A two-stage invasive weed optimization algorithm for distributed assembly permutation flowshop problem

Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we address the DAPFSP with total flowtime minimization. According to the problem knowledge, we present a two-level representation which consists of product permutation and job sequences. A two-stage discrete invasive weed optimization (DIWO) algorithm is proposed for optimizing product permutations and job sequences, respectively. A parameter is introduced to balance the product permutation based search and job sequence based search. The problem knowledge based operators, local search procedures, and heuristics are utilized to improve the DIWO. We carry out a comprehensive computational campaign based on the 900 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithm performs much better than the existing algorithms in the literature for solving the DPFSP with the total flowtime criterion.