Multi-objective simulation optimization for complex urban mass rapid transit systems

Abstract

In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a nondominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our twophase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

“…It is calculated by dividing the average service rate (mu, ) by the average arrival rate (lambda, ). The average service rate should always be higher than the average arrival rate for a stable system [35]. 10.12928/ijio.v5i1.7963…”

Hierarchical Bayesian approach for improving weights for solving multi-objective route optimization problem

“…In contrast to the objective of weight determination of the existing works, which aimed at choosing the set of weights which stabilizes the solution set [23][24][25], this work proposes to frame a model which determines a much stable set of weights in comparison to that obtained deterministically. The criticisms of the existing methodologies for determination of weights have motivated this work and to propose the Bayesian model based on multinomial and Dirichlet priors.…”

Bayesian hierarchical multi-objective optimization for vehicle parking route discovery

“…fitter among them is chosen as one of the parents. For example Chromosome 1: [0, 6,22,15,25,20,18,28] fitness= .728542 Chromosome 2: [0, 8,7,23,3,6,22,13,25,11,2,29] fitness= .859741 Chromosome 3: [0, 4,22,14,13,25,11,26,2,19,17,20,18,28] fitness = .958308 Chromosome 1, being the fittest among the three gets selected as Parent 1. Since the problem deals with minimization, lower the fitness value, fitter is the chromosome.…”

“…Since the problem deals with minimization, lower the fitness value, fitter is the chromosome. Chromosome 4: [0, 4,22,14,13,25,11,26,2,19,17,20,18,28] fitness= .958308 Chromosome 5: [0, 7,4,6,22,13,1,11,2,17,21,28] fitness= .960787 Chromosome 6: [0, 3,6,22,15,1,11,26,21,28] fitness= . 827978 Here, Chromosome 6 is fittest and is selected as Parent 2.…”

“…Majority of the existing works have aimed at choosing the set of weights such that it stabilizes the solution set [11][12][13], this work proposes to frame a model which determines a much stable set of weights in comparison to that obtained deterministically. The criticisms of the existing methodologies for determination of weights have motivated this work and to propose the Bayesian model based on multinomial and Dirichlet priors.…”