Abstract
The "list-based squeezing branch and bound (LSQ) algorithm" is a kind of local search algorithms, which searches neighborhood of an initial schedule in an enumerative manner by using a branching procedure in a branch and bound algorithm (B & B) in parallel. In the LSQ, some initial schedules are found first by using promising heuristic methods, and then a B & B-based parallel local search is implemented for obtaining an optimal (or a near-optimal) schedule. In this paper, the LSQ is applied to n-job, m-machine flowshop scheduling problems to minimize makespan. A new procedure for selecting promising nodes to be branched is proposed and incorporated into the searching process of the LSQ to improve the efficiency and accuracy. Numerical experiments are implemented through benchmark problems to demonstrate that the proposed method can efficiently obtain a near optimal schedule with high accuracy.
