Transmission network expansion planning (TNEP) is an important component of power system planning. Itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. Different methods have been proposed for the solution of the static transmissionnetwork expansion planning (STNEP) problem till now. But in all of them, STNEP pr...

Discrete network design is an important part of urban transportation planning. The purpose of this paper is to present a bilevel model for discrete network design. The upper-level model aims to minimize the total travel time under a stochastic demand to design a discrete network. In the lower-level model, demands are assigned to the network through a multiuser traffic equilibrium assignment. Ge...

this paper develops a model for distribution network expansion planning (dnep) considering reliability issues. with simultaneous switch placement and dnep, the proposed model tries to adequately consider the reliability issues in the planning stage in a way that the designed network has suitable performance from the reliability viewpoint. the reinforcement or installation of mv feeders and hv/m...

By exploring of the multi-stage network design problem of comprehensive traffic, the paper proposes a bi-level programming model with uncertain demand. The upper level problem is to maximize the consumer surplus of all demand scenarios with budget constraint. While under the network investment decisions of the upper level problem, the lower level problem is to maximize the consumer surplus of e...

In the field of transportation planning, Network Design Problem (NDP) is a complex and challenging research area which aims to optimize a transportation network in order to maximize traffic and social benefits through capacity expansion and link addition. Recently, ‘equity’ has become highly valued in the decision process of NDP. The objective of this study is to propose novel approaches to int...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

One of the most important problems for distribution companies is to find the best locations for depots and to find proper routes for transportation vehicles and to optimize supply network. This study intends to develop a model for the problem of location-routing in post offices. So, a new Bi-Objective Location-Routing Problem for Locating Town Post Office and Routing Parcels is defined. This pr...

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network wit...