Abstract This paper presents AUGMECON-Py, a Python framework for solving large and complex multi-objective linear programming problems under uncertainty, optimally robustly capturing all solutions. On the core of AUGMECON-Py software lies integration well-established optimisation algorithm (AUGMECON) with Monte Carlo analysis that helps maximise robustness against stochastic thereby av...

The design of linear Support Vector Machine (SVM) classification techniques is generally a Multi-objective Optimization Problem (MOP). These require finding appropriate trade-offs between two objectives, such as the amount misclassified training data (classification error) and number non-zero elements separator hyperplane. In this article, we review several SVM models in form multi-objective op...

This paper extends the proposed method by Jahanshahloo et al. (2004) (a method for generating all the efficient solutions of a 0–1 multi-objective linear programming problem, Asia-Pacific Journal of Operational Research). This paper considers the recession direction for a multi-objective integer linear programming (MOILP) problem and presents necessary and sufficient conditions to have unbounde...

this paper uses the weighted l$_1-$norm to propose an algorithm for finding a well-dispersed subset of non-dominated solutions of multiple objective mixed integer linear programming problem. when all variables are integer it finds the whole set of efficient solutions. in each iteration of the proposed method only a mixed integer linear programming problem is solved and its optimal solutions gen...