Critical Block Simulated Annealing for Job Shop Scheduling

Abstract

The Job Shop Scheduling Problem is one of the most difficult NP-hard combinatorial optimization problems. This research investigates finding near optimal schedules using simulated annealing and a recently developed schedule permutation procedure. New schedules are generated by permuting operations within existing schedules. Simulated annealing (SA) probabilistically chooses one of the new schedules and probabilistically accepts or rejects it, allowing importance sampling search over the job shop schedule space. The initial and final temperatures are adaptively determined a priori, and a reintensification strategy improves the search by resetting the current temperature and state.
Experimental results show this method, possessing the simplicity and flexibility of SA, can find near optimal schedules for the difficult benchmark problems and can outperform the existing SA adjacent swapping approach as applied to job shop scheduling problems.