Modelling and optimization of average travel time for a metro line by simulation and response surface methodology


YALÇINKAYA Ö., Bayhan G. M.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.196, no.1, pp.225-233, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 196 Issue: 1
  • Publication Date: 2009
  • Doi Number: 10.1016/j.ejor.2008.03.010
  • Journal Name: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.225-233
  • Keywords: Average passenger travel time, Optimization, Simulation, Metamodel, Response surface methodology, The Derringer-Suich optimization procedure, MANUFACTURING SYSTEM, GENETIC ALGORITHM, NETWORK, DESIGN, CHOICE
  • Dokuz Eylül University Affiliated: Yes

Abstract

This research presents a modelling and solution approach based on discrete-event simulation and response surface methodology for dealing with average passenger travel time optimization problem inherent to the metro planning process. The objective is to find the headways optimizing passenger average travel time with a satisfactory rate of carriage fullness. Due to some physical constraints, traffic safety and legal requirements, vehicle speeds cannot be raised any further to decrease travel time. But travel time can be optimized by arranging headways (i.e. the time period between the departure times of two consecutive transportation vehicles) in a timetable. In the presented approach, simulation metamodels that best fit the data collected from the simulated experiments are constructed to describe the relationship between the responses (average travel time and rate of carriage fullness) and input factors (headways). Then, the Derringer-Suich multi-response optimization procedure is used to determine the optimal settings of the input factors that produce the minimum value of the average travel time by providing a proper rate of carriage fullness. This methodology is applied for a real metro line, and good quality solutions are obtained with reduced number of experiments that needed to provide sufficient information for statistically acceptable results. (C) 2008 Elsevier B.V. All rights reserved.