Optimal Selection of Assets Investing Composition Plan based on Grey Multi Objective Programming method

TL;DR

This paper develops and applies grey multi-objective programming algorithms to optimize asset investment plans, effectively handling uncertainty and demonstrating practical applicability through detailed case examples.

Contribution

It introduces novel algorithms for grey multi-objective programming using weighting and q-positioned methods, tailored for asset investment decision-making under uncertainty.

Findings

Algorithms effectively handle grey uncertainty in asset allocation.

Application examples demonstrate the algorithms' practicality and efficiency.

The methods improve decision-making in asset investment planning.

Abstract

The problem for selection of appropriate assets investing composition projects such as assets rationalization plays an important role in promotion of business systems. We consider the assets investing composition plan problems subject to grey multiobjective programming with the grey inequality constraints. In this paper, we show in detail the entire process of the application from modeling the case problem to generating its solution. To solve the grey multi objective programming problem, we then develop and apply an algorithm of grey multiple objective programming by weighting method and an algorithm of grey multiple objective programming based on q -positioned programming method. These algorithms all regard as of great importance uncertainty (greyness) at grey multiobjective programming and simple and easy the calculating process. The calculating examples of paper also show ability and effectiveness of algorithms.