A Many-Objective Multistage Optimization-Based Fuzzy Decision-Making Model for Coal Production Prediction

The traditional coal energy system cannot meet the requirements of modern industrial production for sustainable development because extensive economic growth has caused significant coal consumption. Therefore, it is particularly important to formulate an effective coal production decision-making scheme to support complex coal production systems for sustainable development. To address this challenge, an improved many-objective fuzzy decision-making model with human participation for coal production prediction is established in this article. The model includes five objective functions: economic, energetic, environmental, coal gangue, and safety profits. Moreover, a novel multistage many-objective optimization algorithm is designed to adjust relevant model parameters and make the obtained model solution more accurate when compared with involved algorithms. In the algorithm, there are three stages of optimization. In stage one, the model with the relatively good convergence solution is chosen. In stage two, the diversity of the model solution needs to be retained by employing a diversity maintenance mechanism. In stage three, a comprehensive measurement approach is designed to ensure the balance of convergence and diversity during population evolution. To verify the validity of the model and the superiority of the optimization algorithm, five other advanced many-objective algorithms are used for comparison. The results from simulations on a benchmark function show that the proposed optimization algorithm has better performance than involved algorithms. Model solutions obtained on different metrics have better convergence and diversity for the coal fuzzy decision-making problem.

I. Introduction

With sustainable coal energy production becoming critical, making reasonable decisions plays an important role in the development of the whole industry [1]. Whether it is coal production (CP) or other practical problems in the classical decision theory, only the certainty and randomness of the problem are considered [2]. The fuzzy uncertainty of the problem is completely ignored. However, these practical problems are always affected by various uncertain and fuzzy factors in the decision-making process [3],[4]. Various vague languages are typically used to exchange ideas and information in human decision-making activities. Then, reasoning analysis is conducted and evaluated comprehensively, and finally, reasonable decision making is realized [5]. This kind of vague intuition and strict organic synthesis and logical reasoning ability are viewed as the characteristics of human thinking activities [6],[7]. Therefore, it is both reasonable and necessary for decision -makers to introduce fuzzy uncertainty into decision science production research.