Energy-efficient distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent...

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Artificial Intelligence

Background:

• Increasing energy costs necessitate energy-efficient manufacturing scheduling.
• Distributed heterogeneous re-entrant hybrid flow shop scheduling problems (DHRHFSP) with sequence-dependent setup times (SDST) present significant optimization challenges.
• Factory eligibility constraints and time-of-use (TOU) electricity pricing add complexity to scheduling.

Purpose of the Study:

• To develop an effective algorithm for the distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence-dependent setup times (DHRHFSP-SDST).
• To optimize both makespan (production time) and total energy consumption under TOU pricing.
• To account for factory eligibility constraints in a distributed manufacturing environment.

Main Methods:

• A multi-objective Artificial Bee Colony Algorithm (MOABC) was developed.
• A hybrid initialization method was employed for population setup.
• An energy-saving operator using a right-shift strategy was designed to manage TOU pricing.
• Specialized crossover, mutation, neighborhood search operators, and food source generation strategies were created, considering distributed, heterogeneous, and eligibility constraints.

Main Results:

• The proposed MOABC effectively optimizes makespan and total energy consumption for DHRHFSP-SDST.
• The energy-saving operator successfully reduces energy costs by avoiding high-price periods without compromising productivity.
• The algorithm demonstrates strong performance in handling complex constraints, including factory eligibility and distributed environments.

Conclusions:

• The developed MOABC is a viable and effective approach for solving the complex DHRHFSP-SDST.
• The algorithm offers significant potential for energy savings in manufacturing scheduling under dynamic electricity pricing.
• This research contributes a novel solution for optimizing distributed, heterogeneous manufacturing systems with sequence-dependent setup times.