چکیده:
This paper studies the incapacitated P-hub centre problem in a network under decentralized management assuming time as a fuzzy variable. In this network, transport companies act independently, each company makes its route choices according to its own criteria. In this model, time is presented by triangular fuzzy number and used to calculate the fraction of users that probably choose hub routes instead of direct routes. To solve the problem, two genetic algorithms are proposed. The computational results compared with LINGO indicate that the proposed algorithm solves large-scale instances within promising computational time and outperforms LINGO in terms of solution quality.
خلاصه ماشینی:
"Nematian (2016) presented an incapacitated p-hub center problem in case of single allocation and also multiple allocations in which travel times or transportation costs were considered as fuzzy parameters.
Genetic algorithms in HLPs رجوع شود به تصویر صفحه According to the literature of HLP solution approaches, we think that UMHLP problem under decentralized management has not yet been solved for large-sized scale problems, but it is a very common case in the analysis of networks of regional or greater scope.
In order to provide the best initial state of the network, for each problem size / and (assumed pre- established hubs, GA1 was run 30 times and the solution with the lowest cost value obtained becomes the initial network.
For each problem instance, n denotes the number of nodes, pre-hub denote the initial state of network, / , the optimal values of the objective function (initiate cost of the network before assuming the time as a fuzzy parameter), the numbers in the column labelled "/" , the final cost of the objective function after applying the proposed model.
Comparison of Computational results In order to measure the effectiveness of our method, we solved an instance with n=10, 20 node with deterministic and fuzzy time parameter and then compare the solution quality and running times in the both models.
Since, in the model, we sought to solve the large-sized UHLP-DM problem, it was solved using two genetic algorithms; the first one, GA1, was applied to create the initial network in which there were some pre-established hubs and the second one, GA2, was applied to find new hubs in such a network with regarding to the objective function."