Study On Resource Allocation Under Uncertainty Environments In Container Terminal

With the rapidly increasing of foreign trade and the development of global economy integration, container turnover becomes rapidly increasing. As a capital-dense entity, container terminal's effective resource management and allocation are the effective guarantee for expedite container flow. And they also affect firms' operation cost and the custom demand, all of which contribute to container terminal's competition. Considering the container terminal's uncertainty, complexity and dynamics in resource allocation, the paper systemically lays emphasis on the theory and method of resource allocation. The study has realistic value, for example, it is beneficial to improve container terminal's service level and increase corporation income. The researches of resource allocation under uncertainty circumvents are summarized as follows.1. Due to the shortcoming in the depiction of additivity and entirety of uncertain measure problems, the paper proposes a new uncertain measure denotation, in which additivity index (λ) and the entirety coefficient (κ) depict uncertain problems' characters. On that basis, the paper puts forward some related notions, such as uncertainty measure space, uncertainty product measure space, uncertainty variable, distribution function and distribution density function of uncertainty variable, the variable expected value, uncertainty variable independence, and uncertainty variable simulation are suggested. And the characteristics of related concepts and the determination of a joint uncertainty distribution function are discussed and proofed. Then a simple berth allocation problem with uncertainty variable in container terminal is proposed. Various uncertainties in resource allocation problem are analyzed, and the classification of uncertainties, the mathematical description of uncertainties, the mathematical models for resource allocation and the optimization methods are studied in details. Further, the program of uncertain neural network optimal model in the uncertain environment is given, and the intelligent algorithm of the uncertain program is given as well.2. Based on analysis of the berth and quay crane allocation problem in container terminal, the paper proposes a berth and quay crane allocation uncertainty programming model in uncertain chance constraint. Considering the characteristics of its optimal solution, the author puts forward improved beam search algorithm and genetic algorithm to solve the above model. Genetic algorithm and neural network are combined to simulate uncertainty function and a mixed intelligent algorithm is proposed for the model.3. Considering the characteristics of container storage management, storage space allocation problem is divided into two steps: storage block allocation and container location determination. According to storage block allocation criterion, uncertainty expected value model I and II are proposed. Model I aims to balance the workloads among storage blocks, and model II is to stack the same group containers as on the same block as possible and to decrease the distance of conveying containers between the storage and quay area. The method dependent of uncertainty chance to calculate reshuffle is discussed. Then, dependent of chance uncertain 0-1 programming model of container location determination in a storage block is proposed to put the same group containers as close as possible and decrease total reshuffles of inbound and outbound flow.4. Based on the analysis of the workflow of quay-cranes, Rubber Tyred Gantry Cranes (RTGC) and trucks in container terminal, the equipment allocation problem is split into RTGCassignation among blocks and operation sequence optimization of quay-cranes, RTGCs and trucks. The uncertainty expected value programming model is proposed for RTGC assignation to reduce undone planning jobs by the end of planning period. And the beam search algorithm is discussed to solve the proposed model. The operation sequence optimization is formulated by an uncertain program model based on chance constraint, which could reflect risk preference and experience of decision-makers. Considering the model's complexity, the genetic algorithm and beam search algorithm are discussed to solve the model. And the functions dependent of chance is treated by direct predigestion and neural network simulation method respectively.5. Container terminal is a dynamic and cooperative system. The flexibly, robustly, dynamically, synthetically design and development of Multi-agent system is the necessity for container terminal allocation, through which the integrative performance can be improved and copes with the shortcoming of knowledge and information. To realize the dynamic, cooperative and integrative allocation of container terminal and to decrease the uncertainty troubles, based on uncertainty program, the paper puts forward distributed and intelligent Multi-agent system, in which agent selection, MAS architecture, communication, conflict and cooperation are discussed. In this system we use agent to integrate some optimization technologies and make them get mutual advantages, through which optimize the container terminal production process. The prototype system is developed by C# language.