A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption...

Area of Science:

• Operations Research
• Industrial Engineering
• Computer Science

Background:

• The flexible job shop scheduling problem (FJSP) is a well-researched area.
• Multiobjective FJSP incorporating energy consumption thresholds remains under-investigated.
• Challenges include meeting energy constraints and determining optimal thresholds.

Purpose of the Study:

• To develop a novel algorithm for multiobjective FJSP with energy constraints.
• To minimize makespan and total tardiness simultaneously.
• To address the difficulty in pre-determining energy consumption thresholds.

Main Methods:

• A two-phase meta-heuristic (TPM) combining Imperialist Competitive Algorithm (ICA) and Variable Neighborhood Search (VNS).
• Phase 1: ICA solves a converted FJSP including makespan, tardiness, and energy consumption.
• Phase 2: VNS refines solutions for the original FJSP, incorporating an optimized energy threshold.

Main Results:

• The proposed TPM demonstrates strong performance on the considered FJSP.
• Extensive experiments validate the algorithm's effectiveness.
• TPM is identified as a competitive approach for this complex scheduling problem.

Conclusions:

• The developed TPM is a highly effective method for multiobjective FJSP with energy constraints.
• The approach successfully balances makespan, tardiness, and energy consumption.
• This research contributes a valuable tool for energy-aware production scheduling.