Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding.

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To solve the large-scale scheduling problem more efficiently within the requirements of the contract in shipyard, a threelayer parallel computing system was proposed. An optimized model for shipbuilding project scheduling problem was constructed under the condition of taking time and resource constraints into account. Moreover, the key techniques of proposed system were elaborated and the main steps were designed. In the first computing layer, the problem was decomposed into small parts in heterogeneous systems, reducing the problem scale; then, in the second layer, a coevolution strategy for multi-populations was put forward to improve the algorithm robustness; in the third layer, a massive parallel computing method was performed under the Graphic Processing Unit structure. Finally, through two simulation examples, the robustness and outperforming others of the improved algorithm were verified.

Aimed at enhancing the effectiveness and efficiency for offshore platform project scheduling, a multiagent collaborative scheduling model based on the analysis of distributed offshore platform project scheduling was proposed. The functional definitions and internal structure of agents in the proposed system were analyzed. Moreover, in order to explore the cooperative way of agents, communication mechanism was presented and a negotiation model was elaborated. Then, system architecture and hardware architecture were constructed to lay a foundation to develop the collaborative scheduling system. Finally, an intelligent algorithm based on Bayesian method was designed to verify the negotiation model and a prototype system was developed to test the feasibility and rationality.

Aimed at enhancing the effectiveness and efficiency for offshore project scheduling, the mathematical model of two-stage scheduling is proposed according to the offshore project characteristics discussed in this article, which is used to solve the scheduling problem of large-scale and complex project. Regarding the two-stage scheduling, a system framework with multi-agent technology is presented to facilitate the integration and cooperation of offshore project scheduling. Next, the functional definition and internal structure of each intelligent agent is designed. Moreover, two kinds of intelligent algorithms have been designed to solve the resource conflicts and task assignment, respectively. Finally, the prototype system is developed to test the feasibility and rationality.