Improved African buffalo optimization algorithm for the green flexible job shop scheduling problem considering energy consumption

Abstract

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has been received more and more attention from scholars and researchers. In this paper, we investigate a green flexible job shop scheduling problem (GFJSP) with the consideration of environmental factors. To formulate the GFJSP problem, a mathematical model is first established to minimize the amount of total energy-consumption. To solve the model, a kind of improved African buffalo optimization (IABO) algorithm is proposed based on the characteristics of the problem. In the proposed IABO, a two-vector solution representation method is first designed, and a population initialization method is adopted to generate the initial solutions with certain quality and diversity. Based on the original ABO, several improvement strategies are introduced to enhance the performance of the algorithm, i.e., the modified individual learning mechanism and the aging-based re-initializaiton mechanism. In addition, in order to adapt our algorithm to the scheduling problem, a discrete individual updating method is developed to ensure the algorithm search directly in a discrete domain. Finally, a number of experiments have been conducted to test the performance of the proposed IABO algorithm. The simulation data demonstrate the effectiveness of the proposed IABO for the considered GFJSP.