16 research outputs found
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming
Constraint Programming (CP) is a declarative programming paradigm that allows
for modeling and solving combinatorial optimization problems, such as the
Job-Shop Scheduling Problem (JSSP). While CP solvers manage to find optimal or
near-optimal solutions for small instances, they do not scale well to large
ones, i.e., they require long computation times or yield low-quality solutions.
Therefore, real-world scheduling applications often resort to fast,
handcrafted, priority-based dispatching heuristics to find a good initial
solution and then refine it using optimization methods.
This paper proposes a novel end-to-end approach to solving scheduling
problems by means of CP and Reinforcement Learning (RL). In contrast to
previous RL methods, tailored for a given problem by including procedural
simulation algorithms, complex feature engineering, or handcrafted reward
functions, our neural-network architecture and training algorithm merely
require a generic CP encoding of some scheduling problem along with a set of
small instances. Our approach leverages existing CP solvers to train an agent
learning a Priority Dispatching Rule (PDR) that generalizes well to large
instances, even from separate datasets. We evaluate our method on seven JSSP
datasets from the literature, showing its ability to find higher-quality
solutions for very large instances than obtained by static PDRs and by a CP
solver within the same time limit.Comment: To be published at ICAPS 202
Combining Spreadsheet Smells for Improved Fault Prediction
Spreadsheets are commonly used in organizations as a programming tool for
business-related calculations and decision making. Since faults in spreadsheets
can have severe business impacts, a number of approaches from general software
engineering have been applied to spreadsheets in recent years, among them the
concept of code smells. Smells can in particular be used for the task of fault
prediction. An analysis of existing spreadsheet smells, however, revealed that
the predictive power of individual smells can be limited. In this work we
therefore propose a machine learning based approach which combines the
predictions of individual smells by using an AdaBoost ensemble classifier.
Experiments on two public datasets containing real-world spreadsheet faults
show significant improvements in terms of fault prediction accuracy.Comment: 4 pages, 1 figure, to be published in 40th International Conference
on Software Engineering: New Ideas and Emerging Results Trac
Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming
Domain-specific heuristics are an essential technique for solving
combinatorial problems efficiently. Current approaches to integrate
domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory
when dealing with heuristics that are specified non-monotonically on the basis
of partial assignments. Such heuristics frequently occur in practice, for
example, when picking an item that has not yet been placed in bin packing.
Therefore, we present novel syntax and semantics for declarative specifications
of domain-specific heuristics in ASP. Our approach supports heuristic
statements that depend on the partial assignment maintained during solving,
which has not been possible before. We provide an implementation in ALPHA that
makes ALPHA the first lazy-grounding ASP system to support declaratively
specified domain-specific heuristics. Two practical example domains are used to
demonstrate the benefits of our proposal. Additionally, we use our approach to
implement informed} search with A*, which is tackled within ASP for the first
time. A* is applied to two further search problems. The experiments confirm
that combining lazy-grounding ASP solving and our novel heuristics can be vital
for solving industrial-size problems
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
Semiconductor manufacturing is a notoriously complex and costly multi-step
process involving a long sequence of operations on expensive and
quantity-limited equipment. Recent chip shortages and their impacts have
highlighted the importance of semiconductors in the global supply chains and
how reliant on those our daily lives are. Due to the investment cost,
environmental impact, and time scale needed to build new factories, it is
difficult to ramp up production when demand spikes.
This work introduces a method to successfully learn to schedule a
semiconductor manufacturing facility more efficiently using deep reinforcement
and self-supervised learning. We propose the first adaptive scheduling approach
to handle complex, continuous, stochastic, dynamic, modern semiconductor
manufacturing models. Our method outperforms the traditional hierarchical
dispatching strategies typically used in semiconductor manufacturing plants,
substantially reducing each order's tardiness and time until completion. As a
result, our method yields a better allocation of resources in the semiconductor
manufacturing process
Relevance-Based Compression of Cataract Surgery Videos
In the last decade, the need for storing videos from cataract surgery has
increased significantly. Hospitals continue to improve their imaging and
recording devices (e.g., microscopes and cameras used in microscopic surgery,
such as ophthalmology) to enhance their post-surgical processing efficiency.
The video recordings enable a lot of user-cases after the actual surgery, for
example, teaching, documentation, and forensics. However, videos recorded from
operations are typically stored in the internal archive without any
domain-specific compression, leading to a massive storage space consumption. In
this work, we propose a relevance-based compression scheme for videos from
cataract surgery, which is based on content specifics of particular cataract
surgery phases. We evaluate our compression scheme with three state-of-the-art
video codecs, namely H.264/AVC, H.265/HEVC, and AV1, and ask medical experts to
evaluate the visual quality of encoded videos. Our results show significant
savings, in particular up to 95.94% when using H.264/AVC, up to 98.71% when
using H.265/HEVC, and up to 98.82% when using AV1.Comment: 11 pages, 5 figures, 3 table