Production scheduling of parallel machines with model predictive control

Cataldo, Andréa; Perizzato, Andrea; Scattolini, Riccardo

doi:10.1016/j.conengprac.2015.05.007

Cited by 28 publications

(14 citation statements)

References 30 publications

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“…The results obtained addressing the Production scheduling and Pallet routing problems (Sect. 2.3), extensively described in [27,29], show that the developed algorithms, based on the MLD representation of the systems considered in the two cases, are highly flexible, so that they can be easily adapted to different problems. Moreover, it is easy to obtain different behaviours of the controlled manufacturing system by properly tuning easy-to-understand parameters of the algorithm, such as the cost function and the constraints defining the on-line optimization problem to be solved in MPC.…”

Section: Methodsmentioning

confidence: 99%

“…Experimental results show that the algorithm can be highly adapted to obtain different behaviours, by means of simple and easy-to-understand parameters of the cost function. Moreover, such algorithm allows to dynamically change the minimum production and the maximum energy available and to choose, if present, which possible constraint should be violated if necessary [29]. All these features can be hardly achieved with standard controllers or with simple scheduling.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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Model Predictive Control Tools for Evolutionary Plants

Cataldo

Bertolotti

Scattolini

2019

Factories of the Future

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The analysis and design of control system configurations for automated production systems is generally a challenging problem, in particular given the increasing number of automation devices and the amount of information to be managed. This problem becomes even more complex when the production system is characterized by a fast evolutionary behaviour in terms of tasks to be executed, production volumes, changing priorities, and available resources. Thus, the control solution needs to be optimized on the basis of key performance indicators like flow production, service level, job tardiness, peak of the absorbed electrical power and the total energy consumed by the plant. This paper proposes a prototype control platform based on Model Predictive Control (MPC) that is able to impress to the production system the desired functional behaviour. The platform is structured according to a two-level control architecture. At the lower layer, distributed MPC algorithms control the pieces of equipment in the production system. At the higher layer an MPC coordinator manages the lower level controllers, by taking full advantage of the most recent advances in hybrid control theory, dynamic programming, mixed-integer optimization, and game theory. The MPC-based control platform will be presented and then applied to the case of a pilot production plant.

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Section: Methodsmentioning

confidence: 99%

Section: Conclusion and Future Researchmentioning

confidence: 99%

Model Predictive Control Tools for Evolutionary Plants

Cataldo

Bertolotti

Scattolini

2019

Factories of the Future

Self Cite

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“…Although MPC is nowadays widely used in the process industry due to its ability to cope with constraints and multiple objectives [8], [9], its application in manufacturing is still limited. Notable exceptions, mainly referred to supply chain optimization and control, are reported in [10]- [15], while applications of MPC to routing problems are described in [16] and [17]. However, the specificities of the problem here considered prevent the direct use of the algorithms described in these papers.…”

Section: Introductionmentioning

confidence: 93%

“…. , 15). The actual number of BZs available on each transport module depends on its specific mechanical configuration, which means that there are transport modules with only one BZ and others with two BZs or three BZs.…”

Section: Demanufacturing Plantmentioning

confidence: 99%

Dynamic Pallet Routing in a Manufacturing Transport Line With Model Predictive Control

Cataldo

Scattolini

2016

IEEE Trans. Contr. Syst. Technol.

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This brief describes the application of model predictive control to a manufacturing multipallet multitarget transport line. The mathematical representation of the plant is based on a mixed logical dynamical model. The performance index to be minimized weights the distance of the pallets from their final target, the pallet residence time on the line, and the control inputs. The resulting mixed integer linear programming problem is recursively solved to compute the control action according to the receding horizon approach. Both simulation and experimental results on the real system are reported and discussed to witness the performances of the control algorithm and its ability to manage even pallet route conflicts and target dynamic rescheduling.Index Terms-Manufacturing systems, mixed logical dynamical (MLD) systems, model predictive control (MPC), pallet transport line, propositional calculus.

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“…For example, currently, control systems oriented to prognosis and maintenance of manufacturing systems, based on the historical data, have been developed with the aim to predict and program the required changes or maintenance activities [107,108,109]. Additional to the prediction of maintenance tasks, strategies such as receding horizon control and advanced methods of process control (e.g., model predictive control (MPC)), have started to gain attention too, mainly, focusing on problems of energy efficiency and flexibility for planning and scheduling of processes at machine, line, and plant level [110,111,112,113].…”

Section: Control Strategies In Manufacturing Systemsmentioning

confidence: 99%

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

Diaz

Ocampo‐Martínez

2019

Journal of Manufacturing Systems

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Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.

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Production scheduling of parallel machines with model predictive control

Cited by 28 publications

References 30 publications

Model Predictive Control Tools for Evolutionary Plants

Model Predictive Control Tools for Evolutionary Plants

Dynamic Pallet Routing in a Manufacturing Transport Line With Model Predictive Control

Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies

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