13 research outputs found
Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models
We adopt a supervised learning approach to predict runtimes
of
batch production scheduling mixed-integer programming (MIP) models
with the aim of understanding what instance features make a model
computationally expensive. We introduce novel features to characterize
instance difficulty according to problem type. The developed machine
learning models trained on runtime data obtained from a wide variety
of instances show good predictive performances. Then, we discuss informative
features and their effects on computational performance. Finally,
based on the derived insights, we propose solution methods for improving
the computational performance of batch scheduling MIP models
Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models
We adopt a supervised learning approach to predict runtimes
of
batch production scheduling mixed-integer programming (MIP) models
with the aim of understanding what instance features make a model
computationally expensive. We introduce novel features to characterize
instance difficulty according to problem type. The developed machine
learning models trained on runtime data obtained from a wide variety
of instances show good predictive performances. Then, we discuss informative
features and their effects on computational performance. Finally,
based on the derived insights, we propose solution methods for improving
the computational performance of batch scheduling MIP models
Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models
We adopt a supervised learning approach to predict runtimes
of
batch production scheduling mixed-integer programming (MIP) models
with the aim of understanding what instance features make a model
computationally expensive. We introduce novel features to characterize
instance difficulty according to problem type. The developed machine
learning models trained on runtime data obtained from a wide variety
of instances show good predictive performances. Then, we discuss informative
features and their effects on computational performance. Finally,
based on the derived insights, we propose solution methods for improving
the computational performance of batch scheduling MIP models
Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models
We adopt a supervised learning approach to predict runtimes
of
batch production scheduling mixed-integer programming (MIP) models
with the aim of understanding what instance features make a model
computationally expensive. We introduce novel features to characterize
instance difficulty according to problem type. The developed machine
learning models trained on runtime data obtained from a wide variety
of instances show good predictive performances. Then, we discuss informative
features and their effects on computational performance. Finally,
based on the derived insights, we propose solution methods for improving
the computational performance of batch scheduling MIP models
Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models
We adopt a supervised learning approach to predict runtimes
of
batch production scheduling mixed-integer programming (MIP) models
with the aim of understanding what instance features make a model
computationally expensive. We introduce novel features to characterize
instance difficulty according to problem type. The developed machine
learning models trained on runtime data obtained from a wide variety
of instances show good predictive performances. Then, we discuss informative
features and their effects on computational performance. Finally,
based on the derived insights, we propose solution methods for improving
the computational performance of batch scheduling MIP models
Mixed-Integer Programming Model and Tightening Methods for Scheduling in General Chemical Production Environments
We develop a mixed-integer programming (MIP) model to
address chemical
production scheduling problems in a wide range of facilities, including
facilities with many different types of material handling restrictions
and a wide range of process characteristics. We first discuss how
material handling restrictions result in different types of production
environments and then show how these restrictions can be modeled.
We also present extensions for some important processing constraints
and briefly discuss how other constraints and characteristics can
be modeled. Finally, we present constraint propagation methods for
the calculation of parameters that are used to formulate tightening
constraints that lead to a substantial reduction of computational
requirements. The proposed model is the first to address the generalized
chemical production scheduling problem
Reformulations and Branching Methods for Mixed-Integer Programming Chemical Production Scheduling Models
Mixed-integer
programs for chemical production scheduling are computationally challenging.
One characteristic that makes them hard is that they typically have
many symmetric solutions, that is, solutions that are different in
terms of the values of the decision variables but have the same objective
function value, which means that the algorithms used to solve these
models must search through all such solutions before improving the
bound on the objective. To address this challenge, we propose three
reformulations of the widely used state–task network formulation.
Specifically, we introduce additional constraints to define the number
of batches of each task as an integer variable. Branching on this
new integer variable quickly eliminates schedules that have the same
number of batches, which, in turn, leads to the elimination of many
symmetric solutions. We also study different branching strategies
and variable selection rules and compare them. The proposed solution
methods lead to orders-of-magnitude reductions in the computational
requirements for the solution of scheduling problems
An Optimization-Based Approach for Simultaneous Chemical Process and Heat Exchanger Network Synthesis
We propose a mixed-integer nonlinear
programming (MINLP) model
for simultaneous chemical process and heat exchanger network synthesis.
The model allows process stream inlet/outlet temperatures and flow
rates to vary and can be extended to handle unclassified streams,
thereby facilitating integration with a process synthesis model. The
proposed model is based on a generalized transshipment approach in
which the heat cascade is built upon a “dynamic” temperature
grid. Both hot and cold streams can cascade heat so that exchanger
inlet and outlet temperature, heat duty, and area can be calculated
at each temperature interval. We develop mixed-integer constraints
to model the number of heat exchangers in the network. Finally, we
present several solution strategies tailored to improve the computation
performance of the proposed models
Reformulations of Mixed-Integer Programming Continuous-Time Models for Chemical Production Scheduling
Although several optimization models
have been proposed for chemical
production scheduling, there is still a need for effective solution
methods. Accordingly, the goal of this work is to present different
reformulations of representative continuous-time models by introducing
an explicit variable for the number of batches of a given task. This
idea, which has been successfully applied to discrete-time models,
results in significant computational enhancement. We discuss how different
objective functions benefit from particular reformulations and show
significant improvements by means of an extensive computational study
that includes several instances containing different process networks
and scheduling horizons
Design of Cellulosic Ethanol Supply Chains with Regional Depots
The
conversion of lignocellulosic biomass to fuels has the potential
to reduce our dependence on fossil fuels. To ensure biomass supply
meets biofuel demand, it is necessary to have an effective biomass
supply network. Toward this end, the concept of regional biomass processing
depot, where biomass is pretreated and/or densified to a higher density
intermediate, has been introduced to improve the performance of supply
network in terms of costs and emissions. In this article, we develop
a mixed-integer nonlinear programming model for the capacity and inventory
planning problem of biofuels supply chain including depots. Importantly,
the proposed model accounts for variable locations of depots, which
is a subject that has not been studied in the literature. In addition,
our models account for biomass selection and allocation, technology
selection and capacity planning at depots and biorefineries, and biomass
seasonality