Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Abstract

With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm

“…For the job shop scheduling environment, Liu et al 8 developed a multi-objective scheduling method for the classical job shop scheduling problem (JSP) considering total energy consumption and total weighted tardiness as objectives. Wang et al, 22 Lu et al, 23 and Yin et al 24 proposed scheduling model focused on reducing energy consumption and environmental pollution at the workshop level, and they developed some new algorithms to solve these problems.…”

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

“…In recent years, researchers have been showing strong interests on carbon emission reduction or energy saving in manufacturing activities and corresponding research is conducted [12][13][14][15][16][17][18][19]. For example, aiming at reducing energy cost to achieve carbon efficiency, Ding et al developed effective scheduling strategies in a permutation flow shop (PFS) [19]; they suggested a multi-objective NEH algorithm (MONEH) and a modified multi-objective iterated greedy (MMOIG) algorithm to achieve carbon emission reduction as well as makespan minimization.…”

“…Since cutting parameters affects machining efficiency and carbon emission, Liu et al [21] recently proposed a NSGA-II based optimization method to optimize carbon emission cost as well as cutting efficiency; their experimental results reveal that the cutting speed plays a more important role in carbon emission than the feed rate. Recently, Wang et al [15] developed an estimation of the distribution algorithm with a new decoding method for energy saving in a flexible flow shop environment by optimizing the spindle speed and scheduling scheme simultaneously. Meanwhile, Yin et al [14] tackled a multi-objective single machine scheduling problem that considers total earliness/tardiness minimization as well as energy consumption reduction by their local multi-objective evolutionary algorithm (LMOEA).…”