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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]

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]

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]