Batch scheduling with quality-based changeovers

Brunaud, Braulio; Perez, Hector D.; Amaran, Satyajith; Bury, Scott J.; Wassick, John M.; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2019.106617

Cited by 21 publications

(7 citation statements)

References 16 publications

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“…When using the UOPSS formulation from Kelly [79], Brunaud et al [80] demonstrated that large-scale discrete optimization problems for flowsheet network is solved within a minute. Compared to the traditional state-task network (STN) [81], [82] and resource-task network (RTN) [83] formulations for flowsheet network solutions, the instances are reduced around four-to ten-fold the optimization time in the UOPSS formulation.…”

Section: B Optimizing (Fast Decision-making Of Operational Schedules)mentioning

confidence: 99%

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

et al. 2022

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Smart manufacturing in the so-called Industry 4.0 age pushes the research and development of laboratory-scale proof of concepts before its deployment in pilots and real-size equipment. As such, we present a cyber-physical system (CPS) demonstration in the mining industry field engineered to autonomously manage the handling of solids flowing in a conveyor-belt that drops materials in containers, forming multiple stockpiles per belt. The CPS operates to control multiple stockpiles' inventories using mixed-integer optimization that minimizes the square deviation of the measured inventory to their targets (heights). Within the sensing-optimizing-actuating (SOA) cycle, the CPS demonstration is performed as follows. First, the sensing (data measurement, data processing, and system evaluation) uses a deep neural network in real-time to assess the level of materials stored in transparent containers. Second, the optimizing (mathematical programming, optimization techniques, and decision-making capabilities) is performed using a flowsheet network formulation called unit-operation-port-state superstructure (UOPSS) that permits a fast solution for the position-idle-time-varying discrete manipulated variables as operational schedules. Third, the actuating (cyber-physical integration) implements a physical actuation solution through an integrated CPS environment. According to the findings of our experimentation, stockpiling process control in a smart manufacturing context has enormous potentials to control multiple stockpiles' inventory autonomously.

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Section: B Optimizing (Fast Decision-making Of Operational Schedules)mentioning

confidence: 99%

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

et al. 2022

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“…30 The UOPSS formulation was chosen because of its flexibility and scalability. 31 The model is based on units that can perform different tasks, connected with tanks or other units through ports (Figure 7). Each task can have a different number of ports.…”

Section: Optimization Modelsmentioning

confidence: 99%

“…The scheduling model is a discrete time Unit-Operation-Port-State-Superstructure (UOPSS) formulation . The UOPSS formulation was chosen because of its flexibility and scalability . The model is based on units that can perform different tasks, connected with tanks or other units through ports (Figure ).…”

Section: Optimization Modelsmentioning

confidence: 99%

Novel Approaches for the Integration of Planning and Scheduling

Brunaud

Amaran

Bury

et al. 2019

Ind. Eng. Chem. Res.

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The problem of integrating planning and scheduling models is addressed. Many of the previous models proposed in the literature assume that both models need to be solved over the same time horizon, leading to intractable models. Integrated models with shorter scheduling horizons are considered. To maintain the trade-off balance between the decision levels, the objective functions are appropriately scaled. The second modeling aspect explored is the communication between planners and schedulers. Communication through inventory policies is explored as an alternative to the traditional communication through production and inventory targets. The resulting models are evaluated in a tailored simulation framework. Models with a shorter scheduling horizon, which cover the decision horizon of the planning, obtain similar profits as the full space models. The communication through inventory policies leads to lower inventories, while maintaining similar profit levels. The proposed models and the modular modeling methodology used are contributions to bring decision support systems closer to practice.

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“…However, with the recent advances in decision-making modeling and optimization, solving algorithms, and computer-aided resources (FRANZOI et al, 2018;BRUNAUD et al, 2020), the automation of an efficient scheduling decision-making for crude oil refinery applications has become possible and essential for the competitiveness of the refineries (KELLY and MANN, 2003).…”

Section: Crude Oil Refinery Schedulingmentioning

confidence: 99%

“…Although real-world process industry operations are highly dynamic and fluid environments in which data is uncertain and changes over time, online updates of information in process operations are usually delayed (in terms of timeliness) or incomplete (in their structural and temporal integration) since they are complicated to be synchronized and to be implemented algorithmically in a systematic way (LARSEN and PRANZO, 2018). However, recent advances in network (flowsheet) optimization (BRUNAUD et al, 2020) as well as in solving algorithms and computer-aided resources (FRANZOI et al, 2018) allow more complex decision-making solutions in scheduling propositions. Moreover, the expansion of information and communication technologies can provide an online data measurement of complete process networks for the massive volume and variety of information from the plant (MENEZES, KELLY, and LEAL, 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrated scheduling optimization in the crude oil refinery industry: from crude oil unloading to fuel deliveries.

Júnior¹,

Eduard²

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Batch scheduling with quality-based changeovers

Cited by 21 publications

References 16 publications

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

Novel Approaches for the Integration of Planning and Scheduling

Integrated scheduling optimization in the crude oil refinery industry: from crude oil unloading to fuel deliveries.

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