scholarly journals Intuitionistic Fuzzy Hub Location Problems: Model and Solution Approach

Author(s):  
Malihe Niksirat
Author(s):  
Firoz Ahmad

AbstractThis study presents the modeling of the multiobjective optimization problem in an intuitionistic fuzzy environment. The uncertain parameters are depicted as intuitionistic fuzzy numbers, and the crisp version is obtained using the ranking function method. Also, we have developed a novel interactive neutrosophic programming approach to solve multiobjective optimization problems. The proposed method involves neutral thoughts while making decisions. Furthermore, various sorts of membership functions are also depicted for the marginal evaluation of each objective simultaneously. The different numerical examples are presented to show the performances of the proposed solution approach. A case study of the cloud computing pricing problem is also addressed to reveal the real-life applications. The practical implication of the current study is also discussed efficiently. Finally, conclusions and future research scope are suggested based on the proposed work.


2020 ◽  
pp. 106955
Author(s):  
Sebastian Wandelt ◽  
Weibin Dai ◽  
Jun Zhang ◽  
Qiuhong Zhao ◽  
Xiaoqian Sun

1970 ◽  
Vol 24 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Jasmina Pašagić Škrinjar ◽  
Kristijan Rogić ◽  
Ratko Stanković

In this paper the problems of locating urban logistic terminals are studied as hub location problems that due to a large number of potential nodes in big cities belong to hard non-polynomial problems, the so-called NP-problems. The hub location problems have found wide application in physical planning of transport and telecommunication systems, especially systems of fast delivery, networks of logistic and distribution centres and cargo traffic terminals of the big cities, etc. The paper defines single and multiple allocations and studies the numerical examples. The capacitated single allocation hub location problems have been studied, with the provision of a mathematical model of selecting the location for the hubs on the network. The paper also presents the differences in the possibilities of implementing the exact and heuristic methods to solve the actual location problems of big dimensions i.e. hub problems of the big cities.


2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alan Osorio-Mora ◽  
Francisco Núñez-Cerda ◽  
Gustavo Gatica ◽  
Rodrigo Linfati

Hub location problems (HLPs) support decision making on multimodal transport strategic planning. It is related to the location of hubs and the allocation of origin/destination (O/D) flow in a system. Classical formulations assume that these flows are predefined paths and direct delivery is not available. This applied research presents a mixed integer linear programming (MILP) model for a capacitated multimodal, multi-commodity HLP. Furthermore, an application on the export process in a Latin American country is detailed. The new proposed model, unlike the traditional HLP, allows direct shipment, and its O/D flows are part of the decision model. Situations with up to 100 nodes, six products, and two transport modes are used, working with initial and projected flows. All instances can be solved optimally using the commercial solver, Gurobi 7.5.0, in computational times less than a minute. Results indicate that only one hub is profitable for the case study, both for the initial and projected scenarios. The installation of a hub generates transport savings over 1% per year. Two factors affect the location decision: low concentration and distance between the hubs and destinations. Long distances involve an exhaustive use of trains instead of trucks, which leads to lower transport cost per unit.


2018 ◽  
Vol 122 ◽  
pp. 39-86 ◽  
Author(s):  
Sara Sadat Torkestani ◽  
Seyed Mohammad Seyedhosseini ◽  
Ahmad Makui ◽  
Kamran Shahanaghi

2014 ◽  
Vol 35 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Fereidoon Habibzadeh Boukani ◽  
Babak Farhang Moghaddam ◽  
Mir Saman Pishvaee

2018 ◽  
Vol 7 (4) ◽  
pp. 115-155
Author(s):  
Javad Nematian

Hubs are facilities to collect, arrange and distribute commodities in telecommunication networks, cargo delivery systems, etc. In this article, it will study two popular hub location problems (p-hub center and p-hub maximal covering problems) under uncertainty. First, novel reliable uncapacitated p-hub location problems are introduced based on considering the failure probability of hubs, in which the parameters are random fuzzy variables, but the decision variables are real variables. Then, the proposed hub location problems under uncertainty are solved by new methods using random fuzzy chance-constrained programming based on the idea of possibility theory. These methods can satisfy optimistic and pessimistic decision makers under uncertain framework. Finally, some benchmark problems are solved as numerical examples to clarify the described methods and show their efficiency.


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