Research On Multi-objective Disruption Recovery For Berth And Quay Crane Plan Based On Natural Constraint Language

Container Terminal actual berthing and handling process often appear all sorts of disruptive events, such as rob class assignments, ship delaying work, equipment failures and accidents, etc. The interference events disrupt the original berth and cranes allocation plan, which make the original plan cannot be normal execution, even is not workable. So, it needs to adjust the original plan to minimize negative impact to the system. Research on these issues have a general theoretical research significance and practical value, especially more in the berth scheduling optimization requirements of regional ports and feed ports.In this paper, disruption events occurring in container terminal berthing operations are analyzed and classified; Interferce management thought is used for reference, According to different interference factors on the berth-crane plan, crane plan adjustment strategies and berth plan strategy are worked out. On this basis, first-order mathematical logic is used on the modeling description of the problem. Customer satisfaction index, ship berthing position deviation and crane operation cost are considered in the model. Because the problem is a typical np-hard problem, natural constraint language (NCL) is adopted to solve the problem, which is the syntax of mathematical logic. Minimum degree of relaxation and sequential rules of NCL is designed; solving method is based on dictionary order.Through specific examples, the method to solve the model is verified. And the single objective interference recovery method is compared with multi-object, The results show that within the prescribed time, multi-objective berth-crane interference recovery method has certain advantages on the single objective recovery method, the the NCL rules can effectively and efficiently solve the ship berth-crane plan recovery The method can provide some guidance for practical production container terminal operations.