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Improving productivity using a multi-objective optimization of robotic trajectory planning

Author

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  • Llopis-Albert, Carlos
  • Rubio, Francisco
  • Valero, Francisco

Abstract

This study presents a methodology to tackle robot tasks in a cost-efficient way. It poses a multi-objective optimization problem for trajectory planning of robotic arms that an efficient algorithm will solve. The method finds the minimum time to perform robot tasks while considering the physical constraints of the real working problem and the economic issues participating in the process. This process also considers robotic system dynamics and the presence of obstacles to avoid collisions. It generates an entire set of equally optimal solutions for each process, the Pareto-optimal frontiers. They provide information about the trade-offs between the different decision variables of the multi-objective optimization problem. This procedure can help managers in decision-making processes regarding performing tasks, items to be manufactured or robotic services performed to meet with the current demand, and also, to define an efficient scheduling. It improves productivity and allows firms to stay competitive in rapid changing markets.

Suggested Citation

  • Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2015. "Improving productivity using a multi-objective optimization of robotic trajectory planning," Journal of Business Research, Elsevier, vol. 68(7), pages 1429-1431.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:7:p:1429-1431
    DOI: 10.1016/j.jbusres.2015.01.027
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    References listed on IDEAS

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    1. Fish, Kelly E. & Johnson, John D. & Dorsey, Robert E. & Blodgett, Jeffery G., 2004. "Using an artificial neural network trained with a genetic algorithm to model brand share," Journal of Business Research, Elsevier, vol. 57(1), pages 79-85, January.
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    Cited by:

    1. Carlos Llopis-Albert & Francisco Rubio & Francisco Valero, 2021. "Modelling an Industrial Robot and Its Impact on Productivity," Mathematics, MDPI, vol. 9(7), pages 1-13, April.
    2. Secundo, Giustina & Riad Shams, S.M. & Nucci, Francesco, 2021. "Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management," Journal of Business Research, Elsevier, vol. 131(C), pages 563-572.
    3. Rubio, Francisco & Llopis-Albert, Carlos & Valero, Francisco, 2021. "Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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