15,903 research outputs found
A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks
A climate of increasing deregulation in traditional highway transportation,
where the private sector has an expanded role in the provision of traditional
transportation services, provides a background for practical policy issues to be investigated.
One of the key issues of interest, and the focus of this chapter, would
be the equilibrium decision variables offered by participants in this market. By assuming
that the private sector participants play a Nash game, the above problem can
be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from
the classical Cournot-Nash game because each and every player’s actions is constrained
by another variational inequality describing the equilibrium route choice of
users on the network. In this chapter, we discuss this BLVI and suggest a heuristic
coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm
is subsequently tested on example problems drawn from the literature. The numerical
experiments suggest that the proposed algorithm is a viable solution method for
this problem
Dependency structure matrix, genetic algorithms, and effective recombination
In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions-modularity, hierarchy, and overlap, facet-wise models arc developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.This work was sponsored by Taiwan National Science Council under grant NSC97-
2218-E-002-020-MY3, U.S. Air Force Office of Scientific Research, Air Force Material
Command, USAF, under grants FA9550-06-1-0370 and FA9550-06-1-0096, U.S. National
Science Foundation under CAREER grant ECS-0547013, ITR grant DMR-03-25939 at
Materials Computation Center, grant ISS-02-09199 at US National Center for Supercomputing Applications, UIUC, and the Portuguese Foundation for Science and Technology
under grants SFRH/BD/16980/2004 and PTDC/EIA/67776/2006
A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines
This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method
Supporting Knitwear Design Using Case-Based Reasoning
Organised by: Cranfield UniversityKnitwear design is a creative activity that is hard to automate using the computer. The production of the
associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation.
Our objectives are two-fold: to facilitate the design and to ease the burden of calculations and checks in
pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: we
describe appropriate methods and show how they can be implemented.Mori Seiki – The Machine Tool Compan
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