In order to solve the uncertainty problem of blood supply chain network design, a multi-objective robust optimization design model of blood supply chain network was established. Firstly, for the blood supply chain network with five nodes, an optimization function considering safe stock, minimum cost and shortest storage time was established, and the ε-constraint, Pareto optimization and robust optimization method were used to deal with the established model, so that the multi-objective problem was transformed into a single objective robust problem. Secondly, by improving the original Whale Optimization Algorithm (WOA), the concept of crossover and mutation of the differential algorithm was introduced to WOA to enhance the search ability and improve the limitations, so as to obtain the Differential WOA (DWOA), which was used to solve the processed model. Finally, a numerical example verified that the shortage of the robust model is 76% less than that of the deterministic model when the test problems are the same. Therefore, the optimization robust model has more advantages in dealing with demand shortage. Compared with WOA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), DWOA has shorter interruption time and lower cost.

To improve the production/distribution efficiency of perishable products with short lives under Make-To-Order (MTO) mode, considering the operational costs of business and customer demand for freshness degree of delivered products, a bi-objective model was established to coordinate the production scheduling and vehicle routing with minimum freshness limitations, which aims to minimize the total distribution cost and maximize the total freshness degree of delivered products. And an elitist nodominated sorting genetic algorithm with chromosomes encoded by two substrings was devised to optimize the proposed model. Firstly, the customers' time windows were described and freshness degrees of delivered products were defined with average degree level for multiple kinds of products. The bi-objective model was constructed to schedule production and delivery simultaneously. Then, the hard constraints and two objective functions were transformed. Chromosomes were encoded by two substrings and the computation framework of elitist nodominated sorting genetic algorithm with some key operators was adopted to solve the proposed model. Finally, the proposed algorithm was tested with the comparison of Pareto based simulated annealing on a numerical example. The simulation results show that the two objectives have a trade-off conflict and the proposed algorithm can provide Pareto optimal solutions. The sensitivity analysis of minimum limitation of freshness degree demonstrates that the two objectives are affected significantly when fewer vehicles are put into use.

For the resource allocation problem of automatic guided vehicle system, which was composed by both task assigning and route scheduling, a model based on the automatic in-put and out-put system of warehouse was built, and the algorithm with the framework of Particle Swarm Optimization (PSO) and the process of conflict-free routing was proposed to overcome the shortages of just assigning the tasks in sequence. Firstly, the iteration processes were used to search for the optimal scheme of assigning task. Then, the conflict-free routing was employed to obtain the result of resource allocation. Some constraints were added into the solution evaluation mechanism, such as time window, workload balance and conflict-free routes to ensure that the final scheme was feasible. Through the simulation of an automatic in-put system, the traditional scheduling algorithm and the new algorithm were compared. The proposed algorithm can save 10% of the total travelling distance and its balance of task assigning is better. It means that the proposed solution can improve the efficiency of whole system.