Branch and bound based coordinate search filter algorithm for nonsmooth nonconvex mixed-integer nonlinear programming problems

Publicado em "Computational science and its applications – ICCSA 2014...", ISBN 978-3-319-09128-0. Series "Lecture notes in computer science", ISSN 0302-9743, vol. 8580.A mixed-integer nonlinear programming problem (MINLP) is a problem with continuous and integer variables and at least, one nonlinear function. This kind of problem appears in a wide range of real applications and is very difficult to solve. The difficulties are due to the nonlinearities of the functions in the problem and the integrality restrictions on some variables. When they are nonconvex then they are the most difficult to solve above all. We present a methodology to solve nonsmooth nonconvex MINLP problems based on a branch and bound paradigm and a stochastic strategy. To solve the relaxed subproblems at each node of the branch and bound tree search, an algorithm based on a multistart strategy with a coordinate search filter methodology is implemented. The produced numerical results show the robustness of the proposed methodology.This work has been supported by FCT (Fundação para a Ciência e aTecnologia) in the scope of the projects: PEst-OE/MAT/UI0013/2014 and PEst-OE/EEI/UI0319/2014

Optimale Linienfuehrung und Routenplanung in Verkehrssystemen (Schienenverkehr) Schlussbericht

Available from TIB Hannover: DtF QN1(57,40) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

A Decision Support System to Optimize Railway Stopping Patterns: Application to the Taiwan High Speed Rail

[[abstract]]An intercity passenger rail is built to connect several major cities. To provide satisfactory services to passengers, railway operators plan different stop schedules, such as all-stop, skip-stop, and express services. However, stopping patterns determined by empirical rules or political arguments are generally not optimal. This paper aims at developing a decision support system to generate the optimal combination of stopping patterns for minimizing total passenger in-vehicle time. This problem was first formulated by using mixed integer programming, but this method is intractable when dealing with large-scale problems because of the complexity of model structure and the nature of the problem. A genetic algorithm was then developed to search for the optimal or near-optimal solution efficiently within a reasonable computation time. The proposed algorithm was successfully implemented on Taiwan High Speed Rail. The resulting solution is better than the current practice, and the proposed algorithm is capable of finding the optimal solution in seconds. The present case study demonstrates that the decision support tool can tackle large-scale problems and can help operators efficiently and effectively design an optimal combination of stopping patterns.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]US