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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]

SUMMARY It is often the case in actual airport operations that flight departure/arrival information will vary with time. In practice, flight departure/arrival times closer to the time when the airport authority starts to plan the reassignments tend to be more certain; those further away tend to be more stochastic. These two types of flights can be called deterministic flights and stochastic flights, respectively. A deterministic flight has a certain departure/arrival time; while a stochastic flight will have a variety of stochastic departure/arrival times. In this study the aim is to develop a gate reassignment model (GRM) designed to consider both deterministic and stochastic flight departure/arrival times. A 0-1 integer programming technique is applied to formulate the GRM. In practice gate reassignments need to be handled repeatedly, so to make this possible the GRM is applied to a dynamic gate reassignment framework (DGRF). The theoretical effectiveness of the GRM applied to the DGRF is evaluated by the development of a lower bound solution. Numerical tests, related to the operations of an international Taiwan airport, show that the proposed GRM and DGRF perform well. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The passenger’s perception of the airport’s level of service (LOS) may have a significant impact on promoting or discouraging future tourism and business activities. In this study, we take a look at this problem, but unlike in traditional statistical analysis, we apply a new method, the dominance-based rough set approach (DRSA), to an airport service survey. A set of “if … then …” decision rules is used in the preference model. The passengers indicate their perception of airport LOS by rating a set of criteria/attributes. The proposed method provides practical information that should be of help to airport planners, designers, operators, and managers to develop LOS improvement strategies. The model was implemented using survey data from a large sample of customers from an international airport in Taiwan. [Copyright &y& Elsevier]

Abstract: In this study, we develop a solution method for solving air express cargo loading problems under stochastic demands. The method is designed by combining the scenario decomposition and genetic algorithm (GA) techniques. Numerical tests are performed to evaluate the performance of the solution method using data for the Asian operations of an international express carrier. The results show that the solutions obtained from our method are very close to the optimal solutions. Furthermore, our method has an advantage in terms of computation time over the CPLEX optimization algorithm. [Copyright &y& Elsevier]

Temporary gate shortages and stochastic flight delays (including late departures or arrivals and early arrivals) are common problems that often occur simultaneously in real-time airport operations. The aim of this paper is to design a gate reassignment model to deal with temporary gate shortages and stochastic flight delays. This is done by allowing the violation of gate reassignment constraints in the model. A numerical test was carried out, based on data from the operations of the Taiwan Taoyuan Airport (a major international airport in Taiwan), to demonstrate the performance of the proposed model. A comparison of the proposed model and a traditional reassignment model, as well as major findings related to the test results, are also discussed and presented. [ABSTRACT FROM AUTHOR]

Abstract: In this research, we develop an integrated scheduling model that combines passenger, cargo and combi flight scheduling. We employ network flow techniques to construct the model which is formulated as an integer multiple commodity network flow problem that is characterized as NP-hard. A family of heuristics, based on Lagrangian relaxation, a sub-gradient method, heuristics for the upper bound solution, and a flow decomposition algorithm, is developed to solve the model. The test results, mainly using data from a major Taiwan airline’s operations, show the good performance of the model and the solution algorithms. [Copyright &y& Elsevier]

• 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]

Abstract: Vehicle fleet routing and timetable setting are essential to the enhancement of an inter-city bus carrier’s operating cost, profit, level of service and competitiveness in the market. In past research the average passenger demand has usually served as input in the production of the final fleet routes and timetables, meaning that stochastic disturbances arising from variations in daily passenger demand in actual operations are neglected. To incorporate the stochastic disturbances of daily passenger demands that occur in actual operations, in this research, we established a stochastic-demand scheduling model. We applied a simulation technique, coupled with link-based and path-based routing strategies, to develop two heuristic algorithms to solve the model. To evaluate the performance of the proposed model and the two solution algorithms, we developed an evaluation method. The test results, regarding a major Taiwan inter-city bus operation, were good, showing that the model and the solution algorithms could be useful in practice. [Copyright &y& Elsevier]