The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of ...

The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of ...

The problem of maximal hub covering as a challenging problem in operation research. Transportation programming seeks to find an optimal location of a set of hubs to reach maximum flow in a network. Since the main structure's parameters of the problem such as origin-destination flows, costs and travel time, change periodically in the real world applications, new issues arise in handling it. In t...

The classical 1962 Benders decomposition scheme is a traditional approach for solving mixed-integer problems such as the uncapacitated facility location problem. Subsequent research has been focused on finding better cutting-plane generation schemes to reduce the solution process time. Pareto-optimal cuts are typically preferred because no other cut can dominate them. However, the Pareto-optim...

In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the net...

In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand uncertain and the level of conservatism controlled by uncertainty budget. first stage, locations establishing facilities were determined, allocation decisions made second stage. An accelerated Benders decomposition algorithm was used to solve problem. Computational experiments s...

We investigate a logistics facility location problem to determine whether the existing facilities remain open or not, what the expansion size of the open facilities should be and which potential facilities should be selected. The problem is formulated as a mixed integer linear programming model (MILP) with the objective to minimize the sum of the savings from closing the existing facilities, th...

this paper presents an application of benders decomposition to deal with the complexities in the simultaneous generation expansion planning (gep) and transmission expansion planning (tep). unlike the power system operation fields of study, the power system planning methods are not expected to be fast. however, it is always preferable to speed up computations to provide more analysis options for...

This paper presents the first stochastic mixed integer programming model for a comprehensive hybrid power system design, including renewable energy generation, storage device, transmission network, and thermal generators, in remote areas. Given the computational complexity of the model, we developed a Benders’ decomposition algorithm with Pareto-optimal cuts. Computational results show signific...

the hub location decision is a long term investment and any changes in it take considerable time and money. in real situations, some parameters are uncertain hence, deterministic models cannot be more efficient. the ability of two-stage stochastic programming is to make a long-term decision by considering effects of it in short term decisions simultaneously. in the two-stage stochastic programm...