Improvement artificial bee colony meta-heuristic for solving manufacturing flow-shop scheduling problem under blocking constraint

Manufacturing flow-shop scheduling problem is a highly attractive optimization challenge in operations research. When there is limited space in a workshop or capacity queue of machine is null, a new problem called manufacturing blocking flow-shop scheduling problem (MBFSP) is investigated. This optimization problem is solved by considering that the job must be blocked during scheduling when the next machine is not available. The address this challenge effectively, we present two novel algorithms: the Iterative Local Search (ILS) with neighborhood solutions, and the Artificial Bee Colony (ABC) algorithm inspired by nature’s meta-heuristics. These cutting-edge approaches are designed to address the problem from distinct and innovative perspectives, with a primary focus on optimizing the overall quality of the solution. By embracing these advanced techniques, we can effectively expand the scope of potential outcomes, opening up new avenues for achieving superior results. The target is to decrease the total weighted flow time (TWFT) , which represents the duration of completing all tasks. We provide a mathematical statement of the MBFSP optimization case and propose two solution meta-heuristics. In order to comprehensively assess the effectiveness of the proposed algorithms, we conducted a rigorous comparative study. This study involved an extensive set of instances, carefully curated to encompass a wide range of scenarios with varying numbers of jobs and machines. By testing the algorithms across diverse instances, we aimed to obtain a holistic understanding of their performance. Simulation results show that the ABC algorithm overcame the ILS algorithm, with a hit ratio 88% over the set of test instances and a minimum mean relative deviation in percent of 0.0154%.