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

TL;DR

This work tackles the energy-efficient distributed homogeneous flow shop scheduling problem (EEDHFSSP) by formulating a multi-objective model that minimizes makespan $C_{max}$ and total carbon emissions $CE$. It introduces a knowledge-driven memetic algorithm (KDMA) with collaborative initialization, problem-specific variation, a local search based on key factories, and a carbon-reduction strategy to balance exploration and exploitation. Extensive experiments on 450 instances show that KDMA outperforms NSGA-II, MOEA/D, and WASFGA in solution diversity and convergence, with notable improvements in spread, GD, and IGD, and a clear reduction in emissions when employing the carbon strategy. The results demonstrate practical potential for energy-efficient scheduling in distributed manufacturing and provide a foundation for future integration of machine learning to further enhance algorithm evolution and feature-driven optimization.

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.

Figures (12)

• Figure 1: Process flow diagram of distributed flow shop scheduling
• Figure 2: Flowchart through the KDMA
• Figure 3: PMX operator selection operation
• Figure 4: PMX operator swap operation
• Figure 5: PMX operator determining conflicting jobs operation