Balancing and scheduling assembly lines with human-robot collaboration tasks

Nourmohammadi, Amir; Fathi, Masood; Ng, Amos H. C.

doi:10.1016/j.cor.2021.105674

Cited by 72 publications

(19 citation statements)

References 18 publications

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“…The cost function aims at reducing the total makespan, and the solution is obtained with a constraint programming model and the use of a Genetic Algorithm (GA). In [23], instead, the authors propose the use of a Simulated Annealing (SA) algorithm to find the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration

Pupa

Dijk

Brekelmans

et al. 2022

Sensors

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Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.

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Section: Related Workmentioning

confidence: 99%

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration

Pupa

Dijk

Brekelmans

et al. 2022

Sensors

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“…39 Therefore, parameters of the MRGGA algorithm are carefully determined after conducting the Taguchi method. Details of the Taguchi method and its step by step process can be found in Nourmohammadi et al 40 Table 2 demonstrates the considered levels for each parameter, and Figure 12 shows the obtained signal-to-noise (SN) ratios by performing the Taguchi method. As a result of the conducted experiments, the decided values for the parameters in this research are 0.7 for cross_rate, 0.3 for mut_rate, 100 for popsize, 10 for Num_Good and Num_Bad, and 20 for ter_num.…”

Section: Parameter Settingmentioning

confidence: 99%

“…Results also show that MRGGA requires more CPU time than RGGA. To further evaluate the performance of the MRGGA as compared to RGGA, two indices of relative improvement percentage (RIP) and RIP per second Process time U [40,60] Distances U [40,60] Vehicle transportation speed U [1,3] Supplier production speed U [1,3] Occupied space by orders U [20,60] Vehicle capacity U [5,20] Quality of production U [1,5] Energy consumption by suppliers U [1,5] Fuel…”

Section: Comparison With Rggamentioning

confidence: 99%

Energy‐efficient and sustainable supply chain in the manufacturing industry

Beheshtinia

Fathi

2022

Energy Science & Engineering

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This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

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“…The results indicate a significant increase in the productivity of the assembly area. One of the reasons the authors cite the ability to allocate human and robotic resources in real time for the work plan execution [9]. Also, collaborative works can support the execution of tasks by a person at low costs [1], thus ensuring the profitability of partial automation of the assembly process.…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of Collaborative Assembling Cells

Andrusyshyn

Luściński

Ivanov

et al. 2022

JES

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To produce competitive products, it is necessary to consider their permanent modernization and adaptation to the emerging needs of a consumer. This feature of up-to-date production inevitably leads to design complexities. As a result, the complexity of a technological assembly process increases, which is a new challenge for enterprises. Simultaneously, in most cases, assembly operations are performed manually due to the complexity or impossibility of automating the assembling process for an extensive range of products. This fact is due to the insufficient flexibility of automation systems. Remarkably, this approach has significant drawbacks, i.e., low productivity and risk of chronic diseases. To solve this problem, the use of collaborative systems was proposed. Such systems have the advantages of both humans and automation tools. As a result, industrial robots can be applied as automation tools. However, when using industrial robots next to workers, the safety requirements are significantly increased since the infliction of industrial injuries is unacceptable. After considering all the above, the article deals with a new scientific and methodological approach to designing security systems of collaborative production cells and their design and effectiveness verification.

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Balancing and scheduling assembly lines with human-robot collaboration tasks

Cited by 72 publications

References 18 publications

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration

Energy‐efficient and sustainable supply chain in the manufacturing industry

The Efficiency of Collaborative Assembling Cells

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