Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based the number of optimized objective functions: single multi optimization problems. In this paper, a comparative review since 2000 using one deterministic stochastic modelling approaches called goal programming (GP) genetic algorithm (GA) ...

This paper examines the potential to use multiple objective programming to reduce nutrient excretion from dairy cows through incorporation of nutrient excretion functions into a ration formulation framework. In a typical ration formulation model, a ration is formulated to minimize cost while providing sucient nutrients to meet the needs of the animal type being fed. To reduce the nutrient load...

Let a polyhedral convex set be given by a finite number of linear inequalities and consider the problem to project this set onto a subspace. This problem, called polyhedral projection problem, is shown to be equivalent to multiple objective linear programming. The number of objectives of the multiple objective linear program is by one higher than the dimension of the projected polyhedron. The r...

multi-objective optimization is the simultaneous consideration of two or more objective functions that are completely or partially inconflict with each other. the optimality of such optimizations is largely defined through the pareto optimality. multiple objective integer linear programs (moilp) are special cases of multiple criteria decision making problems. numerous algorithms have been desig...

Abstract This paper addresses the problem of approximating set all solutions for Multi-objective Markov Decision Processes. We show that in vast majority interesting cases, number is exponential or even infinite. In order to overcome this difficulty we propose approximate by means a limited precision approach based on White’s multi-objective value-iteration dynamic programming algorithm. prove ...