Effective heuristic for large-scale unrelated parallel machines scheduling problems

This paper is concerned with non-preemptive scheduling of large-scale unrelated parallel machines (UPM) with the objective of minimizing total weighted completion times (TWCT). We propose a sequential improvement local search algorithm using multiple-jump strategy embedded within Tabu search (TS) components for TWCT, and use a highly efficient data structure to provide a necessary and sufficient condition for local optimality of a solution. We will generate a set of large-scale test problems to evaluate the performance of proposed algorithm in term of scalability, solution quality and efficiency. The non-parametric tests of algorithm components will be used to validate the consistent performance across problem types and problem sizes in the proposed algorithm.