A Knowledge-driven Memetic Algorithm for the Energy-efficient Distributed Homogeneous Flow Shop Scheduling Problem

TL;DR

This paper introduces a knowledge-driven memetic algorithm designed to optimize energy efficiency in distributed homogeneous flow shop scheduling, addressing carbon reduction goals in manufacturing.

Contribution

The paper proposes a novel KDMA that integrates collaborative initialization and multiple strategies to enhance energy-efficient scheduling in manufacturing systems.

Findings

KDMA outperforms existing algorithms in simulation tests.

The algorithm effectively reduces energy consumption in scheduling.

Enhanced search performance improves scheduling quality.

Abstract

The reduction of carbon emissions in the manufacturing industry holds significant importance in achieving the national "double carbon" target. Ensuring energy efficiency is a crucial factor to be incorporated into future generation manufacturing systems. In this study, energy consumption is considered in the distributed homogeneous flow shop scheduling problem (DHFSSP). A knowledge-driven memetic algorithm (KDMA) is proposed to address the energy-efficient DHFSSP (EEDHFSSP). KDMA incorporates a collaborative initialization strategy to generate high-quality initial populations. Furthermore, several algorithmic improvements including update strategy, local search strategy, and carbon reduction strategy are employed to improve the search performance of the algorithm. The effectiveness of KDMA in solving EEDHFSSP is verified through extensive simulation experiments. It is evident that KDMA outperforms many state-of-the-art algorithms across various evaluation aspects.