2006
DOI: 10.1007/s10479-006-0074-z
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A discussion of scalarization techniques for multiple objective integer programming

Abstract: In this paper we consider solution methods for multiobjective integer programming (MOIP) problems based on scalarization. We define the MOIP, discuss some common scalarizations, and provide a general formulation that encompasses most scalarizations that have been applied in the MOIP context as special cases. We show that these methods suffer some drawbacks by either only being able to find supported efficient solutions or introducing constraints that can make the computational effort to solve the scalarization… Show more

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Cited by 160 publications
(68 citation statements)
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“…Such competing goals cannot be arbitrarily squeezed within the narrow framework of a unique objective function, without running the risk of invalidating all implications that are supposed to be drawn from the analysis. Simple examples (see e.g., [9][10][11][12][13]) are in line with the endorsed paradox [14] and the Arrow's impossibility Theorem [15], where there are no good ways of aggregating conflicting criteria into a single one. This has given rise to the field of Multiobjective Programming (MOP).…”
Section: Introductionmentioning
confidence: 78%
“…Such competing goals cannot be arbitrarily squeezed within the narrow framework of a unique objective function, without running the risk of invalidating all implications that are supposed to be drawn from the analysis. Simple examples (see e.g., [9][10][11][12][13]) are in line with the endorsed paradox [14] and the Arrow's impossibility Theorem [15], where there are no good ways of aggregating conflicting criteria into a single one. This has given rise to the field of Multiobjective Programming (MOP).…”
Section: Introductionmentioning
confidence: 78%
“…Such sub-optimality may be introduced by a heuristic combination operator, by the reduction operator, or by previous optimizations. We use the weighted sum schema [12] (i.e. a linear combination of all objectives) so that single-objective local optimization techniques can be used to push the value of a solution into a certain direction as illustrated in Fig.…”
Section: ) Improvement Operatorsmentioning
confidence: 99%
“…We extend the state-of-the art single-objective MA-GTSP to estimate solutions for the multiple objective variant following the weighted-sum method [12]. This method minimizes a positively weighted sum of the objectives: …”
Section: B Details Of Reference Algorithmmentioning
confidence: 99%
“…In other words, there is not generally a feasible absolute optimal solution enables to optimise all objective functions concurrently (Ehrgott 2006). To take such a problem, Pareto-optimal solutions have attracted much more attentions that typically referred to as ''a posteriori'' or ''non-dominated solution generation''.…”
Section: Solution Approachmentioning
confidence: 99%