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Zacharias B. Maroulis - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Objective Optimization of a trigeneration plant
    Energy Policy, 2010
    Co-Authors: K.c. Kavvadias, Zacharias B. Maroulis
    Abstract:

    A Multi-Objective Optimization method was developed for the design of trigeneration plants. The Optimization is carried out on technical, economical, energetic and environmental performance indicators in a Multi-Objective Optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a Multi-Objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices.

  • Multi-Objective Optimization of a trigeneration plant
    Energy Policy, 2010
    Co-Authors: K.c. Kavvadias, Zacharias B. Maroulis
    Abstract:

    A Multi-Objective Optimization method was developed for the design of trigeneration plants. The Optimization is carried out on technical, economical, energetic and environmental performance indicators in a Multi-Objective Optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a Multi-Objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices. ?? 2009 Elsevier Ltd. All rights reserved.

Anita Schobel - One of the best experts on this subject based on the ideXlab platform.

  • Minmax Robustness for Multi-Objective Optimization Problems
    European Journal of Operational Research, 2014
    Co-Authors: Matthias Ehrgott, Anita Schobel
    Abstract:

    In real-world applications of Optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust Optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e., realizations of the uncertain data. For single objective Optimization, several definitions of robustness have been thoroughly analyzed and robust Optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to Multi-Objective Optimization and call this extension robust efficiency for uncertain Multi-Objective Optimization problems. We use ingredients from robust (single objective) and (deterministic) Multi-Objective Optimization to gain insight into the new area of robust Multi-Objective Optimization. We analyze the new concept and discuss how robust solutions of Multi-Objective Optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) Multi-Objective Optimization. The new concepts are illustrated with some linear and quadratic programming instances.

  • Minmax robustness for Multi-Objective Optimization problems
    European Journal of Operational Research, 2014
    Co-Authors: Matthias Ehrgott, Jonas Ide, Anita Schobel
    Abstract:

    In real-world applications of Optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust Optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e.; realizations of the uncertain data. For single objective Optimization, several definitions of robustness have been thoroughly analyzed and robust Optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to Multi-Objective Optimization and call this extension robust efficiency for uncertain Multi-Objective Optimization problems. We use ingredients from robust (single objective) and (deterministic) Multi-Objective Optimization to gain insight into the new area of robust Multi-Objective Optimization. We analyze the new concept and discuss how robust solutions of Multi-Objective Optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) Multi-Objective Optimization. The new concepts are illustrated with some linear and quadratic programming instances. © 2014 Elsevier B.V. All rights reserved.

K.c. Kavvadias - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Objective Optimization of a trigeneration plant
    Energy Policy, 2010
    Co-Authors: K.c. Kavvadias, Zacharias B. Maroulis
    Abstract:

    A Multi-Objective Optimization method was developed for the design of trigeneration plants. The Optimization is carried out on technical, economical, energetic and environmental performance indicators in a Multi-Objective Optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a Multi-Objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices.

  • Multi-Objective Optimization of a trigeneration plant
    Energy Policy, 2010
    Co-Authors: K.c. Kavvadias, Zacharias B. Maroulis
    Abstract:

    A Multi-Objective Optimization method was developed for the design of trigeneration plants. The Optimization is carried out on technical, economical, energetic and environmental performance indicators in a Multi-Objective Optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a Multi-Objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices. ?? 2009 Elsevier Ltd. All rights reserved.

Matthias Ehrgott - One of the best experts on this subject based on the ideXlab platform.

  • Minmax Robustness for Multi-Objective Optimization Problems
    European Journal of Operational Research, 2014
    Co-Authors: Matthias Ehrgott, Anita Schobel
    Abstract:

    In real-world applications of Optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust Optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e., realizations of the uncertain data. For single objective Optimization, several definitions of robustness have been thoroughly analyzed and robust Optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to Multi-Objective Optimization and call this extension robust efficiency for uncertain Multi-Objective Optimization problems. We use ingredients from robust (single objective) and (deterministic) Multi-Objective Optimization to gain insight into the new area of robust Multi-Objective Optimization. We analyze the new concept and discuss how robust solutions of Multi-Objective Optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) Multi-Objective Optimization. The new concepts are illustrated with some linear and quadratic programming instances.

  • Minmax robustness for Multi-Objective Optimization problems
    European Journal of Operational Research, 2014
    Co-Authors: Matthias Ehrgott, Jonas Ide, Anita Schobel
    Abstract:

    In real-world applications of Optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust Optimization, the aim of which is to find solutions which remain feasible and of good quality for all possible scenarios, i.e.; realizations of the uncertain data. For single objective Optimization, several definitions of robustness have been thoroughly analyzed and robust Optimization methods have been developed. In this paper, we extend the concept of minmax robustness (Ben-Tal, Ghaoui, & Nemirovski, 2009) to Multi-Objective Optimization and call this extension robust efficiency for uncertain Multi-Objective Optimization problems. We use ingredients from robust (single objective) and (deterministic) Multi-Objective Optimization to gain insight into the new area of robust Multi-Objective Optimization. We analyze the new concept and discuss how robust solutions of Multi-Objective Optimization problems may be computed. To this end, we use techniques from both robust (single objective) and (deterministic) Multi-Objective Optimization. The new concepts are illustrated with some linear and quadratic programming instances. © 2014 Elsevier B.V. All rights reserved.

Kaushik Deb - One of the best experts on this subject based on the ideXlab platform.

  • Introducing Robustness in Multi-Objective Optimization
    Evolutionary Computation, 2006
    Co-Authors: Kaushik Deb, Himanshu Gupta
    Abstract:

    In Optimization studies including Multi-Objective Optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, represent-ing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solu-tions are quite sensitive to the variable perturbations which cannot be avoided in prac-tice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust Optimization is dealt with in detail in single-objective evolutionary Optimization studies, in this paper, we present two different robust Multi-Objective Optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective Optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust Multi-Objective Optimization principles and the differences between the two robust Optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary Multi-Objective op-timization (EMO) algorithm. Finally, we also apply both robust Optimization method-ologies to an engineering design problem.

  • Multi-Objective Optimization
    Multi-objective optimization using evolutionary algorithms, 2001
    Co-Authors: Kaushik Deb, Kalyanmoy Deb, Karthik Sindhya, Jussi Hakanen
    Abstract:

    In this chapter, we introduce Multi-Objective Optimization, and recall some of the most relevant research articles that have appeared in the international litera-ture related to these topics. The presented state-of-the-art does not have the purpose of being exhaustive; it aims to drive the reader to the main problems and the ap-proaches to solve them. 2.1 Multi-Objective Management The choice of a route at a planning level can be done taking into account time, length, but also parking or maintenance facilities. As far as advisory or, more in general, automation procedures to support this choice are concerned, the available tools are basically based on the " shortest-path problem " . Indeed, the problem to find the single-objective shortest path from an origin to a destination in a network is one of the most classical Optimization problems in transportation and logistic, and has deserved a great deal of attention from researchers worldwide. However, the need to face real applications renders the hypothesis of a single-objective function to be optimized subject to a set of constraints no longer suitable, and the introduction of a Multi-Objective Optimization framework allows one to manage more informa-tion. Indeed, if for instance we consider the problem to route hazardous materials in a road network (see, e.g., Erkut et al., 2007), defining a single-objective function problem will involve, separately, the distance, the risk for the population, and the transportation costs. If we regard the problem from different points of view, i.e., in terms of social needs for a safe transshipment, or in terms of economic issues or pol-11