Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Computational Intelligence

Background:

• Distributed flow-shop scheduling presents challenges in optimizing job assignment and sequencing across multiple factories.
• Minimizing both makespan (completion time) and total energy consumption is crucial for efficient manufacturing operations.
• Lot-streaming adds complexity by allowing jobs to be split and processed in stages.

Purpose of the Study:

• To develop a biobjective mathematical model for the distributed flow-shop scheduling problem with lot-streaming.
• To propose an improved Jaya algorithm to efficiently solve this complex scheduling problem.
• To enhance the Jaya algorithm's performance for makespan and energy efficiency.

Main Methods:

• Development of a biobjective mathematical model.
• Implementation of an improved Jaya algorithm incorporating Nawaz-Enscore-Ham (NEH) initialization.
• Design of specialized strategies for job-factory assignment, makespan minimization, and energy efficiency.

Main Results:

• The improved Jaya algorithm effectively addresses the distributed flow-shop scheduling problem with lot-streaming.
• Experimental results on 120 instances validate the performance of the enhanced strategies.
• The algorithm demonstrates high competitiveness in achieving optimal makespan and total energy consumption.

Conclusions:

• The proposed improved Jaya algorithm is a highly effective method for solving distributed flow-shop scheduling problems.
• The integration of specific strategies significantly enhances the algorithm's performance for makespan and energy objectives.
• This research contributes a competitive approach for optimizing complex manufacturing scheduling scenarios.