A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling...

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Artificial Intelligence

Background:

• The energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in heterogeneous factory systems (HFS) presents significant optimization challenges.
• Minimizing total tardiness (TTD), total energy consumption (TEC), and ensuring factory load balancing (FLB) are critical objectives.

Purpose of the Study:

• To propose a self-learning discrete Jaya algorithm (SD-Jaya) for the HFS-EEDNIFSP.
• To develop an effective evaluation criterion for FLB integrating energy consumption and completion time.
• To introduce an energy-saving strategy to reduce TEC.

Main Methods:

• A mixed-integer programming model for HFS-EEDNIFSP was formulated.
• A self-learning operator selection strategy was designed, utilizing historical success rates for operator guidance.
• An energy-saving strategy was implemented by transforming the problem and reducing operation speeds for adjacent idle times.

Main Results:

• The SD-Jaya algorithm demonstrated superior performance in solving the HFS-EEDNIFSP across 60 benchmark instances.
• Experimental results confirmed the algorithm's effectiveness in optimizing TTD, TEC, and FLB.
• The proposed energy-saving strategy effectively reduced total energy consumption.

Conclusions:

• The SD-Jaya algorithm is a highly effective approach for the complex HFS-EEDNIFSP.
• The integration of self-learning and energy-saving strategies significantly enhances scheduling efficiency and reduces energy consumption.
• This research provides a valuable contribution to optimizing manufacturing processes in heterogeneous environments.