2212. Discrete Representation of Nondominated Sets in Multi-objective Integer Programs
Invited abstract in session MA-51: Recent advances in multiobjective optimization, stream Multiobjective and vector optimization.
Monday, 8:30-10:00Room: Parkinson B22
Authors (first author is the speaker)
| 1. | Banu Lokman
|
| University of Portsmouth | |
| 2. | ILGIN DOGAN
|
| Industrial Engineering, Middle East Technical University | |
| 3. | Murat Koksalan
|
| Industrial Engineering, METU |
Abstract
Multi-objective Integer Programs (MOIPs) have a broad range of applications in diverse domains including logistics, healthcare and energy. These problems often involve conflicting objectives, and as a result, there is no single optimal solution. It becomes crucial to identify nondominated points, where improving any one objective is not possible without compromising another. However, the number of nondominated points increases exponentially with the size of the problem, making it difficult to find each one.
Recognising the need to generate a representative subset of nondominated points with specific desirable properties, we examine the characteristics of nondominated sets in various MOIPs. We observe that the distribution of these points plays a key role in selecting representative ones. We develop an exact algorithm that generates a representative set guaranteeing a prespecified precision. Our experiments demonstrate that this approach is robust across a variety of MOIP types.
Keywords
- Multi-Objective Decision Making
- Mathematical Programming
- Algorithms
Status: accepted
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