Optimization using simulation and response surface methodology with an application on subway train scheduling

Abstract

This paper presents a technique based on discrete‐event simulation and response surface methodology to model and then optimize the schedule of subway train travels. The aim of this study is to find appropriate headways—time intervals between the travels of two consecutive trains—at different hours in order to optimize average passenger travel time and rate of carriage fullness. For physical reasons and the observance of safety standards, an increase in the train speed in order to decrease average passenger travel time may not exceed some specified limits. One of the ways to decrease this average is to appropriately adjust headways of trains in the schedule. For this purpose, a metamodel of multinomial type is fitted to the data obtained from simulation tests to describe the relation between input variables (headways) and output variables (average passenger travel time in system and carriage fullness rate), and then optimal combinations of input variables are obtained using a weighed metric method and sequential quadratic programming.

An ILP approach for tactical flight rescheduling during airport access mode disruptions

“…Their work can be seen as a baseline for implementing collaborative decision-making processes and achieving single ticketing. On the ground side, Rahimi Mazrae Shahi et al (2016) optimise subway headways to minimise passenger travel time while having appropriate train load factors. They combine a discrete-event simulation and a response surface methodology to optimise the train schedule with limited simulation runs.…”

An Optimization Model for Scheduling Freight Trains on a Single Rail Track

“…Murali et al [18] proposed an expert tool to help train schedule planners determine proper routes and schedules for short time frames, and to manage the restricted track capacity available for train movements. Rahimi Mazrae Shahi et al [19] developed a technique based on discrete-event simulation and response surface methodology to model and then optimize the schedule of subway train travels. In a recent study, Behiri et al [20] formulated the problem of the freight rail transport scheduling using mixed integer programming and proved the NP-hardness of the problem.…”