Flight Flow Balanced Allocation in Terminal Airspace

2013 ◽  
Vol 441 ◽  
pp. 602-606
Author(s):  
Wei Jun Pan ◽  
Wen Bin Qiu ◽  
Rui Kang
Keyword(s):  
Integer Programming ◽  
Programming Model ◽  
Air Traffic ◽  
Nonlinear Integer Programming ◽  
Flight Delays ◽  
Air Traffic Controllers ◽  
Balanced Allocation ◽  
Airport Terminal ◽  
Flight Route ◽  
Better Than

A nonlinear integer programming model (NIPM) with constraints is proposed to solve the allocation of approach flight flow where ends with terminal airspace, an example of an airport terminal airspace is given, where the flow is accurately forecasted.Analysising flight delays, theres a conclusion: the results solved by NIPM is far better than the average allocation method, for the second-level airspace, NIPM can reduce two flight delays, and the allocation in each flight route tends to be equilibrium, NIPM can also provide air traffic controllers with accurate and reasonable allocation schedule.

Integer Programming Model for Solving Delay Problem in Air Traffic Management System

2020 ◽  
Vol 17 (7) ◽  
pp. 3160-3163
Author(s):  
Ellis Mardiana Panggabean ◽  
Herman Mawengkang ◽  
M. Zarlis ◽  
Syahril Efendi
Keyword(s):  
Integer Programming ◽  
Traffic Management ◽  
Management System ◽  
Programming Model ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Flight Delays ◽  
Integer Programming Model ◽  
Operational Costs ◽  
Traffic Management System

Nowadays, due to the rapid growth of air traffic and airspace congestion flight delays are becoming serious problems. This situation happens due to the demand capacity imbalances in air traffic management system. The deterioration of the expected service and operational costs would be the direct impact of this problem. Network-wide air traffic flow management can be regarded as an effective way to ease demand-capacity imbalances globally and would be able to reduce airspace congestion and flight delays. This paper proposes an integer programming model to solve the problem. The objective function consists of minimizing operational costs and risk costs.

scholarly journals The unit train make-up scheme for loaded direction in the heavy haul railway

2019 ◽  
Vol 1 (1) ◽  
pp. 30-44 ◽  
Author(s):  
Yuqiang Wang ◽  
Yuguang Wei ◽  
Hua Shi ◽  
Xinyu Liu ◽  
Liyuan Feng ◽  
...  
Keyword(s):  
Integer Programming ◽  
Design Methodology ◽  
Arrival Time ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Content Type ◽  
Proposed Model ◽  
Heavy Haul ◽  
Heavy Haul Railway

Purpose The purpose of this paper is to study the unit train make-up scheme for loaded direction in the heavy haul railway. Design/methodology/approach A 0-1 nonlinear integer programming model with the aim of minimizing the idling period between actual train arrival time and expected train arrival time for all loaded unit trains are proposed. Findings The proposed model is applied into a case study based on Daqin heavy haul railway. Results show that the proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway. Originality/value The proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

scholarly journals A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hao Guo ◽  
Congdong Li ◽  
Ying Zhang ◽  
Chunnan Zhang ◽  
Yu Wang
Keyword(s):  
Supply Chain ◽  
Integer Programming ◽  
Facility Location ◽  
Vehicle Routing ◽  
Closed Loop ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Inventory Routing ◽  
Closed Loop Supply Chain ◽  
Integer Programming Model

Facility location, inventory management, and vehicle routing are three important decisions in supply chain management, and location-inventory-routing problems consider them jointly to improve the performance and efficiency of today’s supply chain networks. In this paper, we study a location-inventory-routing problem to minimize the total cost in a closed-loop supply chain that has forward and reverse logistics flows. First, we formulate this problem as a nonlinear integer programming model to optimize facility location, inventory control, and vehicle routing decisions simultaneously in such a system. Second, we develop a novel heuristic approach that incorporates simulated annealing into adaptive genetic algorithm to solve the model efficiently. Last, numerical analysis is presented to validate our solution approach, and it also provides meaningful managerial insight into how to improve the closed-loop supply chain under study.

scholarly journals A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Kaiquan Cai ◽  
Yaoguang Jia ◽  
Yanbo Zhu ◽  
Mingming Xiao
Keyword(s):  
Network Flow ◽  
Programming Model ◽  
Air Traffic ◽  
Weather Data ◽  
Traffic Network ◽  
Flow Optimization ◽  
Flight Delays ◽  
Air Traffic Flow Management ◽  
Traffic Flow Management ◽  
Network Flow Optimization

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

Paratransit Service Optimization for Special Events

2005 ◽  
Vol 1903 (1) ◽  
pp. 64-77
Author(s):  
Matthew G. Karlaftis ◽  
Konstantinos L. Kepaptsoglou ◽  
Antony Stathopoulos
Keyword(s):  
Integer Programming ◽  
Travel Time ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Time Constraints ◽  
Microsoft Excel ◽  
Integer Programming Model ◽  
Special Events ◽  
User Friendly ◽  
Service Optimization

Paratransit services can be useful for special events, especially when private vehicles are discouraged from approaching the event locations. During the Athens 2004 Olympics, such a shuttle service was planned to connect major Athens spots with athletic complexes. A mixed nonlinear integer programming model is developed for jointly obtaining optimal headways and vehicle types for such a paratransit service, given demand, resource, and travel time constraints. The model is incorporated into a user-friendly Microsoft Excel–based interface. An application of the model to the Athens 2004 Olympics and its results are presented and discussed.

scholarly journals An integer programming approach to a real-world recyclable waste collection problem in Argentina

2017 ◽  
Vol 35 (5) ◽  
pp. 525-533 ◽  
Author(s):  
Gustavo Braier ◽  
Guillermo Durán ◽  
Javier Marenco ◽  
Francisco Wesner
Keyword(s):  
Integer Programming ◽  
Programming Model ◽  
Programming Approach ◽  
Waste Collection ◽  
Collection System ◽  
Routing Problem ◽  
Rural Postman Problem ◽  
Programming Techniques ◽  
Large Municipality ◽  
Better Than

This article reports on the use of mathematical programming techniques to optimise the routes of a recyclable waste collection system servicing Morón, a large municipality outside Buenos Aires, Argentina. The truck routing problem posed by the system is a particular case of the generalised directed open rural postman problem. An integer programming model is developed with a solving procedure built around a subtour-merging algorithm and the addition of subtour elimination constraints. The route solutions generated by the proposed methodology perform significantly better than the previously used, manually designed routes, the main improvement being that coverage of blocks within the municipality with the model solutions is 100% by construction, whereas with the manual routes as much as 16% of the blocks went unserviced. The model-generated routes were adopted by the municipality in 2014 and the national government is planning to introduce the methodology elsewhere in the country.

Study of Solving Crossing Origin-Destination Matrix Based on Entropy Maximizing Model

2012 ◽  
Vol 182-183 ◽  
pp. 970-974
Author(s):  
Guo Jiang Fu
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Maximum Entropy ◽  
Nonlinear Equations ◽  
Lagrange Multiplier ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Flow Matrix

The maximum-entropy model is one of important methods in estimating traffic origin-destination matrix from observed traffic link flows, and it is a nonlinear integer programming model. To find the best solution, traditionally it was transformed to solve nonlinear equations by the introduction of Lagrange multiplier and Newton’s method is adopted to solve the nonlinear equations. In this paper, a entropy maximizing model to estimate the crossing origin-destination flow matrix from in-out flows is given, a genetic algorithm is proposed to solve the model and the introduction of Lagrange multiplier is avoid. A practical example showed the validity of the genetic algorithm.

Sign in / Sign up

Export Citation Format

Share Document