Zugl.: Kaiserslautern, Techn. Univ., Diss., 2008

Diss. TU Delft, 2006.

Bachelor-Arbeit Univ. St. Gallen, 2008.

Universiẗat, Diss., 2005--Karlsruhe.

Univ., FB Wirtschaftswiss., Diss.--Hamburg, 2001. / Literaturverz. S. [193] - 208.

Universiẗat, Diss, 2002--Paderborn.

Transportation is one of the most vital services in modern society. It makes most of the other functions of society possible. Real transportation systems are so large and complex that in order to build the science of transportation systems it will be necessary to work in many areas, such as: Modeling, Optimization and Simulation. We are interested in solutions for the so-called fleet-sizing-and-allocation problem (FSAP). Fleet sizing and allocation problems are one of the most interesting and hard to solve logistic problems. A fleet sizing and allocation problem consists of two interdependent parts. The fleet sizing problem is to determine a number of transportation units that optimally balances service requirements against the cost of purchasing and maintaining the transportation units. The allocation problem is dealing with the repositioning of transportation units to serve future transportation demand. To make the fleet sizing and allocation problem a little bit more tractable we concentrate on logistic systems with a special hub-and-spoke structure. We start with a very simple fleet sizing of one-to-one case. This case will cause us to focus attention on several key issues in fleet sizing. Afterwards, the generalization of the one-to-one system is the one-to-many system. As a simple example can serve the continuous time situation where a single origin delivers items to many destinations. For the case that items are produced in a deterministic production cycle and transportation times are stochastic. We also studied a hub-and-spoke problem with continuous time and stochastic demand. To solve this problem, based on Marginal Analysis, we applied queueing theory methods. The investigation of the fleet-sizing-and-allocation problem for hub-and-spoke systems is started for a single-period, deterministic-demand model. In that the model hub has to decide how to use a given number of TU’s to satisfy a known (deterministic) demand in the spokes. We consider two cases: 1. Renting of additional TU’s from outside the system is not possible, 2. Renting of additional TU’s from outside the system is possible. For each case, based on Marginal Analysis, we developed a simple algorithm, which gives us the cost-minimal allocation. Since the multi-period, deterministic demand problem is NP-hard we suggest to use Genetic Algorithms. Some building elements for these are described. For the most general situation we also suggest to use simulation optimization. To realize the simulation optimization approach we could use the software tool “Calculation Assessment Optimization System” (CAOS). The idea of CAOS is to provide a software system, which separates the optimization process from the optimization problem. To solve an optimization problem the user of CAOS has to build up a model of the system to which the problem is related. Furthermore he has to define the decision parameters and their domain. Finally, we used CAOS for two classes of hub-and-spoke system: 1. A single hub with four spokes, 2. A single hub with fifty spokes. We applied four optimizers – a Genetic Algorithm, Tabu Search, Hybrid Parallel and Hybrid Serial with two distributions (Normal Distribution and Exponential Distribution) for a customer interarrival times and their demand.

Univ., Diss.--Paderborn, 2002.

After the complete liberalization of the airline industry during the 1990s the industry has faced a rapid growth in passenger numbers. This has mainly been caused by the emergence of so-called Low Cost Carrier (LCC) that offer a simplified product (i.e. point-to-point flights without any frills) at a lower cost than traditional Network Carriers. Furthermore LCC also introduced a less differentiated pricing structure (Restriction Free Pricing) which forced competing network carriers to reduce the degree of price discrimination which they were able to practice until then in order to defend their market shares. This has led to a decrease of average yields, which resulted in difficulties for (smaller) Network Carriers to cover their fixed costs, related to the operation of a hub & spoke network. In this environment network airlines are looking for new revenue sources as well as further sources of cost reduction. This development has amplified the consolidation trend of the airline industry and led to the emergence of several multi-hub networks (e.g. Lufthansa runs hub-operation in Frankfurt, Munich, Zurich and Vienna). One way to leverage the fact that multi-hub networks allow several routings for one origin-destination city pair would be the introduction of flexible tickets, where the actual routing of the passenger is not defined at the moment of purchase but only a certain time prior to departure. This allows airlines to raise the load factor on their network by increasing the degree of overbooking which they currently practice by pooling the risk that more passengers arrive than there is capacity among several flights. Furthermore these tickets might allow network carriers to compete in the low-cost-airline segment without having to further reduce the price level of their regular product (with specified routing). The present dissertation examined possible designs of such a ticket and their impact on the acceptance by passengers by means of a choice based conjoint study among 356 travelers. The findings suggest that while 77.5% of leisure travelers are willing to accept flexible time-range tickets in their relevant set, only 56% of business travelers are considering using this kind of ticket. More particular the results also showed that business travelers are not willing to compromise on travel duration and departure times, and are subsequently willing to pay a premium for specified tickets. A market share simulation showed that depending on the selected product layout flexible time-range tickets are able to gain up to 60% market share when offered at a discount of up to 33% relative to traditional tickets. When it comes to the actual layout, the largest lever to increase the acceptance is to exclude connection flights from the potential set of flights. The results contribute to the young research area on flexible products by assessing the disutility which is experienced by customers with regard to particular product characteristics of flexible products. Furthermore the results aim at providing airline managers with a comprehensive overview of the possibilities which flexible time-range tickets bring along when it comes to increasing the load factor and thereby the revenues in a multi-hub network. (author's abstract)

Transportation is one of the most vital services in modern society. It makes most of the other functions of society possible. Real transportation systems are so large and complex that in order to build the science of transportation systems it will be necessary to work in many areas, such as: Modeling, Optimization and Simulation. We are interested in solutions for the so-called fleet-sizing-and-allocation problem (FSAP). Fleet sizing and allocation problems are one of the most interesting and hard to solve logistic problems. A fleet sizing and allocation problem consists of two interdependent parts. The fleet sizing problem is to determine a number of transportation units that optimally balances service requirements against the cost of purchasing and maintaining the transportation units. The allocation problem is dealing with the repositioning of transportation units to serve future transportation demand. To make the fleet sizing and allocation problem a little bit more tractable we concentrate on logistic systems with a special hub-and-spoke structure. We start with a very simple fleet sizing of one-to-one case. This case will cause us to focus attention on several key issues in fleet sizing. Afterwards, the generalization of the one-to-one system is the one-to-many system. As a simple example can serve the continuous time situation where a single origin delivers items to many destinations. For the case that items are produced in a deterministic production cycle and transportation times are stochastic. We also studied a hub-and-spoke problem with continuous time and stochastic demand. To solve this problem, based on Marginal Analysis, we applied queueing theory methods. The investigation of the fleet-sizing-and-allocation problem for hub-and-spoke systems is started for a single-period, deterministic-demand model. In that the model hub has to decide how to use a given number of TU’s to satisfy a known (deterministic) demand in the spokes. We consider two cases: 1. Renting of additional TU’s from outside the system is not possible, 2. Renting of additional TU’s from outside the system is possible. For each case, based on Marginal Analysis, we developed a simple algorithm, which gives us the cost-minimal allocation. Since the multi-period, deterministic demand problem is NP-hard we suggest to use Genetic Algorithms. Some building elements for these are described. For the most general situation we also suggest to use simulation optimization. To realize the simulation optimization approach we could use the software tool “Calculation Assessment Optimization System” (CAOS). The idea of CAOS is to provide a software system, which separates the optimization process from the optimization problem. To solve an optimization problem the user of CAOS has to build up a model of the system to which the problem is related. Furthermore he has to define the decision parameters and their domain. Finally, we used CAOS for two classes of hub-and-spoke system: 1. A single hub with four spokes, 2. A single hub with fifty spokes. We applied four optimizers – a Genetic Algorithm, Tabu Search, Hybrid Parallel and Hybrid Serial with two distributions (Normal Distribution and Exponential Distribution) for a customer interarrival times and their demand.