长江三角洲港口群的功能定位探析

根据国际海洋运输船舶大型化和集装箱化的趋势，为积极应对来自亚太地区各港口的挑战，总结和分析了长江三角洲地区港口群的发展现状，即各港口都取得了较大的发展，特别是宁波和上海两港口的集装箱吞吐量增长很快。但是，上海港的发展受制于航道水深水足和缺乏深水大港，南京以下各港中存在因重复建设而导致岸线浪费和恶性竞争的问题，宁波港的主要制约在于箱源腹地有限和陆上集疏运条件亟待完善。同时，还提出了各自的解决对策。为应对我国加入WTO后国际航运面临的严峻挑战和尽快实施国家战略，该地区应当形成代表国家参与国际航运竞争的以上海为中心、苏南和浙北为两翼的组合港式国际航运中心。其中，形成合理竞争与协调分工的长江三角洲港口群则是当务之急。鉴于此，各港口的功能定位可以是，上海港要尽快建成国际级的集装箱枢纽港，长江沿岸港口（南京以下各港口）要逐步形成协同发展的内支线港和喂给港，宁波、舟山等港口要建设成为上海港的中转港和深水外港。     

Multi-objective optimization of waste and resource management in industrial networks – Part I: Model description

This article presents a general multi-objective mixed-integer linear programming (MILP) optimization model aimed at providing decision support for waste and resources management in industrial networks. The MILP model combines material flow analysis, process models of waste treatments and other industrial processes, life cycle assessment, and mathematical optimization techniques within a unified framework. The optimization is based on a simplified representation of industrial networks that makes use of linear process models to describe the flows of mass and energy. Waste-specific characteristics, e.g. heating value or heavy metal contamination, are considered explicitly along with potential technologies or process configurations. The systems perspective, including both provision of waste treatment and industrial production, enables constraints imposed upon the systems, e.g. available treatment capacities, to be explicitly considered in the model. The model output is a set of alternative system configurations in terms of distribution of waste and resources that optimize environmental and economic performance. The MILP also enables quantification of the improvement potential compared to a given reference state. Trade-offs between conflicting objectives are identified through the generation of a set of Pareto-efficient solutions. This information supports the decision making process by revealing the quantified performance of the efficient trade-offs without relying on weighting being expressed prior to the analysis. Key features of the modeling approach are illustrated in a hypothetical case. The optimization model described in this article is applied in a subsequent paper (Part II) to assess and optimize the thermal treatment of sewage sludge in a region in Switzerland.