The Experts below are selected from a list of 315570 Experts worldwide ranked by ideXlab platform
Wen-hua Chen - One of the best experts on this subject based on the ideXlab platform.
-
Receding Horizon Control for Airport Capacity Management
IEEE Transactions on Control Systems Technology, 2007Co-Authors: Wen-hua ChenAbstract:A major goal of air traffic Management is to strategically control the flow of traffic so that the demand at an airport meets but does not exceed the operational Capacity in a dynamic environment. This paper uses the concept of receding horizon control (RHC) to conduct real-time planning for airport Capacity Management (ACM). It is shown that RHC provides a generic and flexible framework for developing real-time allocation algorithms for airport Capacity in a dynamic and uncertain environment, and existing approaches such as the one step ahead adjustment can be considered as special cases of this approach. Robustness against the change of the environment and demands and computational efficiency are two advantages when applying RHC to the ACM problem, which are illustrated by a case study.
-
Multiairport Capacity Management: Genetic Algorithm With Receding Horizon
IEEE Transactions on Intelligent Transportation Systems, 2007Co-Authors: Wen-hua Chen, E. Di PaoloAbstract:The inability of airport Capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport Capacity Management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment
E. Di Paolo - One of the best experts on this subject based on the ideXlab platform.
-
Multiairport Capacity Management: Genetic Algorithm With Receding Horizon
IEEE Transactions on Intelligent Transportation Systems, 2007Co-Authors: Wen-hua Chen, E. Di PaoloAbstract:The inability of airport Capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport Capacity Management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment
Svetlana Rodgers - One of the best experts on this subject based on the ideXlab platform.
-
Capacity Management for hospitality and tourism: A review of current approaches
International Journal of Hospitality Management, 2010Co-Authors: Madeleine E. Pullman, Svetlana RodgersAbstract:Abstract In this article we provide a review of current Capacity Management approaches applicable to hospitality and tourism enterprises. While many of the traditional methodologies involve closed-form analytical calculations, newer methods have been developed that address more complex problems. These more recent methods include the creative use of mixed models such as those that integrate visitor-preference and operations-research models, linear programming, and simulation models. We consider the full range of methodologies, the advantages and disadvantages, and potential applications to both physical and human Capacity estimation. We highlight the ways that various leisure industries can benefit from different approaches to Capacity Management.
A. Galip Ulsoy - One of the best experts on this subject based on the ideXlab platform.
-
Stochastic Optimal Capacity Management in Reconfigurable Manufacturing Systems
CIRP Annals, 2003Co-Authors: Farshid Maghami Asl, A. Galip UlsoyAbstract:Abstract This paper presents an optimal policy, based on Markov decision theory for the Capacity Management problem in a firm facing stochastic market demand. The firm implements a reconfigurable manufacturing system and faces a delay between the times Capacity changes are ordered and the times they are delivered. Optimal policies are presented as optimal boundaries representing the optimal Capacity Expansion and reduction levels. TO increase the robustness of the optimal policy to unexpected events, the concept of feedback control is applied to address the Capacity Management problem. It is shown that feedback provides sub-optimal solutions to the Capacity Management problem which are more robust under unexpected disturbances in market demand and unexpected events.
-
Optimal Capacity Management With Stochastic Market Demand and Imperfect Information
Dynamic Systems and Control Volumes 1 and 2, 2003Co-Authors: Farshid Maghami Asl, A. Galip UlsoyAbstract:Over-Capacity has been a major problem in the world economy over the past decade. Reconfigurable Capacity, and optimal Capacity Management policies, can contribute to increased economic stability. This research introduces a new approach to optimal Capacity Management for a firm faced with uncertainties and imperfect information of the market demand. It presents an optimal policy for the Capacity Management problem in a firm facing stochastic market demand, based on Markov decision theory. To make the approach more realistic, it is assumed that the firm has imperfect information of its stochastic market demand, and can only observe its previous sales. Optimal policies are presented as boundaries representing the optimal Capacity expansion and reduction levels.Copyright © 2003 by ASME
-
Capacity Management in Reconfigurable Manufacturing Systems With Stochastic Market Demand
Manufacturing, 2002Co-Authors: Farshid Maghami Asl, A. Galip UlsoyAbstract:An optimal solution, based on Markov Decision Theory, is presented for the Capacity Management problem in Reconfigurable Manufacturing Systems with stochastic market demand with a time delay between the time Capacity change is ordered and the time it is delivered. The optimal policy in this paper is presented as optimal boundaries representing the optimal Capacity expansion and reduction levels. The effects of change in the cost function parameters and the delay time on the optimal boundaries are presented for a Capacity Management scenario. The major differences between this research and the ones in inventory control lie in two folds. One is the fact that unlike inventory, Capacity levels can be reduced according to the market demand. The other one is the novel approach presented in this paper to solve the delay problem which unlike the inventory control does not account for the cumulative unmet demand as a decision factor.
Gerald Reiner - One of the best experts on this subject based on the ideXlab platform.
-
Performance improvement of supply chain processes by coordinated inventory and Capacity Management
International Journal of Production Economics, 2006Co-Authors: Werner Jammernegg, Gerald ReinerAbstract:Abstract This study discusses the opportunities and challenges for improving the performance of supply chain processes by coordinated application of inventory Management and Capacity Management. We illustrate our approach by a supplier company in the telecommunication and automotive industry (tier 2), where a manufacturer (production facility) is located in a country with low labor costs and high worker deployment flexibility. Using process simulation, we demonstrate how the coordinated application of methods from inventory Management and Capacity Management result in improved performance measures of both intraorganizational (costs) and interorganizational (service level) objectives.
