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1
Content available Multi-Objective Optimization Applied for Planning of Regional European Airlin
Majka A.
Archives of Transport
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2014
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Vol. 29, iss. 1
37--46
EN
Fleet planning is very important elements in the airlines planning process. Fleet planning should answer the question which types of aircraft are required and how many of them are required taking into account the current and future transportation needs. Decision-making in the field of operations has a character of engineering. This process requires consideration of many factors, dependencies and criteria. The article presents the decision problem formulated in the form of a multi-objective mathematical model. This work preliminarily determines the structure of the transportation system which performs carriages on the local routes.
2
Content available remote An approach to finding trade-off solutions by a linear transformation of objective functions
Podkopaev D.
Control and Cybernetics
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2007
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Vol. 36, no 2
347-355
EN
We address the problem of deriving Pareto optimal solutions of multiple objective optimization problems with predetermined upper bounds on trade-offs. As shown, this can be achieved by a linear transformation of objective functions. Each non-diagonal element of the transformation matrix is related to a bound on the trade-off between a pair of the objective functions.
3
Content available remote Multiple objective optimisation of crew size in public transportation system
Żak J.
Archives of Transport
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2007
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Vol. 19, iss. 1-2
193-209
EN
The paper presents mathematical formulation and a solution procedure for a multiple objective crew sizing problem considered in the mass transit system. It is defined for a complex transportation process carried out in a medium-sized transportation system, operated by a public transportation company (PTC). In the problem of formulation, the interests of different stakeholders are taken into consideration. The problem is solved in a two-phase solution procedure. In the first phase, a set of Pareto optimal solutions is generated by an original, customised heuristic procedure implemented in a computer software PEOPLE. In the second one, the Light Beam Search (LBS) method is applied to review and evaluate the generated set and finally select the most desired outcome.
PL
W artykule przedstawiono sformułowanie matematyczne oraz procedurę rozwiązania wielokryterialnego problemu ustalania liczebności pracowników rozważanego w systemie transportu miejskiego. Problem decyzyjny zdefiniowano dla złożonego procesu transportu pasażerskiego realizowanego w systemie transportowym średniej wielkości, zarządzanego przez Miejskie Przedsiębiorstwo Komunikacji (MPK). W sformułowaniu problemu uwzględniono interesy różnych podmiotów ("oddziaływaczy"). Problem decyzyjny rozwiązano za pomocą dwuetapowej procedury rozwiązania. W pierwszej fazie wygenerowano zbiór rozwiązań paretooptymalnych, wykorzystując do tego celu oryginalny algorytm heurystyczny, dostosowany do specyfIki problemu i zaimplementowany w postaci programu komputerowego PEOPLE, w drugiej zaś fazie do przeglądu i oceny wygenerowanego zbioru oraz ostatecznego wyboru najbardziej pożądanego rozwiązania zastosowano metodę Light Beam Search (LBS).
4
Content available remote Obtaining a restricted Pareto front in evolutionary multiobjective optimization
Borges C. C. H., Barbosa H. J. C.
Foundations of Computing and Decision Sciences
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2001
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Vol. 26, No. 1
5-21
EN
Novel strategies are proposed to deal with multiobjective optimization problems solved via evolutionary computation. The goal of this work is to introduce procedures that allow for the search over a restricted Pareto front where all solutions attain acceptable objective functions values in a way that a minimum of prior knowledge about the objective functions is required.
5
Content available remote Experimental feedback on bioobjective permutation scheduling problems solved with a population heuristic
Morita H., Gandibleux X., Katoh N.
Foundations of Computing and Decision Sciences
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2001
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Vol. 26, No. 1
23-50
EN
Population heuristics present native abilities for solving optimization problems with multiple objectives. Using evolution and adaptation mechanisms, a population of individuals evolves in order to describe a good approximation of the efficient solutions. The resolution of the 1 | | (EC,- ,Tmaar) permutation scheduling problems, for which an exact algorithm is available, is investigated using a population heuristic based on genetic algorithms. The aim here is not to put in competition a heuristic method with an exact method but to give an experimental feedback on the resolution abilities of biobjective permutation scheduling problems with a population heuristic. The paper reports the aspects analyzed in this study : first, the pertinence of using genetic information into a population algorithm and second, a detailed multicriteria analysis of efficient solutions for this class of scheduling problems.
6
Content available remote An evolutionary algorithm with a multilevel pairing strategy for single and multiobjective optimization
Ray T., Tai K.
Foundations of Computing and Decision Sciences
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2001
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Vol. 26, No. 1
75-98
EN
This paper presents an evolutionary algorithm that incorporates a multilevel pairing strategy to solve single and multiobjective optimization problems. The algorithm is based on nondominance of solutions separately in the objective and the constraint space and uses cooperative mating strategies between solutions. Since the methodology is based on nondominance separately in the objective and the constraint space, scaling and aggregation affecting conventional penalty function methods for constraint handling does not arise. The proposed cooperative and intelligent pairing strategies result in mating between solutions that are good in objectives- with those that are good in constraint satisfaction, thus helping to speed up convergence. The diversification mechanism in the algorithm is based on niching that results in a wide spread of solutions in the parametric space. Three constrained multiobjective design examples and a single objective optimization problem with continuous and mixed variables are used to illustrate the performance of the proposed algorithm.
7
Content available remote Multiobjective ant algorithm for continuous function optimizatio: combination of strength Pareto fitness assignment and thermodynamic clustering
Shelokar S., Adhikari S., Vakil R., Jayaraman V. K., Kulkarni B. D.
Foundations of Computing and Decision Sciences
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2000
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Vol. 25, No. 4
213-230
EN
A new multiobjective ant algorithm augmented with the strength Pareto fitness assignment methodology and thermodynamic clustering technique is presented. The algorithm is very effective in preserving diversity and in obtaining Pareto optimal solutions. The efficacy of the algorithm is illustrated by considering four benchmarking test examples.
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