scholarly journals Optimization Model and Algorithm Design for Airline Fleet Planning in a Multiairline Competitive Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Wang ◽  
Hong Sun ◽  
Jinfu Zhu ◽  
Bo Zhu
Keyword(s):  
Heuristic Algorithm ◽  
Planning Process ◽  
Programming Model ◽  
Algorithm Design ◽  
Fleet Size ◽  
Fleet Planning ◽  
Flight Frequency ◽  
Multiobjective Mathematical Programming ◽  
Airline Fleet ◽  
Size And Structure

This paper presents a multiobjective mathematical programming model to optimize airline fleet size and structure with consideration of several critical factors severely affecting the fleet planning process. The main purpose of this paper is to reveal how multiairline competitive behaviors impact airline fleet size and structure by enhancing the existing route-based fleet planning model with consideration of the interaction between market share and flight frequency and also by applying the concept of equilibrium optimum to design heuristic algorithm for solving the model. Through case study and comparison, the heuristic algorithm is proved to be effective. By using the algorithm presented in this paper, the fleet operational profit is significantly increased compared with the use of the existing route-based model. Sensitivity analysis suggests that the fleet size and structure are more sensitive to the increase of fare price than to the increase of passenger demand.

Scheduling a Bus Fleet for Evacuation Planning Using Stop-Skipping Method

2021 ◽  
pp. 036119812110200
Author(s):  
Hu Zhao ◽  
Shumin Feng ◽  
Yusheng Ci
Keyword(s):  
Waiting Time ◽  
Ant Colony Algorithm ◽  
Programming Model ◽  
Peer To Peer ◽  
Mathematical Programming Model ◽  
Fleet Size ◽  
Bus Stop ◽  
Passenger Demand ◽  
Service Strategies ◽  
Bus System

Sudden passenger demand at a bus stop can lead to numerous passengers gathering at the stop, which can affect bus system operation. Bus system operators often deal with this problem by adopting peer-to-peer service, where empty buses are added to the fleet and dispatched directly to the stop where passengers are gathered (PG-stop). However, with this strategy, passengers at the PG-stop have a long waiting time to board a bus. Thus, this paper proposes a novel mathematical programming model to reduce the passenger waiting time at a bus stop. A more complete stop-skipping model that including four cases for passengers’ waiting time at bus stops is proposed in this study. The stop-skipping decision and fleet size are modeled as a dynamic program to obtain the optimal strategy that minimizes the passenger waiting time, and the optimization model is solved with an improved ant colony algorithm. The proposed strategy was implemented on a bus line in Harbin, China. The results show that, during the evacuation, using the stop-skipping strategy not only reduced the total waiting time for passengers but also decreased the proportion of passengers with a long waiting time (>6 min) at the stops. Compared with the habitual and peer-to-peer service strategies, the total waiting time for passengers is reduced by 31% and 23%, respectively. Additionally, the proportion of passengers with longer waiting time dropped to 43.19% by adopting the stop-skipping strategy, compared with 72.68% with the habitual strategy and 47.5% with the peer-to-peer service strategy.

PMP-Based Optimization Method for Determining Airline Fleet Composition

2014 ◽  
Vol 505-506 ◽  
pp. 645-649
Author(s):  
Yu Wang
Keyword(s):  
Network Effects ◽  
Optimization Method ◽  
Numerical Example ◽  
Fleet Planning ◽  
Decision Variables ◽  
Proposed Model ◽  
Fleet Composition ◽  
Aircraft Type ◽  
Airline Fleet ◽  
The Impact

Traditional methods for determining airline fleet composition could not reflect the impact of network effects on fleet composition. To solve this problem for airlines operating in the mode of Hub & Spoke network, the passenger mix problem was incorporated into the model of determining airline fleet composition. The purchasing number of aircrafts in each fleet type, the frequencies of each aircraft type flying on legs and the spilling number of passengers from each itinerary were treated as decision variables. The limitations including maximum flying frequencies on each leg, available flying time each fleet type can provide and maximum passengers spilled from each flight leg were considered as constraints. A model to minimize the fleet planning cost was constructed. The numerical example shows that the fleet planning cost derived from this proposed model is 46266381.64 Yuan and reduces by 3914969.70 Yuan compared to the result from the traditional leg-based model. In hence, this proposed model is effective and feasible.

Portfolio-based airline fleet planning under stochastic demand

Omega ◽  
2020 ◽  
Vol 97 ◽  
pp. 102101
Author(s):  
Constantijn A.A. Sa ◽  
Bruno F. Santos ◽  
John-Paul B. Clarke
Keyword(s):  
Stochastic Demand ◽  
Fleet Planning ◽  
Airline Fleet

scholarly journals A Tabu Search-Based Algorithm for Airport Gate Assignment: A Case Study in Kunming, China

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jun Bi ◽  
Zhen Wu ◽  
Lei Wang ◽  
Dongfan Xie ◽  
Xiaomei Zhao
Keyword(s):  
Tabu Search ◽  
Dynamic Scheduling ◽  
Programming Model ◽  
Algorithm Design ◽  
Neighborhood Structure ◽  
Important Place ◽  
Passenger Satisfaction ◽  
Actual Operation ◽  
Gate Assignment

An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is the prerequisite for other related dispatch. Effective and reasonable allocation of gates can reduce airport operating costs and increase passenger satisfaction. Therefore, an airport gate assignment problem (AGAP) needs to be urgently solved in the actual operation of the airport. In this paper, considering the actual operation of the airport, we formulate an integer programming model for AGAP by considering multiple constraints. The model aims to maximize the number of passengers on flights parked at the gate. A tabu search-based algorithm is designed to solve the problem. In the process of algorithm design, an effective initial solution is obtained. A unique neighborhood structure and search strategy for tabu search are designed. The algorithm can adapt to the dynamic scheduling of airports. Finally, tests are performed using actual airport data selected from Kunming Changshui International Airport in China. The experimental results indicate that the proposed method can enhance the local search ability and global search ability and get satisfactory results in a limited time. These results provide an effective support for the actual gate assignment in airport operations.

Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

2020 ◽  
Vol 2674 (11) ◽  
pp. 532-541 ◽  
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder ◽  
Andreas Rau
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Public Transit ◽  
Programming Model ◽  
Real Life ◽  
Single Line ◽  
Continuum Approximation ◽  
Transit System ◽  
Fleet Size ◽  
Minimum Number

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

Cross Warehouse Scheduling of Logistics Distribution and Cycle Re-Claimer Based on Heuristics Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2606-2610 ◽  
Author(s):  
Rong Hua Ma
Keyword(s):  
Heuristic Algorithm ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Simulation Method ◽  
New Method ◽  
Logistics Distribution ◽  
Suboptimal Solution ◽  
Taboo Search Algorithm ◽  
Heuristics Algorithm

In the uncertain environment, the cycle re-claimer and logistics distribution cross warehouse scheduling is important and it should be optimized. The mathematical programming model is constructed, and the proposed two-stage heuristic algorithm is proposed, the optimal solution of heuristic algorithm is used as the initial value. And the taboo search algorithm is designed to improve the initial solution. In order to verify the availability of the method, the Monte Carlo simulation method is used for numerical experiment. The experiment results show that the new method can solve the suboptimal solution which close to the optimal solution in fast, and the taboo searching algorithm can improve the solution of heuristic algorithm, it has significantly improvement performance for new method, and it has good application value in practice.

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