In this work, we propose a simultaneous scheduling and control formulation by explicitly incorporating into the scheduling model process dynamics in the form of differential/algebraic constraints. The formulation takes into account the interactions between scheduling and control and is able to handle nonlinearities embedded into the processing system. The simultaneous scheduling and control problems is cast as a mixed-integer dynamic optimization (MIDO) problem where the simultaneous approach, based on orthogonal collocation on finite elements, is used to transform it into a mixed-integer nonlinear programming (MINLP) problem. The proposed simultaneous scheduling and control formulation is tested using three multiproduct continuous stirred tank reactors featuring difficult nonlinearities.

/pdf/simultaneous-cyclic-scheduling-and-control-of-a-multiproduct-2lnyddyn2t.pdf

Simultaneous cyclic scheduling and control of a multiproduct CSTR

Production optimization for continuously operated processes with optimal operation and scheduling of multiple units

Optimizing scheduling of refinery operations based on piecewise linear models

Oil refineries are one of the most important examples of multiproduct continuous plants, that is, a continuous processing system that generates a number of products simultaneously. A refinery processes various crude oil types and produces a wide range of products. It is a complex optimization problem, mainly due to the number of different tasks involved and different objective criteria. In addition, some of the tasks have precedence constraints that require other tasks to be scheduled first. In this paper the refinery scheduling problem is addressed using genetic algorithms and cooperative coevolution. A simple refinery, with commonly found types of equipments, tasks and constraints of a real refinery, was created. Three test scenarios were designed with different sizes, demands and constraints. In all of them, the results obtained were far better than the ones obtained through random search

/pdf/refinery-scheduling-optimization-using-genetic-algorithms-2uz7cm7iqj.pdf

Refinery Scheduling Optimization using Genetic Algorithms and Cooperative Coevolution

A Decision Tree based Decomposition Method for Oil Refinery Scheduling

In this work, models are proposed that rely on mixed-integer nonlinear programming and on the discrete representation of the time domain along the scheduling horizon. The proposed models contain raw-material supply conditions, demand for products, initial inventories and qualities, and capacity constraints for processing, storage and transfer. From these data, an optimization solver that relies on an implementation of the outer approximation method obtains a solution that supports the production scheduler. The models were developed and solved at a growing level of complexity. Real-world problems with a time horizon of two days were solved that resulted in models with over 1000 binary variables and 10000 constraints.

Mixed integer nonlinear programming techniques for the short term scheduling of oil refineries

Among healthcare workers, oral and maxillofacial surgeons are some of the most exposed to coronavirus disease (COVID-19). The aim of this retrospective study was to develop suggestions for continuing the work of oral and maxillofacial surgeons using a safe protocol for elective and urgent aerosol-generating procedures that could prevent the onset of new clusters. Based on the results obtained and a guidelines review of those Asian countries that had promptly managed the current pandemic, the following safety protocol was developed. 

https://air.unimi.it/bitstream/2434/933718/2/1-s2.0-S1010518222000129-main.pdf

Recommendations for a safe restart of elective aerosol-generating oral surgery procedures following the COVID-19 pandemic outbreak: An Italian multicenter study

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Mixed integer nonlinear programming techniques for the short term scheduling of oil refineries

Recommendations for a safe restart of elective aerosol-generating oral surgery procedures following the COVID-19 pandemic outbreak: An Italian multicenter study