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This study is devoted to a new research topic in real-time airline operations, the redispatching of unit load devices (ULDs) under demand variations. We develop a new hybrid framework to solve the problem of ULD redispatch following the time-sequence decision-making required by airlines. The hybrid framework is developed by integrating techniques including the probability distribution technique to simulate different types of operational demand, the adjustable number of stages which is needed to meet the requirements of a decision-making process following a time sequence and the time pressure characteristic of real operations, and the scenario tree and probability rule approaches which are aimed and representing all possible demand scenarios for a stage, while the network flow technique is applied to represent the movement and location of ULDs at each airport over time and is used for the development of the associated mathematical model and the simulation. We performed a simulation of 2,000 cases based on different operational days and types of operational demand. The results show that this hybrid framework is able to achieve stability and also a small variability of both ULD operating costs and solution times, which could allow the airline to save on ULD operating costs, under demand variations in real-time operations. [ABSTRACT FROM AUTHOR]

This study considers the planned gate and runway assignment problem for minimizing carbon emissions and costs during the aircraft taxiing stage. The distances between multiple gates and runways, the fuel consumption of different types of aircraft, the location of terminal buildings belonging to the airline, the size of parking aprons for aircraft, and runway lengths for different types of aircraft are all considered. We propose the following two objectives: the minimization of carbon emissions and the minimization of carbon emission costs for airport and for airlines, respectively. A case study based on operations of Taiwan's Taoyuan International Airport is performed. Analytical results indicate that our assignments can achieve an 8.93% reduction of carbon emissions and 10.33% reduction of carbon emission costs compared with the current assignments. [ABSTRACT FROM AUTHOR]

This paper addresses a competitive airline hub-and-spoke network design problem in a duopolistic market with sequential airline entry. Two airlines, a leader and a follower, enter the market sequentially to make decisions about their own flight networks. Each must consider their opponent's reactions and respond to each other's decisions in making their own, leading to interactive competition between them. A bi-level model is proposed to formulate the behaviors and decisions of the two airlines. In addition, an interactive solution procedure based on a genetic algorithm (GA) is developed to repeatedly solve the upper and lower level models. Finally, we generate a numerical example based on the regional air market in Taiwan and China. The results provide useful insights and highlight the importance of considering interactive competition for airlines when designing their flight networks. [ABSTRACT FROM AUTHOR]

In this research, we consider a flight scheduling problem for oligopolistic competition with direct flights and a point to point network. In this type of market situation, passengers are sensitive to the departure time of a flight rather than the transfer time. The airline needs to carefully consider the departure times of their competitors when determining their own. Therefore, unlike past approaches which have only considered one departure time for a competitor's flight, a flight scheduling framework is developed which takes into consideration possible competitor departure times. The framework includes two dependent stages which are repeatedly solved during the solution process. In addition, an upper bound model is also designed to evaluate the solution quality. Numerical tests are performed using data for Taiwan's outlying island route which is characterized by the above market situation. Satisfactory results are obtained, showing the good performance of the framework. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• A ULD sharing model to perform sharing economy in airline industry. • The achievement rate to represent likelihood of applicability of ULD dispatch. • Combining network technique in OR and binomial distribution in Statistics. • Confidence interval estimate and chance constraint techniques used in modeling. • An iterative evolutionary process to develop a three stage solution algorithm. The skypooling is a free platform for the sharing of unit load devices (ULD), which contributes a new sharing economy by allowing airlines to exchange ULDs and coordinate ULD delivery services. The akypooling aids airlines to match their requirements for the two ULD sharing operations. However, such matching is not always successful. The matching success rate naturally affects skypooling performance and generates a gap between the expected and available numbers of ULDs for the two ULD sharing operations. We design two achievement rates for ULD sharing operations, which represent the likelihood of meeting ULD dispatch requirements in actual operations, as well as devising. We develop a novel ULD sharing model and a solution algorithm to handle the problem. The model is developed by combining the network technique utilized in Operations Research with the binomial distribution utilized in Statistics. The solution algorithm comprises a three-stage iterative evolutionary process. We obtain that the carrier decrease the number of ULDs utilized each week by 7% by the two ULD sharing operations. Different instances exemplifying optimistic, normal, and pessimistic situations are examined to understand the ultimate implications and applications of the new technique for airline operations. [ABSTRACT FROM AUTHOR]

SUMMARY This work presents an analysis of the fleet size determination problem for a truckload distribution center in which each customer requests multi-truckload services. A transformation is made to define this problem as a cutting stock problem which can be resolved by finding the proper set of patterns beneficial for an NP-hard (non-deterministic polynomial-time hard) problem. The genetic algorithm (GA) is combined with cutting stock techniques to solve the problem. Experiments are carried out testing two types of problems, with and without time-window constraints. To verify the solution quality obtained with the proposed approach, the problem is also solved using only GA. It is shown that the proposed approach can significantly improve the GA solution by 14.68%. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]