A Biobjective and Trilevel Programming Model for Hub Location Problem in Design of a Resilient Power Projection Network

Abstract

Hubs disruptions are taken into account in design of a resilient power projection network. The problem is tackled from a multiple criteria decision-making (MCDM) perspective. Not only the network cost in normal state is considered, but also the cost in the worst-case situation is taken into account. A biobjective and trilevel integer programming model is proposed using game theory. Moreover, we develop a metaheuristic based on tabu search and shortest path algorithm for the resolution of the complex model. Computational example indicates that making tradeoffs between the performances of the network in different situations is helpful for designing a resilient network.

Method for Determining the Location of Highway Passenger Transportation Hubs Using POI Data and the Dijkstra Algorithm in Large City

“…Te set of nodes along urban expressway P E and the set of nodes along other grades of roads P E are as follows: 3,4,5,6,7,8,9,10,11,12,13,14,15,16,28,35,40,60,70,85,86,84,83,82,81,80,61,51,46,91,90,89,88,87,27,34,39,59,69,68,67,66,65,64,63,62,52,47,41,22,29,36,37,38,20,25,32,57 Ten, mathematical modeling methods (such as integer programming) are used to fnd the optimal result from a limited set of candidate hub locations. Te set of candidate hub locations selected based on land-use planning can greatly reduce the computational complexity.…”

“…Location problems are an important feld of operations research. Because of its extensive application in practice, it has attracted the attention of many scholars [3][4][5][6][7][8][9]. Te methods for determining the location of passenger transportation hubs are mostly divided into two categories: (a) continuous location models, which mainly include the center-of-gravity approach, and (b) discrete location models, which require that the candidate locations of hubs are limited and countable and the optimal location is determined through evaluation.…”

A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures

“…The algorithm runs a total of 20 times. The parameter values in the tabu procedure are set using the experiences from our early related work; see Bi et al [2]. The number of Monte Carlo trials for each hub reliability level is 10,000, and the statistical standard error is about 0.2%.…”

“…through motorized march firstly, and then they are delivered using backbone transportation (highway, railway, marine transport, etc.). Finally they are deployed to dispersed locations through motorization march according to the mission demands [2]. The ability of a state to project its forces into an area may serve as an effective diplomatic lever, influencing the decision-making process and acting as a potential deterrent on other states' behavior.…”

“…Bi et al [2] assumed that the enemy would select the hubs for attack which result in the greatest damage to the network. They gave a resilient hub location strategy considering the failure of specific hubs.…”