Abstract This paper explores the characteristics of solutions when scheduling jobs in a shop with parallel machines. Three classical objective functions were considered: makespan, total completion time, and tardiness. These three criteria combined pairs, resulting bi-objective formulations. formulations solved using ε-constraint method to obtain Pareto frontier for each pair. The research is ev...

A real-life application of a new approach to integrated assessment and screening of water quality improvement strategies in large river basins is presented. The approach is based on the integration of diverse data and mathematical models as well as on the application of interactive visualization of the Pareto frontier. The case study water quality planning in the Oka River, the Volga River basi...

We propose a computational procedure to find the efficient frontier for the standard Markowitz mean-variance model with discrete variables. The integer constraints limit on the one hand the portfolio to contain a predetermined number of assets and, on the other hand, the proportion of the portfolio held in a given asset. We adapt the multiobjective algorithm NSGA for solving the problem. The al...

In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for biand tri-objective programming problems. Numerical experiments are carried out on a ...

Although usage of genetic algorithms (GAs) has become widespread, the theoretical work from the genetic and evolutionary computation (GEC) field has been largely ignored by practitioners in realworld applications. This paper provides an overview of a three-step method for utilizing GEC theory to ensure robust search and avoid the common pitfalls in GA applications. Additionally, this study pres...

Pruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing. Unlike prior work, we train a pruning policy under an objective that measures end-to-end performance: we search for a fast and accurate policy. This poses a difficult machine learning problem, which we tackle with the LOLS algorithm. LOLS training must continually compute the effect...

We develop a novel sensor placement method that maximizes monitoring performance while minimizing deployment cost. Our integrates reduced order model and multi-objective combinatorial optimization. first decompose the spatio-temporal state field to be monitored by proper orthogonal decomposition (POD), we use Gaussian Process uncertainty in each POD mode. Next, lazy greedy (LG)-∊-constraint opt...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz’s meanvariance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four rea...

Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle sw...

Manufacturing industries aim to improve product quantity and quality. These trade-off objectives are typically manifested as cycle time reduction motion accuracy improvement. Corner smoothing approaches that reduce the of piecewise linear trajectories proposed in literature. This study tackles two by Pareto-optimal corner with constraints imposed kinematic limits, continuity conditions user-spe...