An efficient discrete artificial bee colony algorithm for total flowtime lot-streaming flowshop

The lot-streaming flow shop scheduling problem has important applications in modern industry. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem where the objective is to minimize the total flowtime. To solve this problem, a new discrete artificial bee colony (DABC) algorithm is proposed. The proposed DABC algorithm represents a solution as a discrete job permutation. It takes advantage of an efficient initialization scheme based on the NEH heuristic to generate an initial population with a certain level of quality and diversity. It also makes extensive use of the DABC-based search, that is the employed and onlooker bees use the insert and swap operations to produce neighborhood solutions and scout bees generate new solutions by searching the neighborhood of the best solution found so far. An alternative local search is used to evolve in the search space. Computational experiments and comparison results demonstrate the effectiveness of the proposed DABC algorithm over the existing hybrid genetic algorithm, threshold accepting and ant colony optimization algorithm for the lot-streaming flow shop scheduling problem.