Bi-Level Programming Model and Algorithms for Stochastic Network With Elastic Demand

Abstract

2015) Bi-level programming model and algorithms for stochastic network with elastic demand, Transport, 30:1, 117-128, Abstract. Based on a state-of-the-art review of the Road Network Design Problem (RNDP), this paper proposes a bi-level programming model for the RNDP as well as algorithms for it. In the lower level of the proposed model, the elastic-demand Stochastic User Equilibrium (SUE) model is adopted to coincide well with characteristics of users behavior, and additionally, the parameter calibration method for the model is developed based on the Logit path choice model. In the upper level of the proposed model, the consumer surplus is maximized to improve the social benefit of a network in consideration of the travel demand, the construction cost, the off-gas emissions and the saturation level. The algorithm for the lower-level model is developed based on the descent iteration method, Dijkstra's algorithm and linear search technology. A modified Genetic Algorithm (GA) is developed as the algorithm for the whole bi-level model, which takes designed elitist selection operator, adaptive cross operator, mutation operator and niche technology into consideration. The proposed model and algorithms are applied to a numerical example. The results demonstrate the validity and efficiency of the model and algorithms, which shows a bright prospect of the application in RNDP.

A Bi-Level Multi-Objective Optimization Model for Micro-Circulation Road Networks in the Open Block Area Considering Traffic Pollution and Intersection Delays

“…The computational complexity and accuracy of each method are different. Among these algorithms, the genetic algorithm has become one of the most commonly used methods because of its parallelism and compatibility [43]. GA is inspired by the biological evolution process and it is widely used to generate high-quality solutions for optimization and search problems [44].…”

“…Compared with other heuristic algorithms, GA has three main advantages: (1) The operation object of GA is a group of feasible solutions rather than a single solution. Hence, GA has better global search capability [43]; (2) GA dynamically changes the search process by crossover probability and mutation probability to avoid trapping in a local optimum to some extent [46]; (3) GA has fewer requirements for the objective function. For example, the objective function does not need to meet certain requirements such as differentiability.…”

Multitype Recharge Facility Location for Electric Vehicles

“…In terms of solution methodologies, due to the intrinsic complexity of the general nonconvex bilevel formulation of the NDP, metaheuristic approaches are primarily employed. The use of metaheuristic evolutionary algorithms for multiobjective optimization problems has been exploited over the last 30 years, including genetic algorithms (GAs) (Chakroborty, ; Zhang et al., ), ant colony optimization (Poorzahedy and Abulghasemi, ), simulated annealing algorithms (Friesz et al., ), and hybrid heuristic (Bagloee et al., ). Among all these algorithms, the GA is the most prevalent due to its parallelism and compatibility (Drezner and Salhi, ; Sun et al., ; Unnikrishnan and Lin, ; Farahani et al., ; Zhang et al., ).…”

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

“…Generally speaking, the bilevel programming is NP-hard problem, Ben-Ayed and Blair (1988) have proved that [16] this problem does not have a polynomial algorithm, so the solution of it is very complicated. Like genetic algorithm (GA) and Simulated Annealing Algorithm (Simulated Annealing, SA), Neural Network Algorithm (NNA), and so forth, some intelligent algorithms were used to solve the bilevel programming problem.…”