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

Sabuncuoğlu, İhsan; Erel, Erdal; Alp, Arda

doi:10.1016/j.ijpe.2008.11.017

Cited by 90 publications

(30 citation statements)

References 59 publications

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“…The experimental results show that the algorithm is a state-of-the-art method for this problem. Sabuncuoglu et al [44] presented ant colony optimization for the single model U-type assembly line balancing problem.…”

Section: Assembly Line Balancingmentioning

confidence: 99%

A Literature Survey on Metaheuristics in Production Systems

Onar

Öztayşi

Kahraman

et al. 2016

Operations Research/Computer Science Interfaces Series

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Typical problems in production systems are suboptimal production scheduling, long manufacturing lead times, inefficient inventory control, low work center utilization, etc. The solutions of these problems may need complex techniques while the classical techniques are insufficient to solve them. In this chapter we first classified the problems which may be faced in production systems and then the solution techniques called metaheuristics.

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Section: Assembly Line Balancingmentioning

confidence: 99%

A Literature Survey on Metaheuristics in Production Systems

Onar

Öztayşi

Kahraman

et al. 2016

Operations Research/Computer Science Interfaces Series

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“…[10] and an ant colony optimisation based approach by Sabuncuoglu et al [11]) solution approaches were developed to solve the U-shaped assembly line balancing problem in its traditional form where there is only one line on which a single model is produced. Among these studies, Scholl and Klein [7] and Hwang et al [10] also aimed at minimising cycle time and obtaining a smooth workload distribution, respectively, as well as minimising the number of workstations as a common objective.…”

Section: Review Of the Literaturementioning

confidence: 99%

Balancing of parallel U-shaped assembly lines

Küçükkoç

Zhang

2015

Computers & Operations Research

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A new hybrid assembly line design, called Parallel U-shaped Assembly Line system, is introduced and characterised along with numerical examples for the first time. Different from existing studies on U-shaped lines, we combine the advantages of two individual line configurations (namely parallel lines and U-shaped lines) and create an opportunity for assigning tasks to multi-line workstations located in between two adjacent U-shaped lines with the aim of maximising resource utilisation.Utilisation of crossover workstations, in which tasks from opposite areas of a same U-shaped line can be performed, is also one of the main advantages of the U-shaped lines. As in traditional U-shaped line configurations, the newly proposed line configuration also supports the utilisation of crossoverworkstations. An efficient heuristic algorithm is developed to find well-balanced solutions for the proposed line configurations. Test cases derived from existing studies and modified in accordance with the proposed system in this study are solved using the proposed heuristic algorithm. The

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“…ACO has been promisingly applied to a number of engineering optimization problems, such as assembly line balancing [33][34][35], the transportation scheduling problem [36], the travelling salesman problem [37], process planning and scheduling [38], the flowshop scheduling problem [39,40]), and many more.…”

Section: Ant Colony-based Heuristicmentioning

confidence: 99%

A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem

Fathi

Rodríguez

Álvarez

2014

Int J Adv Manuf Technol

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In recent years, part feeding at assembly lines has become a critical issue as the result of a high level of product customization. The assembly line part feeding problem is a complex problem in which a number of decisions should be made in order to select the right quantity of each part to be supplied at the right time under a set of constraints. This study aims to cope with the part feeding problem at assembly lines by introducing a new memetic ant colony optimization-based heuristic algorithm. Due to the novelty of the problem and in order to evaluate the performance of the proposed memetic algorithm, a mathematical formulation is also presented. Case data from an automobile manufacturer and a set of generated instances are used to test both the mathematical model and the proposed memetic algorithm. The results reveal that although it is computationally difficult to solve the problem using the exact mathematical model, the mimetic algorithm was able to find sufficiently good solutions in a short period of time.

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

Cited by 90 publications

References 59 publications

A Literature Survey on Metaheuristics in Production Systems

A Literature Survey on Metaheuristics in Production Systems

Balancing of parallel U-shaped assembly lines

A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem

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