithm.

A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs

A common assumption in the literature on mixed-model assembly line balancing is that a task that is common to multiple models must be assigned to a single station. In this paper, we relax this restriction, and allow a common task to be assigned to different stations for different models. We seek to minimize the sum of costs of the stations and the task duplication. We develop an optimal solution procedure based on a backtracking branch-and-bound algorithm and evaluate its performance via a large set of experiments. A branch-and-bound based heuristic is then developed for solving large-scale problems. The heuristic solutions are compared with a lower bound and experiments show that the heuristic provides much better solutions than those obtained by traditional approaches.

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines. This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators. The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.

A heuristic for solving mixed-model line balancing problems with stochastic task durations and parallel stations

This paper describes an approach for solving a mixed-model assembly line-balancing problem with stochastic task times when paralleling of tasks within work centers is permitted. The research modifies previous work and incorporates new and existing task selection rules for assigning tasks to work centers. The heuristic is applied to six different line-balancing problems for each presented rule. The resulting layouts are simulated and performance results are analyzed. Through simulation, the research demonstrates how the presented heuristic can be used to solve complex line-balancing problems using different strategies, and these strategies can be evaluated across many performance dimensions.

A goal programming approach to mixed-model assembly line balancing problem

In this paper, a binary goal programming model for the mixed-model assembly line balancing (ALB) problem is developed. The model is based on the concepts developed by Patterson and Albracht [1] and the model of Deckro and Rangachari [2] developed for the single-model ALB problem. The proposed model provides a considerable amount of flexibility to the decision maker since several conflicting goals can be simultaneously considered.