摘要
近年来,随着钢铁行业竞争的日益加剧,降低生产成本、提高产品质量成为钢铁企业追求的目标。热轧是钢铁生产过程中的关键工序之一,其中存在两个重要的优化问题：热轧生产计划与负荷分配。优化热轧生产计划对提高生产效率、降低生产成本有着重要的意义,而优化热轧负荷分配则是提高产品质量的重要途径。因此,研究热轧生产计划与负荷分配的优化方法具有重要的现实意义。
热轧生产计划与负荷分配本质上属于多目标优化问题,以往文献大都通过加权法将其转化为单目标优化问题,然后使用单目标优化算法求解,存在的问题是目标权重往往很难确定,特别是在目标数量级不一致的情形下。鉴于单目标优化方法的局限性,本文使用多目标群智能算法来优化热轧生产计划与负荷分配问题,不仅避免了目标权重的选择,且算法一次运行便可产生多个Pareto最优解,给决策者带来了更大的决策自由度。
针对热轧生产计划与负荷分配问题,首先设计了一种基于Maximin适应度函数的多目标群智能算法框架,其基本思想是在保证优化性能的同时,尽量降低算法复杂度。在多目标群智能算法中,适应度分配、精英保留、多样性保留是最重要的三个机制,该算法框架利用Maximin适应度函数具有的“识别”非支配解与“奖励”分散解、“惩罚”聚集解的特性,在对其改进的基础上,使用Maximin适应度函数进行了适应度分配、精英保留与多样性保留,并提出了一种混合多样性保留策略,提高了算法的多样性能,同时通过引入一个二维数组存储Min适应度,使得该算法框架的计算复杂度降低为O(mN2),体现了“以空间换取时间”的思想。
针对热轧生产计划优化问题,本文将其归结为多目标奖金收集车辆路径问题(PCVRP)模型,该模型兼具板坯选择与排程的功能。在此基础上提出了一种Pareto最大最小蚂蚁系统算法(P-MMAS)来优化该模型。P-MMAS在最大最小蚂蚁系统算法的基础上,重新设计了状态转移策略、信息素更新策略、局部搜索策略及信息素平滑机制,并使用基于Maximin适应度函数的多目标群智能算法框架进行适应度分配、精英保留与多样性保留。在此基础上,设计了热轧生产计划的多目标优化方法：首先在满足轧制规程约束的基础上,使用P-MMAS算法在最小化相邻板坯间跳跃惩罚的同时,使收集的奖金尽可能多(即尽可能处理优先级高的板坯),由此得到一组非支配解；然后使用TOPSIS多目标决策方法选择其中的一个解作为最终的生产计划。
针对热轧负荷分配问题,本文建立了综合考虑轧制力裕量均衡、轧辊磨损控制与板形控制等因素的多目标优化模型,并提出了一种基于局部搜索的多目标粒子群算法(LS-MOPSO)进行优化。该算法在多目标优化中引入局部搜索策略,通过构造局部搜索聚合函数,并使用数学规划方法求解以加速算法收敛到Pareto最优解。此外,使用基于Maximin适应度函数的多目标群智能算法框架进行适应度分配、精英保留与多样性保留。为避免粒子群算法早熟收敛,算法中引入了高斯变异算子。最后,为处理负荷分配优化模型中的约束条件,提出了一种有效的约束处理方法。研究表明使用LS-MOPSO不仅能获得比经验负荷分配方法更好的解,而且还能揭示负荷分配模型不同目标间的矛盾关系,体现了热轧负荷分配多目标优化方法的优越性,也验证了LS-MOPSO算法解决实际问题的有效性。
In recent years, with the increasing competition in the steel industry, most of the ironand steel enterprises want to reduce production costs and improve product quality. Hotrolling process is one of the key processes in the steel production, and it has two importantoptimization problems: hot rolling production planning (HRPP) and load distribution. TheHRPP problem has an important impact on production efficiency and production costs,while load distribution is an important way to improve product quality. Therefore, to studythe optimization methods for the HRPP and load distribution problems has a practicalsignificance.
The HRPP and load distribution essentially belong to the multi-objective optimizationproblems. Most of the previous methods belong to single objective optimization based onthe weighted-sum approach. However, it is difficult to determine the weight coefficients inpractice, especially when the objectives have different orders of magnitude. Therefore, weadapt multi-objective swarm intelligence algorithm (MOSIA) to optimize the HRPP andload distribution problems. The MOSIA can not only avoid the selection of weightcoefficients, but also generate more than one Pareto-optimal solution in one run, whichprovides more decision-making flexibility for decision-makers.
As for the HRPP and load distribution problems, a MOSIA framework based on theMaximin fitness function is proposed to improve the performance and decrease thecomputational complexity. In the MOSIA, fitness assignment, elitism preservation anddiversity preservation are three most important mechanisms. This framework assignsfitness, preserves elitism and diversity based on the modified Maximin fitness function. Inorder to improve the diversity performance, a hybrid diversity preservation strategy is alsoproposed. Meanwhile, by introducing a two-dimensional array to store the min fitnesses,the computational complexity of the framework decreases to O(mN2), which reflects the idea of trading space for time.
In this dissertation, the HRPP problem is formulated as a multi-objective prizecollecting vehicle routing problem (PCVRP) model, which considers the selection processof the candidate slabs. Moreover, a Pareto max-min ant system algorithm (P-MMAS) isproposed to solve this model. On the basis of MMAS, P-MMAS modifies the statetransition rule, the pheromone updating rule, the local search rule and pheromone trailsmoothing mechanism, as well as employs the MOSIA framework based on the Maximinfitness function to preserve elitism and diversity. Then, a multi-objective optimizationmethod for the HRPP problem is proposed. Firstly, P-MMAS is used to minimize thepenalties caused by jumps between adjacent slabs, and maximize the prizes collectedsimultaneously. Then a multi-objective decision-making approach based on TOPSIS isused to select the final rolling batch from the Pareto-optimal solutions.
In this dissertation, a multi-objective load distribution model that takes into accountthe rolling force margin balance, roll wear ratio and strip shape control, is presented. Then,a local search based multi-objective particle swarm optimization algorithm (LS-MOPSO)is used to solve this model. It introduces a local search strategy into the multi-objectiveoptimization, and adapts the mathematical programming method to approach thePareto-optimal solutions quickly. Moreover, it also employs the MOSIA framework basedon the Maximin fitness function to preserve elitism and diversity. Meanwhile, a Gaussianmutation operator is introduced to avoid LS-MOPSO premature convergence. Finally, anefficient constraint handling method is proposed to handle the constraints in the loaddistribution model. The simulation results based on practical production data indicate thatLS-MOPSO can not only achieve a better performance in comparison with the empiricalsolution, but also find the conflicting relationship between different objectives, whichreflects the advantage of multi-objective load distribution optimization and theeffectiveness of LS-MOPSO.
引文
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