Evolutionary Combinatorial Programming for Discrete Road Network Design with Reliability Requirements

This paper examines the formulation and solution of the discrete version of the stochastic Network Design Problem (NDP) with incorporated network travel time reliability requirements. The NDP is considered as a two-stage Stackelberg game with complete information and is formulated as a combinatorial stochastic bi-level programming problem. The current approach introduces the element of risk in the metrics of the design process through representing the stochastic nature of various system components related to users' attributes and network characteristics. The estimation procedure combines the use of mathematical simulation for the risk assessment with evolutionary optimization techniques (Genetic Algorithms), as they can suitably address complex non-convex problems, such as the present one. The implementation over a test network signifies the potential benefits of the proposed methodology, in terms of intrinsically incorporating stochasticity and reliability requirements to enhance the design process of urban road networks.

[1]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[2]  Yafeng Yin,et al.  Optimal Improvement Scheme for Network Reliability , 2002 .

[3]  Yosef Sheffi,et al.  Urban Transportation Networks: Equilibrium Analysis With Mathematical Programming Methods , 1985 .

[4]  Agachai Sumalee,et al.  Reliable Network Design Problem: Case with Uncertain Demand and Total Travel Time Reliability , 2006 .

[5]  David E. Boyce,et al.  A general bilevel linear programming formulation of the network design problem , 1988 .

[6]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[7]  Michael G.H. Bell,et al.  A game theory approach to measuring the performance reliability of transport networks , 2000 .

[8]  Y Iida,et al.  Transportation Network Analysis , 1997 .

[9]  Thomas L. Magnanti,et al.  Network Design and Transportation Planning: Models and Algorithms , 1984, Transp. Sci..

[10]  Larry J. LeBlanc,et al.  An Algorithm for the Discrete Network Design Problem , 1975 .

[11]  Hong Kam Lo,et al.  Capacity reliability of a road network: an assessment methodology and numerical results , 2002 .

[12]  Athanasios K. Ziliaskopoulos,et al.  Stochastic Dynamic Network Design Problem , 2001 .

[13]  Terry L. Friesz,et al.  TRANSPORTATION NETWORK EQUILIBRIUM, DESIGN AND AGGREGATION: KEY DEVELOPMENTS AND RESEARCH OPPORTUNITIES. IN: THE AUTOMOBILE , 1985 .

[14]  Michael G.H. Bell,et al.  Transportation Network Analysis: Bell/Transportation Network Analysis , 1997 .

[15]  Alan Nicholson,et al.  DEGRADABLE TRANSPORTATION SYSTEMS: SENSITIVITY AND RELIABILITY ANALYSIS , 1997 .

[16]  Caroline S. Fisk,et al.  A Conceptual Framework for Optimal Transportation Systems Planning with Integrated Supply and Demand Models , 1986, Transp. Sci..

[17]  Patrice Marcotte,et al.  Bilevel programming: A survey , 2005, 4OR.

[18]  Ziyou Gao,et al.  Solution algorithm for the bi-level discrete network design problem , 2005 .

[19]  Ben Paechter,et al.  THE CONTINUOUS EQUILIBRIUM OPTIMAL NETWORK DESIGN PROBLEM: A GENETIC APPROACH , 1998 .

[20]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[21]  R. Iman,et al.  A distribution-free approach to inducing rank correlation among input variables , 1982 .