Operation Scheduling And Optimization Of Bulk Material Yard In Iron And Steel Industry

Iron and steel industry is an important pillar industry of the national economy. Along with the rapid development, it is also facing issues such as high material cost and high energy consumption. As the first step of the iron and steel production line, the bulk material yard which is responsible for the stroage and processing of raw material, plays an important role in cost reduction and energy conservation. However, for a long time, researches that are about the scheduling and optimization problems in bulk material yard are rarely found in the literatures. On the other hand, comparing to other procedure in the iron and steel production, the bulk material yard operation lacks for effective means of optimization. Hence, research on the operation scheduling and optimizaiton problems in bulk material yard of iron and steel industry not only expands the research area of the industrial operations, but also has important practical significance.Based on the a review of the related researches on the combinatorial optimization problems and an introcution of the operation process of the bulk material yard, this thesis focus on some main scheduling and optimizaiton problems in the bulk materal yard operation and converts them into different types of combinatorial optimization problem. The corresponding mathimatical models and algorithms are also proposed. And by combining these models and algorithms with the technologies of positioning and monitoring, an real-time monitoring and operation optimization for bulk marterial yard is also designed and implemented in a real iron and steel plant. The main contributions of this thesis are presented as follows:(1) A model for the optimization of bulk material yard storage space allocation in a finite horizon is proposed. In the model, the process in real industry is convert into a multi-objective combinatorial optimization problem via discretizing the inbound/outbound operations and partitioning the yard space and material piles, while a 2-layer decision making framework is also established to recreate the decision making process of the materail yard dispathcer. Moreover, the quantified measures for the material blending risk and the space utilization of the yard, which are the two main optimization objectives of the storage space allocation, are also defined in the model.(2) A Pareto multi-objective ant colony optimization algorithm for the storage space allocation optimization, DCACO, is proposed. In the algorithm, two separated colonies are generated to search for each single objective, under indepent pheromone update rules and a distributed/centralized framework, which improves the convergence and the spread extent of the solution set. The multi-pheromone trail matrix strategy is also implemented in the algorithm, which gives the colonies more accurate guidance for search, and improves the computational efficiency.(3) A model that describes the bucket-wheel excavator scheduling in the bulk ore blending process of iron making is proposed. By discretizing the time horizon and partitioning the yard space, eventually the bulk ore blending process is reformulated as a two stage assembly flow shop with sequence-depended setup time and limited intermediate buffer (F2|STsd, block, assembly| Cmax). Further more, the senario of muliple excavator cooperation and some other constraints in real industry are also considerd. And the model is generalized as a two stage hybrid flow shop with sequence-depended setup time, limited intermediate buffer, eligibility constraint and assembly process (HF2| STsd, Mj, block, assembly| Cmax).(4) An improved genetic algorihm iGA and an improved ant colony optimization algorithm ACOtt for solving the single-excavator scheduling in bulk ore blending process are proposed,. Either algorithm is combined with problem knowledge-based local search procedure or heuristic, which improves the both the search speed and the optimal solutions'quality.(5) An ant colony optimization algorithm ACO_AHFLB for solving the multi-excavator scheduling in bulk ore blending process is proposed. In the algorithm, the searching path of each ant is consist of mutiple sub-pathes, for searching the optimal schedulings on the parallel machines. Multiple pheromone matrices are used to improve the search effeciency. And a sub-path switch mechanism is also designed to synchronize the time horizons of all the sub-paths, in order to reduce the probability of deadlock during the solution construction.(6) A real-time monitoring & operation optimization system for bulk material yard (BMYRMOOS) is designed and implentmented in a real iron and steel plant. By combining the mathmatical models and algorithms proposed in this thesis and technologies such as the differential global positioning and 3-D graphic display, the system is capable to realize the real-time monitoring of the storage space allocation and the excavators'status, and provide optimal decision support for the operations management on the bulk material yard of iron and steel industry.