A genetic algorithm for robotic assembly line balancing

Levitin, Gregory; Rubinovitz, Jacob; Shnits, Boris

doi:10.1016/j.ejor.2004.07.030

Cited by 184 publications

(26 citation statements)

References 15 publications

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“…The literature on type II straight RALB problems is discussed below. Levitin, Rubinovitz, and Shnits (2006) first developed an algorithm for solving RALB-II problems. Their method aims to distribute tasks to the workstation in a balanced manner and to allocate the best robot to these workstations to perform the tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The assumptions used in this article, based on the work by Levitin, Rubinovitz, and Shnits (2006) and Gao et al (2009), are listed below.…”

Section: Mathematical Model For Robotic U-shaped Assembly Line Balancingmentioning

confidence: 99%

“…A consecutive heuristic procedure proposed by Levitin, Rubinovitz, and Shnits (2006) for straight line assembly line is adopted in this article. This heuristic allocates tasks and robots to workstations with an objective of minimizing the cycle time of the assembly line.…”

Section: Fitness Evaluation (Task and Robot Allocation)mentioning

confidence: 99%

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Robotic U-shaped assembly line balancing using particle swarm optimization

Nilakantan

Ponnambalam

2015

Engineering Optimization

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Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

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Section: Literature Reviewmentioning

confidence: 99%

“…The assumptions used in this article, based on the work by Levitin, Rubinovitz, and Shnits (2006) and Gao et al (2009), are listed below.…”

Section: Mathematical Model For Robotic U-shaped Assembly Line Balancingmentioning

confidence: 99%

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Robotic U-shaped assembly line balancing using particle swarm optimization

Nilakantan

Ponnambalam

2015

Engineering Optimization

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“…In this way, CAD systems can develop new configurations and alternative modifications to the geometry, and supply the designer with a set of alternative modifications, defined automatically based on the selected principles, which may themselves be applied based on the concept of cataclysmic mutations [24]. Mutations following a pattern similar to a ''cataclysm'' are now being studied in the area of evolutionary algorithms as tools for finding innovation [25,26]. The extension of the solution can reach a level of detail that leads to two possibilities: either automatic development of solutions by the genetic algorithm with or without the use of cataclysmic operators or, on a more detailed level inspired by these suggestions, the designer can select the most suitable solution and implement it.…”

Section: Triz and Cataclysmic Operatorsmentioning

confidence: 99%

Development of an engine crankshaft in a framework of computer-aided innovation

Albers

León-Rovira

Aguayo

et al. 2009

Computers in Industry

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“…The most typical anthropomorphic or human like characteristic of a robot is its arm. The arm, together with the robot's capacity to be programmed, makes it ideally suited for a variety of production tasks, including machine loading, spot welding, spray painting, as well as assembly of parts such as screwing and drilling [4][5][6]. Flexibility and automation in assembly lines can be achieved by the use of robots [7].…”

Section: Robots In Flexible Manufacturing Systemsmentioning

confidence: 99%

Optimizing the Robot Arm Movement Time Using Virtual Reality Robotic Teaching System

Tahriri¹,

Mousavi²,

Yap³

2015

Int. j. simul. model.

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Robots play an important role in performing operations such as welding, drilling and screwing parts in manufacturing. Optimizing the robot arm movement time between different points is an important task which will minimize the make-span and maximize the production rate. But robot programming is a complex task whereby the user needs to teach and control the robot in order to perform a desired action. In order to address the above problem, an integrated 3-dimensional (3D) simulation software and virtual reality (VR) system is developed to simplify and speed up tasks and therefore enhance the quality of manufacturing processes. This system has the capability to communicate, transfer, optimize and test the data obtained from the VR and 3D environment to the real robot in a fast and efficient manner. In addition, this system eliminates the need for robot programming, and thus it is easily implemented by users with limited engineering knowledge. The optimization model is tested on a test case, in which the data are extracted from the VR system. The results show an increase in production rate and a decrease in cycle time when the make-span is minimized. The virtual reality robotic teaching system (VRRTS) offers several benefits to users, and will therefore surpass complex and time-intensive conventional robot programming methods.

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A genetic algorithm for robotic assembly line balancing

Cited by 184 publications

References 15 publications

Robotic U-shaped assembly line balancing using particle swarm optimization

Robotic U-shaped assembly line balancing using particle swarm optimization

Development of an engine crankshaft in a framework of computer-aided innovation

Optimizing the Robot Arm Movement Time Using Virtual Reality Robotic Teaching System

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