Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem

2015 ◽  
Vol 78 ◽  
pp. 238-259 ◽  
Author(s):  
Zhe Liang ◽  
Yuan Feng ◽  
Xiaoning Zhang ◽  
Tao Wu ◽  
Wanpracha Art Chaovalitwongse
Keyword(s):  
Assignment Problem ◽  
Aircraft Maintenance ◽  
Routing Problem

Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations

2017 ◽  
Vol 117 (10) ◽  
pp. 2142-2170 ◽  
Author(s):  
Abdelrahman E.E. Eltoukhy ◽  
Felix T.S. Chan ◽  
S.H. Chung ◽  
Ben Niu ◽  
X.P. Wang
Keyword(s):  
Airline Industry ◽  
Solution Algorithm ◽  
The Other ◽  
Computational Time ◽  
Aircraft Maintenance ◽  
Solution Quality ◽  
Routing Problem ◽  
Content Type ◽  
Other Hand ◽  
Availability Constraints

Purpose The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry. Design/methodology/approach Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit. Findings The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model. Research limitations/implications The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models. Practical implications The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large. Originality/value In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

A Multi-Agent Taxi Dispatching System

2010 ◽  
Vol 2 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Mahdi Hasheminezhad ◽  
Ardeshir Bahreininejad
Keyword(s):  
Resource Allocation ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Assignment Problem ◽  
Dynamic Problem ◽  
Routing Problem ◽  
Agent Based ◽  
Multi Agent

The taxi assignment problem may be categorized as a vehicle routing problem. ?When placed in the field of resource allocation, it is a dynamic problem in which ?the situation changes as the work progresses. This paper presents a new agent-based approach to tackle the taxi assignment problem. New parameters are ?introduced to increase the satisfaction of the drivers. The authors propose a new algorithm to improve the parameters. Simulations were also conducted to examine the efficiency of the proposed method. The results indicate the effectiveness of the proposed taxi assignment/dispatching approach.

scholarly journals A clustering algorithm for solving the vehicle routing assignment problem in polynomial time

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
L. W. Rizkallah ◽  
M. F. Ahmed ◽  
N. M. Darwish
Keyword(s):  
Vehicle Routing ◽  
Polynomial Time ◽  
Vehicle Routing Problem ◽  
Assignment Problem ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Number Of Clusters ◽  
Routing Problem ◽  
Major Increase ◽  
Optimal Number Of Clusters

The Vehicle Routing Problem (VRP) consists of a group of customers that needs to be served. Each customer has a certain demand of goods. A central depot having a fleet of vehicles is responsible for supplying the customers with their demands. The problem is composed of two sub-problems: The first sub-problem is an assignment problem where both the vehicles that will be used as well as the customers assigned to each vehicle are determined. The second sub-problem is the routing problem in which for each vehicle having a number of cus-tomers assigned to it, the order of visits of the customers is determined. Optimal number of vehicles as well as optimal total distance should be achieved. In this paper, an approach for solving the first sub-problem, the assignment problem, is presented. In the approach, a clustering algorithm is proposed for finding the optimal number of vehicles by grouping the customers into clusters where each cluster is visited by one vehicle. This work presents a polynomial time clustering algorithm for finding the optimal number of clusters. Also, a solution to the assignment problem is provided. The proposed approach was evaluated using Solomon’s C1 benchmarks where it reached optimal number of clusters for all the benchmarks in this category. The proposed approach succeeds in solving the assignment problem in VRP achieving a solving time that surpasses the state-of-the-art approaches provided in the literature. It also provides a means of working with varying num-ber of customers without major increase in solving time.  

Applications of Computational Intelligence to Impairment-Aware Routing and Wavelength Assignment in Optical Networks

2013 ◽  
pp. 194-216
Author(s):  
Joaquim F. Martins-Filho ◽  
Carmelo J. A. Bastos-Filho ◽  
Daniel A. R. Chaves ◽  
Helder A. Pereira
Keyword(s):  
Optical Networks ◽  
Computational Intelligence ◽  
Assignment Problem ◽  
Routing And Wavelength Assignment ◽  
Wavelength Assignment ◽  
Evolutionary Strategy ◽  
Wavelength Converters ◽  
Routing Problem ◽  
Hard Problems ◽  
Logical Topology

Computational intelligence techniques have been used to solve hard problems in optical networks, such as the routing and wavelength assignment problem, the design of the physical and the logical topology of these networks, and the placement of some high cost devices along the network when it is necessary, such as regenerators and wavelength converters. In this chapter, the authors concentrate on the application of computational intelligence to solve the impairment-aware routing and wavelength assignment problem. They present a brief survey on this topic and a detailed description and results for two applications of computational intelligence, one to solve the wavelength assignment problem with an evolutionary strategy approach and the other to tackle the routing problem using ant colony optimization.

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