This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.

Aiming at distribution logistics planning in green manufacturing, heterogeneous-vehicle vehicle routing problems are identified for the first time with multiple time windows that meet load constraints, arrival time window constraints, material demand, etc. This problem is expressed by a mathematical model with the characteristics of the vehicle routing problem with split deliveries by order. A hybrid ant colony optimization algorithm based on tabu search is designed to solve the problem. The search time is reduced by a peripheral search strategy and an improved probability transfer rule. Parameter adaptive design is used to avoid premature convergence, and the local search is enhanced through a variety of neighborhood structures. Based on the problem that the time window cannot be violated, the time relaxation rule is designed to update the minimum wait time. The algorithm has the best performance that meets the constraints by comparing with other methods.