Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

Bashiri, Mahdi; Rezanezhad, M.; Tavakkoli–Moghaddam, Reza; Hasanzadeh, Hamid

doi:10.1016/j.apm.2017.09.032

Cited by 46 publications

(40 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed the artificial bee colony and differential evolution algorithms to solve their model. Bashiri et al (2018) presented a new dynamic mobile p-hub location problem, in which hub facilities with a mobility feature are transferred to other nodes to meet demands. They proposed simulated annealing and genetic algorithms to solve this model.…”

Section: Literature Reviewmentioning

confidence: 99%

A new bi-objective model of the urban public transportation hub network design under uncertainty

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper presents a new bi-objective multi-modal hub location problem with multiple assignment and capacity considerations for the design of an urban public transportation network under uncertainty. Because of the high construction costs of hub links in an urban public transportation network, it is not economic to create a complete hub network. Moreover, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the elasticity of the demand is considered in this paper. The presented model also has the ability to compute the number of each type of transportation vehicles between every two hubs. The objectives of this model are to maximize the benefits of transportation by establishing hub facilities and to minimize the total transportation time. Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, an intensive sensitivity analyze study is carried out on a real-case application related to the monorail project of the holy city of Qom.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A new bi-objective model of the urban public transportation hub network design under uncertainty

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we introduce a GA, which is proposed by [3]. GA introduced by [41] is based on natural biological evolution and has been successfully applied to various combinatorial optimization problems in hub location problem ( [42][43][44][45][46][47][48][49][50][51][52]).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

Huo

Hou

2019

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.

show abstract

“…They proposed a hybrid heuristic of multi-start simulated annealing and ant colony system for solving the problem [35]. Bashiri et al developed a mathematical model for the Pmobile hub location problem to improve network efficiency and used a genetic algorithm augmented by a local search algorithm to solve large instances of the proposed model [36]. In another research, Mogale et al developed an integrated multi-objective, multi-modal and multi-period model for the grain silo location-allocation problem with dwell time.…”

Section: Hybrid Metaheuristic Literaturementioning

confidence: 99%

A P-hub median network design problem with preventive reliability approach for before and after hub failure

Eydi

Nasiri

2019

Sādhanā

View full text Add to dashboard Cite

Hubs are vital elements of communication and transportation networks and play an important role in interchanging the flows of information/passenger/goods. For this purpose, designing a highly reliable hub network is very critical, because inefficiency of even a single hub across the network tends to reduce the efficiency of the whole network in transferring the flow appropriately. In this research, a bi-objective mathematical model was designed to study the situations before and after hub failure. Considering reliability, the first objective was to maximize the flow through the network, and the second objective was to prevent wasting the flow due to a possible hub failure. The lexicographic method was used to solve this multi-objective problem with dependent objectives. This method represents an appropriate solution for problems whose objective functions are of different priorities or depend on one another. Various cases of different sizes were used to evaluate the model in terms of reliability. Since the hub location problem is an NP-Hard problem of commonly large dimensions, a hybrid meta-heuristic algorithm called ''memetic algorithm'' was used to have it solved. The algorithm was a combination of genetic algorithm with simulated annealing algorithm, where simulated annealing algorithm was used for local neighborhood search. Findings indicated that, consideration of the backup hub tends to enhance route reliability, thereby increasing the flow through the network, as compared to the case with no backup hub.

show abstract

Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

Cited by 46 publications

References 40 publications

A new bi-objective model of the urban public transportation hub network design under uncertainty

A new bi-objective model of the urban public transportation hub network design under uncertainty

A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

A P-hub median network design problem with preventive reliability approach for before and after hub failure

Contact Info

Product

Resources

About