Just-in-time scheduling in identical parallel machine sequence-dependent group scheduling problem

In this research, a parallel machine sequence-dependent group scheduling problem with the goal of minimizing total weighted earliness and tardiness is investigated. First, a mathematical model is developed for the research problem which can be used for solving small-sized instances. Since the problem is shown to be NP-hard, this research focuses on proposing meta-heuristic algorithms for finding near-optimal solutions. In this regard, the main contribution of this research is to apply the Biogeography-based Optimization (BBO) algorithm as a novel meta-heuristic and Variable Neighborhood Search (VNS) algorithm as a best-known one. In order to evaluate the mathematical model and solution methods, several computational experiments are conducted. The computational experiments demonstrate the efficiency of the proposed meta-heuristic algorithms in terms of speed and solution quality. The maximum gap of BBO algorithm is 1.04% and for VNS algorithm, it is 1.35%.