MIP models and a hybrid method for the capacitated air-cargo network planning and scheduling problems

Zhang, Canrong; Xie, Fangxia; Huang, Kun; Wu, Tao; Liang, Zhe

doi:10.1016/j.tre.2017.05.003

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…Most of the main parameters of the problem such as travel time (distance), demand, hub capacities and costs, which are used as an example to solve, are from a real case in Turkey. 29 Other parameters such as carrier capacities and hourly transportation cost of carriers are taken from Zhang et al 21 Other parameters are included in Table 1. This should be marked that for parameters A kmlt and rev ijt to save space, we just include the minimum and maximum values using [min ij , max ij ] format (there are four sets of ([min ij , max ij ] values at each period t for A kmlt , which refers to each l).…”

Section: Computational Resultsmentioning

confidence: 99%

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Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Alizadeh

Nishi

2019

Advances in Mechanical Engineering

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In this article, a dynamic maximal hub location covering problem for a freight transportation system is studied, in which the model has the possibility of having expansion scenarios for future according to the forecasts of increasing demands. Two expansion scenarios are to add up the number of hubs in the network and to add up more carriers. As the markets are involved in the pricing procedure, the model is a bi-level problem which needs more effort to deal with, for which in this work two reformulations based on Karush-Kuhn-Tucker conditions and duality theory are utilized to reformulate the bi-level problem to a single-level one. To solve the model efficiently, a decomposition method is applied and numerical examples are solved to verify the accuracy of the proposed model.

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Section: Computational Resultsmentioning

confidence: 99%

“…Most recently, Zhang et al 21 have examined hub location and plane assignment problems for the aircargo delivery service. They presented two mixed integer programming models.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Alizadeh

Nishi

2019

Advances in Mechanical Engineering

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“…The loading optimization problem is an NP-hard problem [11], which is difficult to solve. In this study, the model was solved by the improved genetic algorithm.…”

Section: Solution By Improved Genetic Algorithmmentioning

confidence: 99%

Research on Airfreight Loading Optimization of E-Commerce Logistics Based on Improved Genetic Algorithm

2021

IJOMAM

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At present, more and more e-commerce logistics use airfreight. To further optimize the freight loading problem, this study first established the freight loading optimization model, improved the genetic algorithm to solve the model, and made an example analysis. It was found that the load utilization rate was 96.63%, and the space utilization rate was 92.95% in the scheme obtained by the algorithm proposed in this study, which was higher than those of the other two algorithms. In the aspect of calculation time, the method proposed in this study could obtain the optimal scheme in 56.78 s. The experimental results show that the proposed algorithm performs better in the loading optimization problem and can be further promoted and applied.

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“…This issue has not been addressed in the literature to date. However, there are research in the literature on considering operational decisions in HLP that indicate the importance of considering integrated decisions in this scope [see Zhang et al (2017), Karimi and Setak (2018), Masaeli et al (2018) and Charisis et al (2020)].…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al (2015) designed a timetable for managing airport which is a new access service provided by the airport with the purpose of attracting more passengers. Zhang et al (2017) considered an integrated plane assignment and hub location problem for the air-cargo delivery service. They present two different MIP model to integrate hub location, flow allocation and plane scheduling and also developed a two-stage hybrid algorithm for solving large-size instances.…”

Section: Introductionmentioning

confidence: 99%

Integrated hub location and flow processing schedule problem under renewable capacity constraint

Rostami

Jabbarzadeh

2021

Comp. Appl. Math.

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Transport and logistics networks are more complex than ever before. Complex networks and limitation of available capacities make flow processing schedule (FPS) a challenging problem to ensure customer satisfaction and profitability. This paper addresses an integrated hub location and flow processing schedule problem (HLFPSP) to determine hub locations, the allocated traffic flows and the optimal FPS at hubs under capacity constraints. Specifically, in an air transport network, the optimal FPS leads to optimal flight scheduling. The problem is formulated as a mixed-integer linear programming (MILP) model to minimize total tardiness costs (at operational level) and hub construction costs (at strategic level), simultaneously. The developed model is utilized to solve small-size hub location-scheduling problems. To provide a good solution in a reasonable time, the Lagrangian relaxation algorithm is employed. Based on the data from 80 airports in Turkey, the application of problem in real world is showed and the efficiency of the proposed solution methods is evaluated. Finally, a number of sensitivity analyses and managerial insights are provided to enrich the computational results.

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MIP models and a hybrid method for the capacitated air-cargo network planning and scheduling problems

Cited by 16 publications

References 29 publications

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Research on Airfreight Loading Optimization of E-Commerce Logistics Based on Improved Genetic Algorithm

Integrated hub location and flow processing schedule problem under renewable capacity constraint

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