16,845 research outputs found
On electrical load tracking scheduling for a steel plant
Nolde and Morari (2010) study a steel manufacturing scheduling problem where the tasks must be scheduled such
that electricity consumption matches to a pre-specified periodic energy chart. They propose a continuous time integer
linear programming formulation to solve the problem. In this note, we present an alternative continuous time
formulation, focused on the relative positions of tasks and time periods, that improves significantly the computation
time
A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning
Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be necessary. We present a combination of approximate reasoning
based constraints and iterative optimization based heuristics that help to
model and solve such problems in a framework of C++ software libraries called
StarFLIP++. While initially developed to schedule continuous caster units in
steel plants, we present in this paper results from reusing the library
components in a shift scheduling system for the workforce of an industrial
production plant.Comment: 33 pages, 9 figures; for a project overview see
http://www.dbai.tuwien.ac.at/proj/StarFLIP
On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model
This paper, inspired by a real production process of steel hardening,
investigates a scheduling problem to minimize the idle energy consumption of
machines. The energy minimization is achieved by switching a machine to some
power-saving mode when it is idle. For the steel hardening process, the mode of
the machine (i.e., furnace) can be associated with its inner temperature.
Contrary to the recent methods, which consider only a small number of machine
modes, the temperature in the furnace can be changed continuously, and so an
infinite number of the power-saving modes must be considered to achieve the
highest possible savings. To model the machine modes efficiently, we use the
concept of the energy function, which was originally introduced in the domain
of embedded systems but has yet to take roots in the domain of production
research. The energy function is illustrated with several application examples
from the literature. Afterward, it is integrated into a mathematical model of a
scheduling problem with parallel identical machines and jobs characterized by
release times, deadlines, and processing times. Numerical experiments show that
the proposed model outperforms a reference model adapted from the literature.Comment: Accepted to 9th International Conference on Operations Research and
Enterprise Systems (ICORES 2020
Tasks, cognitive agents, and KB-DSS in workflow and process management
The purpose of this paper is to propose a nonparametric interest rate term structure model and investigate its implications on term structure dynamics and prices of interest rate derivative securities. The nonparametric spot interest rate process is estimated from the observed short-term interest rates following a robust estimation procedure and the market price of interest rate risk is estimated as implied from the historical term structure data. That is, instead of imposing a priori restrictions on the model, data are allowed to speak for themselves, and at the same time the model retains a parsimonious structure and the computational tractability. The model is implemented using historical Canadian interest rate term structure data. The parametric models with closed form solutions for bond and bond option prices, namely the Vasicek (1977) and CIR (1985) models, are also estimated for comparison purpose. The empirical results not only provide strong evidence that the traditional spot interest rate models and market prices of interest rate risk are severely misspecified but also suggest that different model specifications have significant impact on term structure dynamics and prices of interest rate derivative securities.
E-POLCA to control multi-product, multi-machine job shops.
Control; Job; University; Research;
A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids
This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information
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