56 research outputs found
Algorithm Engineering in Robust Optimization
Robust optimization is a young and emerging field of research having received
a considerable increase of interest over the last decade. In this paper, we
argue that the the algorithm engineering methodology fits very well to the
field of robust optimization and yields a rewarding new perspective on both the
current state of research and open research directions.
To this end we go through the algorithm engineering cycle of design and
analysis of concepts, development and implementation of algorithms, and
theoretical and experimental evaluation. We show that many ideas of algorithm
engineering have already been applied in publications on robust optimization.
Most work on robust optimization is devoted to analysis of the concepts and the
development of algorithms, some papers deal with the evaluation of a particular
concept in case studies, and work on comparison of concepts just starts. What
is still a drawback in many papers on robustness is the missing link to include
the results of the experiments again in the design
Minmax regret combinatorial optimization problems: an Algorithmic Perspective
Candia-Vejar, A (reprint author), Univ Talca, Modeling & Ind Management Dept, Curico, Chile.Uncertainty in optimization is not a new ingredient. Diverse models considering uncertainty have been developed over the last 40 years. In our paper we essentially discuss a particular uncertainty model associated with combinatorial optimization problems, developed in the 90's and broadly studied in the past years. This approach named minmax regret (in particular our emphasis is on the robust deviation criteria) is different from the classical approach for handling uncertainty, stochastic approach, where uncertainty is modeled by assumed probability distributions over the space of all possible scenarios and the objective is to find a solution with good probabilistic performance. In the minmax regret (MMR) approach, the set of all possible scenarios is described deterministically, and the search is for a solution that performs reasonably well for all scenarios, i.e., that has the best worst-case performance. In this paper we discuss the computational complexity of some classic combinatorial optimization problems using MMR. approach, analyze the design of several algorithms for these problems, suggest the study of some specific research problems in this attractive area, and also discuss some applications using this model
Qualitative Characteristics and Quantitative Measures of Solution's Reliability in Discrete Optimization: Traditional Analytical Approaches, Innovative Computational Methods and Applicability
The purpose of this thesis is twofold. The first and major part is devoted to
sensitivity analysis of various discrete optimization problems while the second
part addresses methods applied for calculating measures of solution stability
and solving multicriteria discrete optimization problems.
Despite numerous approaches to stability analysis of discrete optimization
problems two major directions can be single out: quantitative and qualitative.
Qualitative sensitivity analysis is conducted for multicriteria discrete optimization
problems with minisum, minimax and minimin partial criteria. The main
results obtained here are necessary and sufficient conditions for different stability
types of optimal solutions (or a set of optimal solutions) of the considered
problems.
Within the framework of quantitative direction various measures of solution
stability are investigated. A formula for a quantitative characteristic called
stability radius is obtained for the generalized equilibrium situation invariant
to changes of game parameters in the case of the H¨older metric. Quality of the
problem solution can also be described in terms of robustness analysis. In this
work the concepts of accuracy and robustness tolerances are presented for a
strategic game with a finite number of players where initial coefficients (costs)
of linear payoff functions are subject to perturbations.
Investigation of stability radius also aims to devise methods for its calculation.
A new metaheuristic approach is derived for calculation of stability
radius of an optimal solution to the shortest path problem. The main advantage
of the developed method is that it can be potentially applicable for
calculating stability radii of NP-hard problems.
The last chapter of the thesis focuses on deriving innovative methods based
on interactive optimization approach for solving multicriteria combinatorial
optimization problems. The key idea of the proposed approach is to utilize
a parameterized achievement scalarizing function for solution calculation and
to direct interactive procedure by changing weighting coefficients of this function.
In order to illustrate the introduced ideas a decision making process is
simulated for three objective median location problem.
The concepts, models, and ideas collected and analyzed in this thesis create
a good and relevant grounds for developing more complicated and integrated
models of postoptimal analysis and solving the most computationally challenging
problems related to it.Siirretty Doriast
Models and algorithms for deterministic and robust discrete time/cost trade-off problems
Ankara : The Department of Management, Bilkent University, 2008.Thesis (Ph.D.) -- Bilkent University, 2008.Includes bibliographical references leaves 136-145Projects are subject to various sources of uncertainties that often negatively
impact activity durations and costs. Therefore, it is of crucial importance to develop
effective approaches to generate robust project schedules that are less vulnerable to
disruptions caused by uncontrollable factors. This dissertation concentrates on robust
scheduling in project environments; specifically, we address the discrete time/cost
trade-off problem (DTCTP).
Firstly, Benders Decomposition based exact algorithms to solve the deadline
and the budget versions of the deterministic DTCTP of realistic sizes are proposed.
We have included several features to accelerate the convergence and solve large
instances to optimality. Secondly, we incorporate uncertainty in activity costs. We
formulate robust DTCTP using three alternative models. We develop exact and
heuristic algorithms to solve the robust models in which uncertainty is modeled via
interval costs. The main contribution is the incorporation of uncertainty into a
practically relevant project scheduling problem and developing problem specific
solution approaches. To the best of our knowledge, this research is the first
application of robust optimization to DTCTP.
Finally, we introduce some surrogate measures that aim at providing an
accurate estimate of the schedule robustness. The pertinence of proposed measures is
assessed through computational experiments. Using the insight revealed by the
computational study, we propose a two-stage robust scheduling algorithm.
Furthermore, we provide evidence that the proposed approach can be extended to
solve a scheduling problem with tardiness penalties and earliness rewards.Hazır, ÖncüPh.D
Mathematical Multi-Objective Optimization of the Tactical Allocation of Machining Resources in Functional Workshops
In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers and to maintain control of the tied-up working capital. We introduce new multi-item, multi-level capacitated resource allocation models with a medium--to--long--term planning horizon. The model refers to functional workshops where costly and/or time- and resource-demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimize the maximum excess resource loading above a given loading threshold while incurring as low qualification costs as possible and minimizing the inventory.In Paper I, we propose a new bi-objective mixed-integer (linear) optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. In Paper II, we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. In Paper III, we extend the TRAP with an inventory of semi-finished as well as finished parts, resulting in a tri-objective mixed-integer (linear) programming model. We create a criterion space partitioning approach that enables solving sub-problems simultaneously. In Paper IV, using our knowledge from our previous work we embarked upon a task to generalize our findings to develop an approach for any discrete tri-objective optimization problem. The focus is on identifying a representative set of non-dominated points with a pre-defined desired coverage gap
Mixed uncertainty sets for robust combinatorial optimization
In robust optimization, the uncertainty set is used to model all possible outcomes of uncertain parameters. In the classic setting, one assumes that this set is provided by the decision maker based on the data available to her. Only recently it has been recognized that the process of building useful uncertainty sets is in itself a challenging task that requires mathematical support. In this paper, we propose an approach to go beyond the classic setting, by assuming multiple uncertainty sets to be prepared, each with a weight showing the degree of belief that the set is a “true” model of uncertainty. We consider theoretical aspects of this approach and show that it is as easy to model as the classic setting. In an extensive computational study using a shortest path problem based on real-world data, we auto-tune uncertainty sets to the available data, and show that with regard to out-of-sample performance, the combination of multiple sets can give better results than each set on its own
Criticality Analysis of Activity Networks under Interval Uncertainty
Dedicated to the memory of Professor Stefan Chanas - The extended abstract version of this paper has appeared in Proceedings of 11th International Conference on Principles and Practice of Constraint Programming (CP2005) ("Interval Analysis in Scheduling", Fortin et al. 2005)International audienceThis paper reconsiders the Project Evaluation and Review Technique (PERT) scheduling problem when information about task duration is incomplete. We model uncertainty on task durations by intervals. With this problem formulation, our goal is to assert possible and necessary criticality of the different tasks and to compute their possible earliest starting dates, latest starting dates, and floats. This paper combines various results and provides a complete solution to the problem. We present the complexity results of all considered subproblems and efficient algorithms to solve them
Database query optimisation based on measures of regret
The query optimiser in a database management system (DBMS) is responsible for
�nding a good order in which to execute the operators in a given query. However, in
practice the query optimiser does not usually guarantee to �nd the best plan. This is
often due to the non-availability of precise statistical data or inaccurate assumptions
made by the optimiser. In this thesis we propose a robust approach to logical query
optimisation that takes into account the unreliability in database statistics during
the optimisation process. In particular, we study the ordering problem for selection
operators and for join operators, where selectivities are modelled as intervals rather
than exact values. As a measure of optimality, we use a concept from decision theory
called minmax regret optimisation (MRO).
When using interval selectivities, the decision problem for selection operator ordering
turns out to be NP-hard. After investigating properties of the problem and
identifying special cases which can be solved in polynomial time, we develop a novel
heuristic for solving the general selection ordering problem in polynomial time. Experimental
evaluation of the heuristic using synthetic data, the Star Schema Benchmark
and real-world data sets shows that it outperforms other heuristics (which take
an optimistic, pessimistic or midpoint approach) and also produces plans whose regret
is on average very close to optimal.
The general join ordering problem is known to be NP-hard, even for exact selectivities.
So, for interval selectivities, we restrict our investigation to sets of join
operators which form a chain and to plans that correspond to left-deep join trees.
We investigate properties of the problem and use these, along with ideas from the
selection ordering heuristic and other algorithms in the literature, to develop a
polynomial-time heuristic tailored for the join ordering problem. Experimental evaluation
of the heuristic shows that, once again, it performs better than the optimistic,
pessimistic and midpoint heuristics. In addition, the results show that the heuristic
produces plans whose regret is on average even closer to the optimal than for
selection ordering
Multi-Attribute Decision Tree Evaluation in Imprecise and Uncertain Domains
Abstract We present a decision tree evaluation method integrated with a common framework for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled in convex sets of utility and probability measures restricted by closed intervals. Evaluation is done relative to a set of rules, generalizing the concept of admissibility, computationally handled through optimization of aggregated utility functions. Pros and cons of two approaches, and tradeoffs in selecting a utility function, are discussed
A critical review of the approaches to optimization problems under uncertainty
Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Master's) -- Bilkent University, 2001.Includes bibliographical references leaves 58-72.In this study, the issue of uncertainty in optimization problems is studied. First of all,
the meaning and sources of uncertainty are explained and then possible ways of its
representation are analyzed.
About the modelling process, different approaches as sensitivity analysis, parametric
programming, robust optimization, stochastic programming, fuzzy programming,
multiobjective programming and imprecise optimization are presented with
advantages and disadvantages from different perspectives. Some extensions of the
concepts of imprecise optimization are also presented.Gürtuna, FilizM.S
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