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A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop

Kaifeng Geng¹, Li Liu², Shaoxing Wu³

¹Fan Li Business School, Nanyang Education Informationization Engineering Technology Research Center, Nanyang Institute of Technology, Nanyang, 473004, Henan, China. gkf8605@126.com.

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This study introduces a hybrid memetic algorithm (HMA) to solve the distributed two-stage flexible job shop scheduling problem (DTFJSP) considering time-of-use electricity pricing. The HMA effectively minimizes makespan and total energy consumption costs (TEC).

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Area of Science:

Operations Research
Manufacturing Systems
Artificial Intelligence

Background:

Increasing environmental challenges necessitate sustainable manufacturing solutions.
Distributed green manufacturing and flexible job shop scheduling are critical areas of interest.
Time-of-use (TOU) electricity pricing presents an opportunity for energy cost optimization.

Purpose of the Study:

To address the distributed two-stage flexible job shop scheduling problem (DTFJSP).
To minimize both makespan and total energy consumption costs (TEC) under TOU pricing.
To develop and validate an efficient optimization algorithm for this complex scheduling problem.

Main Methods:

A hybrid memetic algorithm (HMA) was developed, featuring a three-tier vector encoding and population initialization strategies.
Global search operators were tailored, and seven Q-learning-based local search algorithms were introduced.
An energy-saving operator and orthogonal experimental design were employed for parameter tuning and validation.

Main Results:

Numerical experiments confirmed the effectiveness of the proposed local search operator and energy-saving strategy.
The HMA demonstrated superior performance in terms of diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II.
The specialized components of the HMA were validated for their efficacy in solving the DTFJSP.

Conclusions:

The proposed HMA is a highly effective approach for the distributed two-stage flexible job shop scheduling problem.
The integration of Q-learning and energy-saving strategies significantly enhances scheduling efficiency and reduces energy costs.
The study validates the HMA's superiority over existing algorithms for DTFJSP under TOU pricing.