| Equipment manufacturing industry is the basic industry for national economy and defense, as well as a symbol for overall national strength. However, China's equipment manufactories are facing serious problems like poor capability of quick response to the changing markets, over-long product development cycle, incompleteness of product sets, long delivery time, and so on. These problems, which can be eventually traced back to forward materials handling, have become one of the important reasons of losing ground for China's equipment manufactories in the international and domestic competitions. The developed contries have been able to get these problems settled properly, whereas China's equipment manufactories are in such a low level of development and information technology, that it is very difficult to solve these problems in a short term by copying foreign models. There is an urgent need to study effective solutions, compatible to China's actual situation, in order to promote cluster networked manufacturing, to enhance China's equipment manufactories competitiveness.In this dissertation, based on the 2005 year's cooperative project"ZMJ's Precision Planning and Control"between Zhangjiakou Coal Mine Machinery Co., Ltd.(ZMJ) and Hebei University of Technology(HEBUT), as well as the 2006 year's guidance project"Intelligence Supporting Methods for Large Equipment Manufactories'Precision Production Planning"from Hebei Provincial Science & Technology Department, the current study situation and problems remained in topics about forward materials handling in China's equipment manufactories are analyzed through literature review, an on-site study is conducted in a selected large equipment manufactory from China National Coal Group Corporation, and henceforth from the perspective of overall forward materials handling, a solution idea using intelligence supporting methods for dynamic balance to forward materials handling is proposed, and the intelligent balance model compatible to China's situation for forward materials handling in equipment manufactories is put forward. Furthermore, the overall framework of the dissertation is firstly developed, follwed by comments about key technologies to be used, then issues discussed sequentially including orders sorting method, orders-oriented project organization design, parallel process reengineering of design and manufacturing, rapid design system for deformable parts, intelligent lead-time estimating system, and the design of intelligent workshop scheduling support system.Major conclusions:(1) The manufactory in discussion is a typical one-of-a-kind production (OKP) enterprise; its weak control on order progress is the main problem. Therefore, a solution is proposed with focus on orders, by establishing orders-oriented project organization, strengthening orders tracking control across strategic businee units, to ensure matching orders completed on due time.(2) The changing market leads to frequent changes in corporate technical data and ERP is still lack of dynamic information support, this intensified the manufactory's difficulties for comprehensive information system and response to the changing market. This article holds that the relative studies should focus on intelligent dynamic data generation solutions to support ERP.(3) Studies conducted in this dissertation about orders priority sorting, parallel flow of design and manufacturing, rapid design, lead-time estimating methods, workshop scheduling, are verified to achieve the desired objectives. Major innovation view-points:(1) Rapid design methods for deformable parts using product family data management and similarity identification approach;(2) Improved RBF-neural networks training methods using fuzzy clustering approach;(3) A scheduling model solver for limited capacity workshop using mixed genetic algorithm on the basis of simulated annealing algorithm and multiple-population genetic algorithm (MPGA).
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