Approximating multi-objective scheduling problems
Document type :
Article dans une revue scientifique
Title :
Approximating multi-objective scheduling problems
Author(s) :
Dabia, Said [Auteur]
Department of Industrial Engineering and Innovation Sciences
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
van Woensel, Tom [Auteur]
Department of Industrial Engineering and Innovation Sciences
de Kok, Ton [Auteur]
Department of Industrial Engineering and Innovation Sciences
Department of Industrial Engineering and Innovation Sciences
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
van Woensel, Tom [Auteur]
Department of Industrial Engineering and Innovation Sciences
de Kok, Ton [Auteur]
Department of Industrial Engineering and Innovation Sciences
Journal title :
Computers and Operations Research
Pages :
1165-1175
Publisher :
Elsevier
Publication date :
2013-05
ISSN :
0305-0548
English keyword(s) :
Multi-objective decisions
State-dependent costs
Approximation
Dynamic programming
State-dependent costs
Approximation
Dynamic programming
HAL domain(s) :
Informatique [cs]/Recherche opérationnelle [cs.RO]
English abstract : [en]
In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions ...
Show more >In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to the complexity of MOSPs, an efficient approximation based on dynamic programming is developed. The approximation has a provable worst case performance guarantee. Even though the approximate Pareto set consists of fewer solutions, it represents a good coverage of the true set of Pareto solutions. We consider generic cost parameters that depend on the state of the system. Numerical results are presented for the time-dependent multi-objective knapsack problem, showing the value of the approximation in the special case when the state of the system is expressed in terms of time.Show less >
Show more >In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to the complexity of MOSPs, an efficient approximation based on dynamic programming is developed. The approximation has a provable worst case performance guarantee. Even though the approximate Pareto set consists of fewer solutions, it represents a good coverage of the true set of Pareto solutions. We consider generic cost parameters that depend on the state of the system. Numerical results are presented for the time-dependent multi-objective knapsack problem, showing the value of the approximation in the special case when the state of the system is expressed in terms of time.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :