Optimising a nonlinear utility function in multi-objective integer programming

In this paper we develop an algorithm to optimise a nonlinear utility function of multiple objectives over the integer efficient set. Our approach is based on identifying and updating bounds on the individual objectives as well as the optimal utility value. This is done using already known solutions, linear programming relaxations, utility function inversion, and integer programming. We develop a general optimisation algorithm for use with k objectives, and we illustrate our approach using a tri-objective integer programming problem.Comment: 11 pages, 2 tables; v3: minor revisions, to appear in Journal of Global Optimizatio

Lexicographic Max-Ordering - A Solution Concept for Multicriteria Combinatorial Optimization

In this paper we will introduce the concept of lexicographic max-ordering solutions for multicriteria combinatorial optimization problems. Section 1 provides the basic notions of multicriteria combinatorial optimization and the definition of lexicographic max-ordering solutions. In Section 2 we will show that lexicographic max-ordering solutions are pareto optimal as well as max-ordering optimal solutions. Furthermore lexicographic max-ordering solutions can be used to characterize the set of pareto solutions. Further properties of lexicographic max-ordering solutions are given. Section 3 will be devoted to algorithms. We give a polynomial time algorithm for the two criteria case where one criterion is a sum and one is a bottleneck objective function, provided that the one criterion sum problem is solvable in polynomial time. For bottleneck functions an algorithm for the general case of Q criteria is presented

Linear force and moment equations for an annular smooth shaft seal perturbed both angularly and laterally

Coefficients are derived for equations expressing the lateral force and pitching moments associated with both planar translation and angular perturbations from a nominally centered rotating shaft with respect to a stationary seal. The coefficients for the lowest order and first derivative terms emerge as being significant and are of approximately the same order of magnitude as the fundamental coefficients derived by means of Black's equations. Second derivative, shear perturbation, and entrance coefficient variation effects are adjudged to be small

Primal and dual multi-objective linear programming algorithms for linear multiplicative programmes

Multiplicative programming problems (MPPs) are global optimization problems known to be NP-hard. In this paper, we employ algorithms developed to compute the entire set of nondominated points of multi-objective linear programmes (MOLPs) to solve linear MPPs. First, we improve our own objective space cut and bound algorithm for convex MPPs in the special case of linear MPPs by only solving one linear programme in each iteration, instead of two as the previous version indicates. We call this algorithm, which is based on Benson’s outer approximation algorithm for MOLPs, the primal objective space algorithm. Then, based on the dual variant of Benson’s algorithm, we propose a dual objective space algorithm for solving linear MPPs. The dual algorithm also requires solving only one linear programme in each iteration. We prove the correctness of the dual algorithm and use computational experiments comparing our algorithms to a recent global optimization algorithm for linear MPPs from the literature as well as two general global optimization solvers to demonstrate the superiority of the new algorithms in terms of computation time. Thus, we demonstrate that the use of multi-objective optimization techniques can be beneficial to solve difficult single objective global optimization problems

Influence of soil properties on the aboveground blast environment from a near-surface detonation

Detonation of an explosive charge, such as a mine or an improvised explosive device (IED) at the ground surface or buried at shallow depth in soil, can produce high airblast pressures and significant dynamic soil debris loads on an overlying or nearby structure, such as a vehicle passing over the explosive. The blast loading environment is a function of many factors including the explosive type, configuration, mass, and depth of burial, soil characteristics, and the distance between the ground surface and the structure or object. During the past several years, the US Army has focused considerable attention on developing improved methods for predicting this environment, particularly for use by vehicle/armor analysts, thereby, improving the survivability of these platforms. Research is needed to better understand the aboveground environment created by the detonation of a shallow-buried explosive in order to design adequate protective measures for an aboveground structure. Unfortunately, there is no accurate methodology for predicting these airblast and soil debris loads to support the designs. Development of the required prediction tools is hampered by lack of well controlled and documented experimental results for these complex loads. Without detailed experimental data, the numerical simulations of these loads cannot be adequately validated for the large deformation, stress, and motion gradients and the resulting interactions with structures. The focus of this research is to quantify the influence of soil properties on the aboveground environment from the detonation of a bare explosive charge resting on the soil surface or shallow-buried. In order to fully quantify the influence of soil parameters, well-controlled experiments were designed to directly measure soil debris and airblast loadings on an aboveground reaction structure due to the detonation of explosives at the surface of and shallow buried in three very different soils. The experiments were performed using specifications and strict quality controls that limited the influence of outside variables and ensured the experiments were repeatable. The experiments provided blast pressure, soil stress, and impulse data for each soil type. These data were analyzed to investigate the influence of the properties of the different soil types on the aboveground environment

Operations Research Methods for Optimization in Radiation Oncology

Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the uence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modern software facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered

Modeling Techniques for Evaluation the Effectiveness of Particle Damping in Turbomachinery

High power turbopumps are frequently used to supply propellants to the combustion chambers of rocket engines. Due to the high pressures and flow-rates required, turbopump components are subjected to harsh environments which include dynamic excitation due to random, sine, and acoustic vibration. Additionally, fluid-induced forces can couple with the dynamics of the structure resulting in flow induced instabilities (flutter). Structural response to these forms of excitation results in reduced fatigue life and increases the likelihood of an operational failure. Particle damping has been used successfully on vibration problems in the past by increasing the damping and therefore reducing the response to acceptable levels. Empirical methods have typically been employed to evaluate the performance of the particles in reducing the structural response. This report explores the use of finite element methods to estimate the effectiveness of particle damping in a typical non-rotating turbopump component. Axisymmetric harmonic models are used to estimate the increase in modal damping produced by the addition of particles in the cavity of an axisymmetric seal. Target modes of vibration are evaluated to quantify how the effective particle damping is altered by geometry changes in the seal design. A new method to predict the performance of particle dampers is developed and shown to provide more reasonable estimates of damping

A bi-objective column generation algorithm for the multi-commodity minimum cost flow problem

We present a column generation algorithm for solving the bi-objective multi-commodity minimum cost flow problem. This method is based on the bi-objective simplex method and Dantzig–Wolfe decomposition. The method is initialised by optimising the problem with respect to the first objective, a single objective multi-commodity flow problem, which is solved using Dantzig–Wolfe decomposition. Then, similar to the bi-objective simplex method, our algorithm iteratively moves from one non-dominated extreme point to the next one by finding entering variables with the maximum ratio of improvement of the second objective over deterioration of the first objective. Our method reformulates the problem into a bi-objective master problem over a set of capacity constraints and several single objective linear fractional sub-problems each over a set of network flow conservation constraints. The master problem iteratively updates cost coefficients for the fractional sub-problems. Based on these cost coefficients an optimal solution of each sub-problem is obtained. The solution with the best ratio objective value out of all sub-problems represents the entering variable for the master basis. The algorithm terminates when there is no entering variable which can improve the second objective by deteriorating the first objective. This implies that all non-dominated extreme points of the original problem are obtained. We report on the performance of the algorithm on several directed bi-objective network instances with different characteristics and different numbers of commodities

Approximating Pareto frontier using a hybrid line search approach

This is the post-print version of the final paper published in Information Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.CNCSIS IDEI 2412, Romani