488. A multi-objective evolutionary model for the generalised bin packing problem
Invited abstract in session MA-21: Packing with practical constraints, stream Cutting and packing (ESICUP).
Monday, 8:30-10:00Room: Esther Simpson 2.12
Authors (first author is the speaker)
| 1. | Rosephine Georgina Rakotonirainy
|
| Department of Statistical Sciences, University of Cape Town | |
| 2. | Andrea Plumbley
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| Statistical Sciences, University of Cape Town |
Abstract
This study investigates the multi-objective optimization of the generalized bin packing problem, which involves allocating both compulsory and non-compulsory items into a set of bins. The items possess various characteristics, such as weight, width, height, and due date, while the bins have attributes including capacity and cost. The primary goal of the problem is to minimize the total cost, which is typically achieved by minimizing the number of bins used. In many practical scenarios, however, multiple competing objectives need to be addressed, such as minimizing item lateness or balancing the load across bins. This work proposes a multi-objective evolutionary model for solving the generalized bin packing problem. The proposed model simultaneously optimizes multiple objectives, presenting decision-makers with a Pareto front of trade-off solutions. Two specific objective combinations were considered: cost and item lateness, and cost and load imbalance. The model was tested on both one-dimensional and two-dimensional problem instances, demonstrating its capacity to minimize the relevant objectives while also generating a set of conflicting solutions in certain cases.
Keywords
- Cutting and Packing
- Algorithms
- Programming, Multi-Objective
Status: accepted
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