https://orcid.org/0000-0001-6702-5876
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Abstract
In this work, a novel optimisation-simulation based on the Recursive Optimisation-Simulation Approach (ROSA) methodology is developed to provide effective decision-support for integrated production planning and scheduling. The proposed iterative approach optimises production plans while satisfying complex scheduling constraints, such as robots' allocation in collaborative tasks. The plans are determined through a two-level MILP model and are iteratively evaluated by a detailed discrete-event simulation model to guarantee capacity-feasible solutions at the scheduling level. Through an industrial case study of a multistage assembly line design collaboratively operated by humans and mobile shared robots, near-optimal solutions comprise lot-sizing decisions, the release schedule of production orders, the allocation of tasks to humans or robots, and the number of robots per period. Moreover, by addressing a set of propositions to assess the methodology, the results highlight the advantages of the hybrid approach to converge into optimised operational decisions and analyse the process dynamics.
Acknowledgements
The authors would like to acknowledge the financial support by UE/FEDER funds through program COMPETE and FCT-Fundação para a Ciência e a Tecnologia under the projects DM4Manufacturing (POCI-01-0145-FEDER-016418), COBOTIS (PTDC/EME-EME/32595/2017) and UIDB/00285/2020.
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No potential conflict of interest was reported by the author(s).
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Notes on contributors
Miguel Vieira
Miguel Vieira obtained his Ph.D. in Leaders for Technical Industries awarded by Instituto Superior Técnico in partnership with the Massachusetts Institute of Technology under the MIT Portugal Programme, with the focus on the development of decision-support tools for the production planning optimisation of industrial plants. Since 2017, Miguel has been a postdoctoral Research Fellow of the Centre for Management Studies at IST and since 2019 an Associate Researcher of CEMMPRE at University of Coimbra. His research interests have been applied to the industrial engineering challenges of optimisation methods, simulation models and machine learning algorithms to solve supply chain, design, planning and scheduling problems of complex production decision systems, alongside with the leading collaboration in numerous industrial-based project.
Samuel Moniz
Samuel Moniz is an Assistant Professor in Industrial Engineering and Management at the Department of Mechanical Engineering of the University of Coimbra. Samuel finished his Ph.D. in 2014 in production planning and scheduling optimisation, focused on the implementation of data-driven methods for improving the economic performance of chemical-pharmaceutical plants. He has been involved in solving real-world problems through the application of advanced modelling approaches and data science techniques, leading a cross-functional team focused on the design of complex manufacturing and logistics systems, and being a project manager of several contract-based research programmes with industrial companies.
Bruno S. Gonçalves
Bruno S. Gonçalves is an Invited Professor in the Department of Business Sciences at the School of Management and Technology of the Polytechnic of Porto. He is a collaborator researcher of the ALGORITMI Centre at the University of Minho, which awarded his Ph.D. in Leaders for Technical Industries in 2018 under the MIT Portugal Programme. His areas of interest are the integration of advanced techniques and models for operations management in environments with uncertainty and large sets of data. He has been involved in several research projects in the area of production industrial planning, control and simulation, focused in the development of methods to improve decision-making in the context of real-world operations.
Tânia Pinto-Varela
Tânia Pinto-Varela is an Assistant Professor at the Engineering and Management Department in Instituto Superior Técnico, Universidade de Lisboa, and Researcher of the Centre for Management Studies of IST. She holds a M.Sc. in Operations Research and Process System and a PhD in Industrial Engineering and Management from IST, being currently the co-coordinator of the B.Sc. and M.Sc. in Industrial Engineering and Management at IST. Her research interests are in process systems engineering, with several projects and publications over the subjects of design, planning, scheduling, flexible manufacturing, energy, applying methodologies from manufacturing science and operational research process within diverse industrial fields.
Ana Paula Barbosa-Póvoa
Ana Paula Barbosa-Póvoa is a Full Professor in Operations and Logistics, currently head of the Engineering and Management Department of Instituto Superior Técnico, Universidade de Lisboa. She holds a Ph.D. in Engineering from the Imperial College of Science Technology and Medicine and her research focus is on developing a comprehensive understanding of complex problems in supply chains and operations management, supported by novel and sound engineering systems models and techniques. Sustainability, resilience, design and planning of supply chains are among her main addressed authorial domains, being involved in numerous scientific boards and international projects, and coordinating the Operations Management and Logistics (OpLog) research group.
Pedro Neto
Pedro Neto obtained his Ph.D. in Mechanical Engineering (Robotics) from the University of Coimbra in 2012. He is currently Professor Auxiliar in the Department of Mechanical Engineering of the University of Coimbra. His research interests include collaborative robotics, human–robot interaction, pattern recognition and robot autonomy, which led to the creation of the Collaborative Robotics Laboratory (CoRLuc) in 2016, heading a team of several M.Sc. and Ph.D. researchers. Pedro Neto is an active researcher in technology transfer projects, serving in the scientific committees of several conferences and member of the IEEE Factory Automation technical committee.