Enhanced Interactive Ant Colony Algorithm for Solving Multi-objective Distributed Flow Shop Production and Time-Dependent Multi-compartment Vehicle Routing Integrated Optimization Problem

In this paper, an enhanced interactive ant colony algorithm (EIACA) is proposed to solve the multi-objective distributed flow shop production and time-dependent multi-compartment vehicle routing integrated optimization problem (MODFSP_TDMCVRIOP) with the objectives of minimizing total carbon emissions and total cost. In the global search phase of EIACA, a random initialization method is first employed to ensure a diverse and dispersed population, generating high-quality initial solutions and initializing the pheromone matrix. Subsequently, three ant colonies are utilized, with the first and second colonies optimizing total carbon emissions and total cost separately, while the third colony concurrently optimizes both objectives to enhance search efficiency. In the local search phase of EIACA, specific local search methods are designed to perform in-depth exploration of discovered high-quality regions. Finally, the effectiveness of EIACA is validated through simulation experiments and algorithm comparisons.