A Multi-Objective Evolutionary Algorithm for Multi-Energy Allocation Problem Considering Production Changeover in the Integrated Iron and Steel Enterprise

Abstract

In this paper, the energy allocation problem of major production equipment is investigated from the perspective of the whole production process to meet the energy demands of the production system in the iron and steel enterprise. Taking into account the replacement between energy and the changeover state of equipment, a bi-objective mixed integer programming model is established to minimize the total energy cost and changeover cost of equipment. A multi-objective evolutionary algorithm (MOEA) is proposed to solve the model. The MOEA takes into account the correlation among variables in the model and extracts free variables to encode the individual. In order to preserve and utilize the local non-dominated information during evolution, a set-based population structure is proposed. A self-adaptive selection strategy of crossover operators is also designed to improve the efficiency of evolution. With the aid of the 0–1 state variables in the model, an improvement and updating mechanism is proposed to improve the quality and diversity of the external archive, which can help to prevent the evolution from premature or trapping in a local optimum. The computational results based on 160 practical instances illustrate that the proposed MOEA is superior to NSGA-II and MOEA/D and has potential application ability in practical production.