Hyper-heuristic Ant Colony Optimization for solving the integrated distributed permutation flow shop prob-lem and multiple compartments vehicle routing problem with simultaneous deterministic delivery and fuzzy pickup

Production and transportation are two essential activities in supply chain management, decision-makers strive to enhance the operational efficiency of these two stages to maximize business interests. In this paper, we consider the integrat-ed distributed permutation flow shop problem (IDFSP) and multiple compartments vehicle routing problem with simultaneous deterministic delivery and fuzzy pickup (IDFSP_MCVRPSDDFP). The IDFSP_MCVRPSDDFP aims to simultaneously minimize cost and carbon emissions caused by both production and transporation. To address the IDFSP_MCVRPSDDFP, we propose a hyper-heuristic ant colony optimization algorithm (HH_ACO). The HH_ACO is com-posed of two main components: a hyper-heuristic algorithm (HHA) and an ant colony optimization algorithm (ACO). To enhance the efficiency of local search, we design six heuristic operations within the low-level heuristics (LLHs). Meanwhile, the ACO is utilized to enhance the performance of the high-level heuristics (HLS) within the HHA. Experimental simulations and data analysis have validated that HH_ACO can effectively solve IDFSP_MCVRPSDDFP.