Using goal programming on estimated Pareto fronts to solve multiobjective problems

Pinheiro, Rodrigo Lankaites; Landa-Silva, Dario; Laesanklang, Wasakorn; Constantino, Ademir Aparecido

Abstract

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time.

Citation

Pinheiro, R. L., Landa-Silva, D., Laesanklang, W., & Constantino, A. A. (2018). Using goal programming on estimated Pareto fronts to solve multiobjective problems.

• Conference Name: 7th International Conference on Operations Research and Enterprise Systems (ICORES 2018)
• End Date: Jan 26, 2018
• Acceptance Date: Nov 22, 2017
• Publication Date: Jan 24, 2018
• Deposit Date: Dec 8, 2017
• Publicly Available Date: Jan 24, 2018
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/906902