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Patrício, L.; Costa, L.; Varela, L.; Ávila, P. Sustainable Implementation of Robotic Process Automation. Encyclopedia. Available online: https://encyclopedia.pub/entry/52094 (accessed on 27 July 2024).
Patrício L, Costa L, Varela L, Ávila P. Sustainable Implementation of Robotic Process Automation. Encyclopedia. Available at: https://encyclopedia.pub/entry/52094. Accessed July 27, 2024.
Patrício, Leonel, Lino Costa, Leonilde Varela, Paulo Ávila. "Sustainable Implementation of Robotic Process Automation" Encyclopedia, https://encyclopedia.pub/entry/52094 (accessed July 27, 2024).
Patrício, L., Costa, L., Varela, L., & Ávila, P. (2023, November 27). Sustainable Implementation of Robotic Process Automation. In Encyclopedia. https://encyclopedia.pub/entry/52094
Patrício, Leonel, et al. "Sustainable Implementation of Robotic Process Automation." Encyclopedia. Web. 27 November, 2023.
Sustainable Implementation of Robotic Process Automation
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This entry is adapted from the peer-reviewed paper 10.3390/su152015045

The concept of sustainability has garnered growing international focus across various spheres, including the general public, academia, and the corporate realm. The World Commission on Environmental Development (WCED) defined sustainable development as the advancement that fulfills the present requirements while safeguarding the capacity of forthcoming generations to fulfill their own necessities.

sustainability robotic process automation multi-objective optimization

1. Introduction

The concept of sustainability has garnered growing international focus across various spheres, including the general public, academia, and the corporate realm. The World Commission on Environmental Development (WCED) defined sustainable development as the advancement that fulfills the present requirements while safeguarding the capacity of forthcoming generations to fulfill their own necessities [1]. The significance of societal concerns and the natural ecosystem for communities and enterprises has undergone a profound transformation over the last five decades. Corporate executives are increasingly recognizing the imperative to broaden their objectives beyond conventional financial anticipations. Sustainability endeavors to harmonize economic, societal, and environmental advancement, ensuring the well-being of both current and future generations [2].
Robotic Process Automation (RPA) is a technology that involves the use of software to automate repetitive, low-value, rule-based tasks within a business process [3]. These tasks are typically routine and highly structured and require interaction with existing software systems [4]. However, in an increasingly sustainability-conscious business landscape, the implementation of RPA should be carefully evaluated to ensure that it is not only effective but also sustainable in the long run [4]. RPA can drive sustainability by optimizing tasks, reducing waste, and promoting greater energy efficiency in business activities [5]. RPA has emerged as an innovative technological solution that aims to optimize operational efficiency, reduce costs, and enhance work quality through task automation [5].

2. Sustainability and Robotic Process Automation

Sustainability refers to the ability to meet present needs without compromising the ability of future generations to meet their own needs. In other words, it is a principle that aims to balance economic, social, and environmental development in order to ensure that natural resources and social conditions are preserved and maintained over time [6]. Organizations have embraced sustainability to address their social responsibilities and comply with environmental legislation. It means a new paradigm for organizations, emphasizing the significance of businesses that prioritize their social, environmental, and economic responsibilities. These responsibilities have gained value in society and in the regulations that govern companies following this trend. Sustainability presents a challenge for many organizations, but overcoming this challenge relies on establishing good relationships with stakeholders, including customers, suppliers, employees, and society as a whole. Since the introduction of the term sustainability in business, more companies have emerged that incorporate sustainability into their activities, thereby enhancing their economic, environmental, and social objectives [7][8][9][10]. Organizations striving for sustainability should focus on three fundamental pillars: economic, social, and ecological [11][12][13]. A Special Issue on governance and sustainability [14] reinforces the value of the theme and emphasizes the importance of the word ‘sustainability’. It explains why sustainability has become the primary driver of innovation based on a study of sustainability initiatives involving 30 large corporations [15][16].
Technological advancements have revolutionized various sectors, and Robotic Process Automation (RPA) emerges as a promising solution for optimizing and streamlining business processes. In this chapter, the benefits of RPA and its impact on different sectors are explored. By providing a concise definition of RPA and a more engaging presentation, the aim was to capture the reader’s interest in the topic and its advantages [17].
Robotic Process Automation is a technology that enables the automation of rule-based, repetitive tasks through software robots [18]. These robots can mimic human actions in digital systems, performing activities such as form filling, data processing, and interaction with graphical interfaces [19].
The implementation of RPA is facilitated by specialized tools that allow the creation and management of robots [20]. These tools enable the capture of steps in a manual process, the creation of automated workflows, and the integration with existing systems. As a result, RPA can be deployed in an agile and flexible manner, delivering significant efficiency gains [21].
The benefits of RPA are extensive and directly impact organizations. By dividing these benefits into individual points, it is possible to emphasize each of them more clearly and emphatically. Recent studies have shown that RPA brings advantages such as increased operational efficiency, cost reduction, improved quality and accuracy, resource allocation for higher-value tasks, agility, and scalability [22].

3. Concepts about Production Planning and Scheduling Problems

Planning involves defining objectives, determining actions, and allocating resources to achieve desired outcomes. On the other hand, scheduling deals with sequencing activities or tasks within specific constraints. Integrating planning and scheduling enables efficient resource allocation, the minimization of costs, and the meeting of project deadlines [23].
Heuristic algorithms have been widely used to solve planning and scheduling problems. These methods employ intuitive rules and strategies to find near-optimal solutions within a reasonable time frame. Metaheuristic algorithms such as simulated annealing have been successfully applied to various planning and scheduling problems [24]. The authors of [25] proposed a hybrid metaheuristic algorithm that combines genetic algorithms and particle swarm optimization to solve a complex project scheduling problem.
Many planning and scheduling problems can be formulated as constraint satisfaction problems (CSPs), where the goal is to find a feasible assignment of values to variables that satisfy a set of constraints. Backtracking, constraint propagation, and local search techniques have been widely employed to solve CSPs [26]. Researchers have applied CSPs to vehicle routing problems, resource allocation, and job shop scheduling [27].
Another way to solve planning and scheduling problems is through multi-objective approaches. In recent years, multi-objective optimization has gained significant attention due to its ability to address complex decision-making problems involving conflicting objectives. Simultaneously, planning and scheduling have emerged as crucial components in various domains, including manufacturing, transportation, and project management.
Multi-objective optimization deals with problems that have multiple conflicting objectives. Its aim is to find a set of solutions, known as the Pareto front, that represents the trade-offs between these objectives. The application of multi-objective optimization techniques has yielded valuable insights in diverse fields. For example, [28] investigated multi-objective optimization in sustainable supply chain management and demonstrated its effectiveness in cost reduction and environmental impact reduction.
Evolutionary algorithms, such as genetic algorithms and particle swarm optimization, have been extensively used to solve multi-objective problems. These algorithms employ a population-based approach, simulating natural evolution to search for optimal solutions. In [29], the authors proposed the widely known NSGA-II algorithm, which combines non-dominated sorting and crowding distance to enhance the diversity and convergence of solutions.
Selecting a solution from the Pareto front requires decision-making methods that consider the decision-maker’s preferences. Aggregation methods, such as the Weighted Sum and ε-constraint, and interactive methods, like reference-point-based methods and visual analytics, have been explored [30]. In [31], the hyper-volume indicator was introduced, providing a quantitative measure of the quality of a set of solutions, enabling informed decision making.

References

  1. World Commission on Environment and Development (WCED). Our Common Future. In The Brundtland Report; Oxford University Press: Oxford, UK, 1987.
  2. Bansal, P. Evolving sustainably: A longitudinal study of corporate sustainable development. Strateg. Manag. J. 2005, 26, 123–136.
  3. Asatiani, A.; Penttinen, E. Turning robotic process automation into commercial success—Case Opus Capita. J. Inf. Technol. Teach. Cases 2016, 6, 67–74.
  4. Leopold, H.; Van der Aa, H.; Reijers, H.A. Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions. Lect. Notes Bus. Inf. Process. 2018, 318, 67–81.
  5. Gartner. Predicts 2020: RPA Renaissance Driven by Morphing Offerings and Zeal for Operational Excellence. Technical Report. 2020. Available online: https://www.gartner.com/en/documents/3976135 (accessed on 14 July 2023).
  6. Putnik, G.; Ávila, P. Governance and Sustainability (Special Issue Editorial). Int. J. Ind. Syst. Eng. 2016, 24, 137–143.
  7. Camarinha-Matos, L.M.; Afsarmanesh, H. Collaborative networks: A new scientific discipline. J. Intell. Manuf. 2005, 16, 439–452.
  8. Demirkan, I.; Deeds, D.; Demirkan, S. Exploring the Role of Network Characteristics, Knowledge Quality, and Inertia on the Evolution of Scientific Networks. J. Manag. 2013, 39, 1462–1489.
  9. Powell, W.; White, D.; Koput, K.; Owen-Smith, J. Network dynamics and field evolution: The growth of inter-organizational collaboration in the life sciences. Am. J. Sociol. 2005, 110, 1132–1205.
  10. Camarinha-Matos, L.; Afsarmanesh, H. Redes Colaborativas: Modelagem de Referência; Springer: Berlin/Heidelberg, Germany, 2008; pp. 139–164. ISBN 978-0-387-79426-6.
  11. Ávila, P. Modelo Rigoroso de Seleção de Sistemas de Recursos para o Projeto de Empresas Ágeis/Virtuais para Produtos Complexos. Ph.D. Thesis, Universidade do Minho, Braga, Portugal, 2004.
  12. Ávila, P.; Mota, A.; Bastos, J.; Patrício, L.; Pires, A.; Castro, H.; Cruz-Cunha, M.; Varela, L. Framework for a risk assessment model to apply in Virtual/Collaborative Enterprises. Procedia Comput. Sci. 2020, 181, 612–618.
  13. Cruz-Cunha, M.M. Organization of a Market of Resources for Agile and Virtual Enterprises Integration. Ph.D. Thesis, Universidade do Minho, Braga, Portugal, 2013.
  14. Cunha, M.M.; Putnik, G.D.; Gunasekaran, A.; Ávila, P. Market of resources as a virtual enterprise integration enabler. In Virtual Enterprise Integration: Technological and Organizational Perspectives; IGI Global: Hershey, PA, USA, 2005.
  15. Lange, D.E.; Busch, T.; Delgado-Ceballos, J. Sustaining Sustainability in Organizations. J. Bus. Ethics 2012, 110, 151–156.
  16. Brundtland, G.H.; Khalid, M.; Agnelli, S.; Al-Athel, S.; Chidzero, B. Our Common Future; Brundtland Report: New York, NY, USA, 1987; Volume 8.
  17. Kaur, J. Robotic Process Automation in Healthcare Sector. In E3S Web of Conferences; EDP Sciences: Les Ulis, France, 2023; Volume 391.
  18. Gajjar, N.; Rathod, K.; Jani, K. A Systematic Literature Review on Robotic Process Automation Security; Cornell University: Ithaca, NY, USA, 2022.
  19. Kuppan, S. Robotic Process Automation (RPA) to Automate Business Process. BJIB 2023 Information Technology and Communications. 2022. Available online: https://www.researchgate.net/publication/366012844_ROBOTIC_PROCESS_AUTOMATION_RPA_TO_AUTOMATE_BUSINESS_PROCESS (accessed on 1 May 2023).
  20. Eulerich, M.; Waddoups, N.; Wagener, M.; Wood, D. The Dark Side of Robotic Process Automation (RPA): Understanding Risks and Challenges with RPA. Account. Horiz. 2023, 21, 1–10.
  21. Chakraborty, A.; Bhattacharyya, S.; De Debashis, M.; Banerjee, J. Intelligent Automation Framework Using AI and RPA: An Introduction. In Confluence of Artificial Intelligence and Robotic Process Automation; Springer Nature: Singapore, 2023; pp. 1–13.
  22. Seshadri, A.; Anand, P.; Parameswari, M.; Sasikala, M. Automated Work Schedule Management with Various Robotics Process Automation (RPA) Tools. In Recent Advances in Materials Technologies; Springer Nature: Singapore, 2022; pp. 337–345.
  23. Chi-Shiuan Lin, P.; Jun-Min Wei, M. Integration of process planning and scheduling for distributed flexible job shops. Comput. Oper. Res. 2020, 124, 1–16.
  24. Chaudhry, I.A.; Khan, A.A. A research survey: Review of flexible job shop scheduling techniques. Int. Trans. Oper. Res. 2016, 23, 551–591.
  25. Zhou, L.; Liang, Z.; Chou, C.; Chaovalitwongse, W. Airline planning and scheduling: Models and solution methodologies. Front. Eng. Manag. 2020, 7, 1–26.
  26. Hooker, J. Planning and Scheduling by Logic-Based Benders Decomposition. Oper. Res. 2007, 55, 588–602.
  27. Ibn Majdoub Hassani, Z.; El Barkany, A.; Moumen, D.; Jabri, A.; Ikram, E. Planning and scheduling problems of production systems: Review. classification and opportunities. Int. J. Product. Qual. Manag. 2019, 28, 372.
  28. Foumani, M.; Razeghi, A.; Smith-Miles, K. Stochastic optimization of two-machine flow shop robotic cells with controllable inspection times: From theory toward practice. Robot. Comput. Integr. Manuf. 2020, 61, 101822.
  29. Srivivas, N.; Deb, K. Multiobjective optimization using non dominated sorting in genetic algorithms. Evol. Comput. 1995, 2, 221–248.
  30. Miettinen, K. Nonlinear multiobjective optimization. In International Series in Operations Research & Management Science, 1st ed.; Springer Science & Business Media: Berlin/Heidelberg, Germany, 1998.
  31. Zitzler, E. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph.D. Thesis, Federal Institute of Technology Zurich, Zurich, Switzerland, 1999.
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Subjects: Engineering, Manufacturing
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Leonel Patrício
,
Lino Costa
,
Leonilde Varela
,
Paulo Ávila
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Update Date: 28 Nov 2023
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