2,170 research outputs found
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
Multi-objective integer programming: An improved recursive algorithm
This paper introduces an improved recursive algorithm to generate the set of
all nondominated objective vectors for the Multi-Objective Integer Programming
(MOIP) problem. We significantly improve the earlier recursive algorithm of
\"Ozlen and Azizo\u{g}lu by using the set of already solved subproblems and
their solutions to avoid solving a large number of IPs. A numerical example is
presented to explain the workings of the algorithm, and we conduct a series of
computational experiments to show the savings that can be obtained. As our
experiments show, the improvement becomes more significant as the problems grow
larger in terms of the number of objectives.Comment: 11 pages, 6 tables; v2: added more details and a computational stud
Stability Analysis in Multicriteria Discrete Portfolio Optimization.
Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research.
While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing.
Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters).
This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions.
In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.Siirretty Doriast
Workload Equity in Vehicle Routing Problems: A Survey and Analysis
Over the past two decades, equity aspects have been considered in a growing
number of models and methods for vehicle routing problems (VRPs). Equity
concerns most often relate to fairly allocating workloads and to balancing the
utilization of resources, and many practical applications have been reported in
the literature. However, there has been only limited discussion about how
workload equity should be modeled in VRPs, and various measures for optimizing
such objectives have been proposed and implemented without a critical
evaluation of their respective merits and consequences.
This article addresses this gap with an analysis of classical and alternative
equity functions for biobjective VRP models. In our survey, we review and
categorize the existing literature on equitable VRPs. In the analysis, we
identify a set of axiomatic properties that an ideal equity measure should
satisfy, collect six common measures, and point out important connections
between their properties and those of the resulting Pareto-optimal solutions.
To gauge the extent of these implications, we also conduct a numerical study on
small biobjective VRP instances solvable to optimality. Our study reveals two
undesirable consequences when optimizing equity with nonmonotonic functions:
Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all
tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent,
i.e. composed of tours whose workloads are all equal to or longer than those of
other Pareto-optimal solutions. We show that the extent of these phenomena
should not be underestimated. The results of our biobjective analysis are valid
also for weighted sum, constraint-based, or single-objective models. Based on
this analysis, we conclude that monotonic equity functions are more appropriate
for certain types of VRP models, and suggest promising avenues for further
research.Comment: Accepted Manuscrip
Solving Linux Upgradeability Problems Using Boolean Optimization
Managing the software complexity of package-based systems can be regarded as
one of the main challenges in software architectures. Upgrades are required on
a short time basis and systems are expected to be reliable and consistent after
that. For each package in the system, a set of dependencies and a set of
conflicts have to be taken into account. Although this problem is
computationally hard to solve, efficient tools are required. In the best
scenario, the solutions provided should also be optimal in order to better
fulfill users requirements and expectations. This paper describes two different
tools, both based on Boolean satisfiability (SAT), for solving Linux
upgradeability problems. The problem instances used in the evaluation of these
tools were mainly obtained from real environments, and are subject to two
different lexicographic optimization criteria. The developed tools can provide
optimal solutions for many of the instances, but a few challenges remain.
Moreover, it is our understanding that this problem has many similarities with
other configuration problems, and therefore the same techniques can be used in
other domains.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083
Truthful Assignment without Money
We study the design of truthful mechanisms that do not use payments for the
generalized assignment problem (GAP) and its variants. An instance of the GAP
consists of a bipartite graph with jobs on one side and machines on the other.
Machines have capacities and edges have values and sizes; the goal is to
construct a welfare maximizing feasible assignment. In our model of private
valuations, motivated by impossibility results, the value and sizes on all
job-machine pairs are public information; however, whether an edge exists or
not in the bipartite graph is a job's private information.
We study several variants of the GAP starting with matching. For the
unweighted version, we give an optimal strategyproof mechanism; for maximum
weight bipartite matching, however, we show give a 2-approximate strategyproof
mechanism and show by a matching lowerbound that this is optimal. Next we study
knapsack-like problems, which are APX-hard. For these problems, we develop a
general LP-based technique that extends the ideas of Lavi and Swamy to reduce
designing a truthful mechanism without money to designing such a mechanism for
the fractional version of the problem, at a loss of a factor equal to the
integrality gap in the approximation ratio. We use this technique to obtain
strategyproof mechanisms with constant approximation ratios for these problems.
We then design an O(log n)-approximate strategyproof mechanism for the GAP by
reducing, with logarithmic loss in the approximation, to our solution for the
value-invariant GAP. Our technique may be of independent interest for designing
truthful mechanisms without money for other LP-based problems.Comment: Extended abstract appears in the 11th ACM Conference on Electronic
Commerce (EC), 201
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