Fuzzy programming for multi-choice bilevel transportation problem

Multi-choice programming problems arise due to diverse needs of people. In this paper, multi-choice optimisation is applied to bilevel transportation problem. This problem deals with transportation at both the levels, upper as well as lower. There are multiple choices for demand and supply parameters. The multi-choice parameters at the respective levels are converted into polynomials which transmute the defined problem into a mixed integer programming problem. The objective of the paper is to determine a solution methodology for the transformed problem. The significance of the formulated model is exhibited through an example by applying it to a hotel industry. The fuzzy programing approach is employed to obtain the satisfactory solution for the decision makers at the two levels. A comparative analysis is presented in the paper by solving bilevel multi-choice transportation problem with goal programming mode as well as by the linear transformation technique proposed in the paper by Khalil et al. The example is solved using computing software.

Topological Effects and Performance Optimization in Transportation Continuous Network Design

Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO) method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.