92 research outputs found
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice
Behaviour of Self-Compacting Concrete Columns Reinforced Longitudinally with Steel Tubes
Performance of concrete columns has been significantly improved by using composite material systems such as encased sections and concrete filled steel tubes. Different combinations of encased sections and steel sections have been widely studied. Steel sections and concrete have been used to construct composite columns with different cross-sections. The composite columns are usually constructed of normal vibrated concrete. Recently, self-compacting concrete (SCC) is also used in the construction of the composite columns. The synergies between steel and SCC in composite columns provide better performance in terms of high strength, stiffness, ductility, as well as fire and seismic resistance. This study proposes two innovative concepts: a new method to determine the stress-strain behaviour of SCC under direct uniaxial tension and a new method of reinforcing SCC columns by using longitudinal small-diameter steel tubes instead of reinforcing steel bars.
For the stress-strain behaviour of SCC under direct uniaxial tension, special steel claws were designed, built and installed at both ends of 100 × 100 × 500 mm SCC specimens. These claws were used to transfer the applied tensile forces to the specimens. The crosssection of the specimens was reduced in the middle to ensure that failure would occur in the middle of the specimen. The test results showed that there was no slippage or fracture at the ends of any of the tested specimens. Also, the failure occurred in the middle of specimens, as expected. The direct tensile testing method developed in this study was also used for different types of concrete including normal strength concrete (NSC), high-strength concrete (HSC) and steel fibre reinforced high-strength concrete (SFHSC). The developed method provided rational and reliable results for the direct tensile strength of the SCC, NSC, HSC and SFHSC using a simple and effective testing technique
Multi-objective path planning using spline representation
Off-line point to point navigation to calculate feasible paths and optimize them for different objectives is computationally difficult. Path planning problem is truly a multi-objective problem, as reaching the goal point in short time is desirable for an autonomous vehicle while ability to generate safe paths in crucial for vehicle viability. Path representation methodologies using piecewise polynomial and B-splines have been used to ensure smooth paths. Multi-objective path planning studies using NSGA-II algorithm to optimize path length and safety measures computed using one of the three metrics (i) an artificial potential field, (ii) extent of obstacle hindrance and (iii) a measure of visibility are implemented. Multiple tradeoff solutions are obtained on complex scenarios. The results indicate the usefulness of treating path planning as a multiobjective problem
ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints
Ship design is a years-long process that requires balancing complex design
trade-offs to create a ship that is efficient and effective. Finding new ways
to improve the ship design process can lead to significant cost savings for
ship building and operation. One promising technology is generative artificial
intelligence, which has been shown to reduce design cycle time and create
novel, high-performing designs. In literature review, generative artificial
intelligence has been shown to generate ship hulls; however, ship design is
particularly difficult as the hull of a ship requires the consideration of many
objectives. This paper presents a study on the generation of parametric ship
hull designs using a parametric diffusion model that considers multiple
objectives and constraints for the hulls. This denoising diffusion
probabilistic model (DDPM) generates the tabular parametric design vectors of a
ship hull for evaluation. In addition to a tabular DDPM, this paper details
adding guidance to improve the quality of generated ship hull designs. By
leveraging classifier guidance, the DDPM produced feasible parametric ship
hulls that maintain the coverage of the initial training dataset of ship hulls
with a 99.5% rate, a 149x improvement over random sampling of the design vector
parameters across the design space. Parametric ship hulls produced with
performance guidance saw an average of 91.4% reduction in wave drag
coefficients and an average of a 47.9x relative increase in the total displaced
volume of the hulls compared to the mean performance of the hulls in the
training dataset. The use of a DDPM to generate parametric ship hulls can
reduce design time by generating high-performing hull designs for future
analysis. These generated hulls have low drag and high volume, which can reduce
the cost of operating a ship and increase its potential to generate revenue
Cricket team selection using evolutionary multi-objective optimization
Selection of players for a high performance cricket team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization problem. In cricket team formation, batting strength and bowling strength of a team are the major factors affecting its performance and an optimum trade-off needs to be reached in formation of a good team. We propose a multi-objective approach using NSGA-II algorithm to optimize overall batting and bowling strength of a team and find team members in it. Using the information from trade-off front, a decision making approach is also proposed for final selection of team. Case study using a set of players auctioned in Indian Premier League, 4th edition has been taken and player's current T-20 statistical data is used as performance parameter. This technique can be used by franchise owners and league managers to form a good team within budget constraints given by the organizers. The methodology is generic and can be easily extended to other sports like soccer, baseball etc
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
In the rapidly advancing field of multi-modal machine learning (MMML), the
convergence of multiple data modalities has the potential to reshape various
applications. This paper presents a comprehensive overview of the current
state, advancements, and challenges of MMML within the sphere of engineering
design. The review begins with a deep dive into five fundamental concepts of
MMML:multi-modal information representation, fusion, alignment, translation,
and co-learning. Following this, we explore the cutting-edge applications of
MMML, placing a particular emphasis on tasks pertinent to engineering design,
such as cross-modal synthesis, multi-modal prediction, and cross-modal
information retrieval. Through this comprehensive overview, we highlight the
inherent challenges in adopting MMML in engineering design, and proffer
potential directions for future research. To spur on the continued evolution of
MMML in engineering design, we advocate for concentrated efforts to construct
extensive multi-modal design datasets, develop effective data-driven MMML
techniques tailored to design applications, and enhance the scalability and
interpretability of MMML models. MMML models, as the next generation of
intelligent design tools, hold a promising future to impact how products are
designed
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