Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

Multiple Criteria Analysis of the Airport Terminal Effectiveness by Multi-objective Optimization and Simulation

[[abstract]]A rational approach to terminal airport management is not a trivial task due to relatively complex interactions between passengers and terminal infrastructure. Such infrastructure may be represented or modelled as a network of service nodes. To make a decision about such a network structure, one has to take into account not only the cost of terminal infrastructure, but also a set of quality indicators depicting passenger service level. Such decision problems may be formulated in the multiple criteria setting. We propose a bi-criteria decision making problem with a discrete-event simulation model of a terminal airport as a base model. The simulation model is used to evaluate a finite set of configurations representing a network of service nodes. To point out the most preferred Pareto optimal configuration, we propose to use an interactive decision making method to navigate Pareto optimal solutions with so-called vectors of concessions and reference points as preference carriers. Such versatile decision making scheme may be used to solve practical multiple criteria decision problems with values of criteria functions obtained by simulation runs

Evolutionary Multiobjective Optimization for Intensity Modulated Radiation Therapy

As cancer diseases take nowadays a heavy toll on societies worldwide, extensive research is being conducted to provide more accurate diagnoses and more effective treatments. In particular, Multiobjective Optimization has turned out to be an appropriate and efficient framework for timely and accurate radiotherapy planning. In the paper, we sketch briefly the background of Multiobjective Optimization research to Intensity Modulated Radiation Therapy, and next we present a rudimentary formulation of the problem. We also present a generic methodology we developed for Multiple Criteria Decision Making, and we present preliminary results with it when applied to radiation treatment planning

Decision Maker's Preferences, Airport Gate Assignment Problem and Multiobjective Optimisation

We present an application of a methodology we developed earlier to capture a decision maker's preferences in multiobjective environments to a notorious problem in the realm of Air Traffic Management, namely the Airport Gate Assignment Problem. The problem has been modelled as an all-integer optimisation problem with two criteria. We have implemented this methodology into the commercial solver CPLEX and also into an Evolutionary Multiobjective Optimisation algorithm and we have solved with them a numerical instance of the Airport Gate Assignment Problem for a couple of decision making scenarios

Trade-Off Guided Search for Approximate Pareto Optimal Portfolios

In this paper, we attempt to represent the Pareto Front in the Markowitz mean-variance model by two-sided discrete approximations. We discuss the possibility of using such approximations for portfolio selection. The potential of the approach is illustrated by the results of preliminary numerical experiments

Multiobjective optimization in the Airport Gate Assignment Problem, exact versus evolutionary multiobjective optimization

In this paper, we approach the Airport Gate Assignment Problem by Multiobjective Optimization as well as Evolutionary Multi-objective Optimization. We solve a bi-criteria formulation of this problem by the commercial mixedinteger programming solver CPLEX and a dedicated Evolutionary Multiobjective Optimization algorithm. To deal with multiple objectives, we apply a methodology that we developed earlier to capture decision-maker preferences in multi-objective environments. We present the results of numerical tests for these two approaches

A condition for asset redundancy in the mean-variance model of portfolio investment

The mean-variance approach to portfolio investment exploits the fact that the diversification of investments by combination of different assets in one portfolio allows for reducing the financial risks significantly. The mean-variance model is formulated as a bi-objective optimization problem with linear (expected return) and quadratic (variance) objective functions. Given a set of available assets, the investor searches for a portfolio yielding the most preferred combination of these objectives. Naturally, the search is limited to the set of non-dominated combinations, referred to as the Pareto front. Due to the globalization of financial markets, investors nowadays have access to large numbers of assets. We examine the possibility of reducing the problem size by identifying those assets, whose removal does not affect the resulting Pareto front, thereby not deteriorating the quality of the solution from the investor’s perspective. We found a sufficient condition for asset redundancy, which can be verified before solving the problem. This condition is based on the possibility of reallocating the share of one asset in a portfolio to another asset without deteriorating the objective function values. We also proposed a parametric relaxation of this condition, making it possible to removemore assets for a price of a negligible deterioration of the Pareto front. Computational experiments conducted on five real-world problems have demonstrated that the problem size can be reduced significantly using the proposed approach