Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

Area of Science:

• Operations Research
• Industrial Engineering
• Manufacturing Systems

Background:

• Flexible Job-shop Scheduling Problem (FJSP) is a complex optimization challenge.
• Stochastic processing times introduce uncertainty in traditional scheduling models.
• Minimizing completion time and energy consumption are critical objectives in modern manufacturing.

Purpose of the Study:

• To develop and validate a novel method for bi-objective FJSP optimization under stochastic processing times.
• To consider both makespan (completion time) and total energy consumption as optimization objectives.
• To demonstrate the practical applicability and benefits of the proposed method in an industrial setting.

Main Methods:

• Formulation of a robust counterpart model for the bi-objective FJSP.
• Application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for solving the formulated problem.
• Comparative analysis of NSGA-II against other algorithms like HPSO and PSO+SA using a case study.

Main Results:

• The NSGA-II algorithm effectively solves the bi-objective FJSP with stochastic processing times.
• The proposed method demonstrates superior performance compared to HPSO and PSO+SA.
• The optimization approach successfully balances completion time and energy consumption.

Conclusions:

• The presented method offers an effective solution for bi-objective FJSP optimization under uncertainty.
• The approach provides significant advantages in reducing energy consumption for manufacturing enterprises.
• The methodology is broadly applicable to energy-intensive industries, enabling cost-effective energy reduction.