56 research outputs found
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
Improving RRT for Automated Parking in Real-world Scenarios
Automated parking is a self-driving feature that has been in cars for several
years. Parking assistants in currently sold cars fail to park in more complex
real-world scenarios and require the driver to move the car to an expected
starting position before the assistant is activated. We overcome these
limitations by proposing a planning algorithm consisting of two stages: (1) a
geometric planner for maneuvering inside the parking slot and (2) a
Rapidly-exploring Random Trees (RRT)-based planner that finds a collision-free
path from the initial position to the slot entry. Evaluation of computational
experiments demonstrates that improvements over commonly used RRT extensions
reduce the parking path cost by 21 % and reduce the computation time by 79.5 %.
The suitability of the algorithm for real-world parking scenarios was verified
in physical experiments with Porsche Cayenne.Comment: 19 pages, 14 figures, 2 table
Data-driven Algorithm for Scheduling with Total Tardiness
In this paper, we investigate the use of deep learning for solving a
classical NP-Hard single machine scheduling problem where the criterion is to
minimize the total tardiness. Instead of designing an end-to-end machine
learning model, we utilize well known decomposition of the problem and we
enhance it with a data-driven approach. We have designed a regressor containing
a deep neural network that learns and predicts the criterion of a given set of
jobs. The network acts as a polynomial-time estimator of the criterion that is
used in a single-pass scheduling algorithm based on Lawler's decomposition
theorem. Essentially, the regressor guides the algorithm to select the best
position for each job. The experimental results show that our data-driven
approach can efficiently generalize information from the training phase to
significantly larger instances (up to 350 jobs) where it achieves an optimality
gap of about 0.5%, which is four times less than the gap of the
state-of-the-art NBR heuristic
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