遗传-分布估计算法求解化工生产中一类带多工序的异构并行机调度问题

针对化工生产中广泛存在的一类带多工序的异构并行机调度问题,即部分产品需多工序加工,同时不同产品间带序相关设置时间的异构并行机调度问题(heterogeneous parallel machine scheduling problem with multiple operations and sequence-dependent setup times,HPMSP_MOSST),提出了一种遗传-分布估计算法(genetic algorithm-estimation of distribution algorithm,GA-EDA),用于优化最早完工时间(makespan)。首先,提出了一种基于GA的概率模型训练机制,用来提高概率模型在算法进化初期的信息积累量,进而提高搜索的效率;其次,设计了一种有效的GA与EDA混合策略,使得算法的全局探索和局部开发能力得到合理平衡。计算机模拟验证了GA-EDA的有效性和鲁棒性。     

基于关键工序的全局随机机器选择和改进GA求解FJSP

以FJSP的最大完工时间作为优化目标,在考虑同一工件的工序顺序约束的同时,为提高初始种群的多样性,针对FJSP的机器选择问题采用堆栈方式存储工序。P-FJSP中只有一台机器可选的关键工序能直接影响机器总负荷和工件加工时间,进而提出了一种基于关键工序的全局随机选择（GRS）初始化方法。为了避免基本遗传算法在求解FJSP时陷入局部极优而停滞,在GA算法中加入再激活（re-activation）机制,旨在重新激活种群,增加种群的多样性。最后,针对FJSP基准测试算例进行数值分析,通过初始机器选择部分的性能对比实验、不同初始方式下遗传算法求解FJSP对比实验分别验证了GRS初始化机制的有效性和所提改进算法的可靠性。     

不确定条件下中间存储时间有限多产品间歇生产过程调度

针对产品处理时间不确定条件下中间存储时间有限多产品间歇生产过程调度问题, 采用三角模糊数描述处理时间的不确定性, 通过一种模糊排序的方法建立了以最小化模糊最大完工时间的值以及不确定度作为调度目标的数学模型, 提出一种基于改进粒子群和分布估计的混合算法(IPSO-EDA)。IPSO-EDA算法在粒子群更新公式中引入基于所有粒子自身最优位置的优质个体分布信息, 提高了算法的全局搜索能力, 同时采用NEH初始化获得理想的初始解, 采用NEH局部搜索提高算法的局部搜索能力。通过正交实验设计对算法的参数进行调节, 仿真结果表明了所提出算法的有效性和优越性。     

一类流水车间调度问题的合作博弈

对多个客户参与的一类流水车间调度问题,研究客户之间以合作的方式建立联盟,通过加工任务重新排序节省生产成本。一般流水车间调度合作博弈是受限制的,提出一类加工时间和工序相关的流水车间调度问题,相应的合作博弈是平衡的,因而具有非空核。从合作博弈理论出发,以优化多客户线性成本为指标,构建了加工时间和工序相关的流水车间调度合作博弈模型。在获得最优调度排列后,提出了一种加权前后边际成本的客户成本分配的方法,证明了该分配方法是加工时间和工序相关的流水车间调度合作博弈的一个核分配。最后通过一个实例对所提出的基于合作博弈的加工时间和工序相关流水车间调度模型及成本分配方法进行了验证。     

单机多产品注塑过程的节能调度方法

为减小单机多产品注塑过程的生产总能耗,提出一种基于旅行商算法(TSP)和遗传算法(GA)的节能调度方法。研究了注塑生产总能耗的3个重要组成:产品切换能耗、过渡调整能耗和稳定生产能耗,建立了产品切换过渡的能耗模型。以单产平稳模态为节点、过渡模态为支路,建立了单机多产品过程生产总能耗的有向图模型,将单机多产品能耗优化问题转化为经典的TSP问题。采用基于遗传算法的多目标逐层优化与TSP路径寻优思想,搜索各个单产平稳生产下的最优操作参数以及多产品的最优生产顺序,以期降低生产总能耗。该方法可提高生产效率,降低生产能耗。应用研究结果验证了方法的可行性和有效性。     

基于关键工序的全局随机机器选择和改进GA求解FJSP

以FJSP的最大完工时间作为优化目标,在考虑同一工件的工序顺序约束的同时,为提高初始种群的多样性,针对FJSP的机器选择问题采用堆栈方式存储工序。P-FJSP中只有一台机器可选的关键工序能直接影响机器总负荷和工件加工时间,进而提出了一种基于关键工序的全局随机选择(GRS)初始化方法。为了避免基本遗传算法在求解FJSP时陷入局部极优而停滞,在GA算法中加入再激活(re-activation)机制,旨在重新激活种群,增加种群的多样性。最后,针对FJSP基准测试算例进行数值分析,通过初始机器选择部分的性能对比实验、不同初始方式下遗传算法求解FJSP对比实验分别验证了GRS初始化机制的有效性和所提改进算法的可靠性。     

BP神经网络与GA算法相结合的双色成型保压曲线优化

提出采用前馈神经网络（BP神经网络）与遗传算法(GA算法)相结合优化产品保压曲线,通过改善2种材料的顶出时体积收缩率,进而改善双色产品的翘曲问题。得到优化的工艺参数组合为：聚丙烯（PP）保压压力55 MPa,保压时间12.5 s;丙烯腈苯乙烯丁二烯共聚物（ABS）保压压力75 MPa,保压时间3.5 s; 模拟验证得到优化保压曲线下优化目标为 4.411,小于各实验方案; 双色产品的翘曲由原来的1.696 mm降为0.7427 mm。     

改进生物地理学算法对正丁烷异构反应模型的优化

针对生物地理学优化（biogeography-based optimization,BBO）算法在寻优过程中容易陷入早熟的现象,提出了一种基于三维变异的生物地理学优化（three-dimensional variation biogeography-based optimization,Tdv-BBO）算法。该算法是在BBO算法的基础上,引入了三维变量的变异,解决了BBO算法后期搜索动力不足的问题,加快了BBO算法的寻优速度。同时,提出将改进的Tdv-BBO算法应用到正丁烷异构反应动力学模型的优化中,对反应动力学模型的参数进行了优化和整定。仿真实验表明：改进的Tdv-BBO算法提高了个体种群的多样性,增强了算法的搜索能力,加快了寻优速度。用该方法优化得到的反应动力学模型,模型精度较高,泛化能力强;可为正丁烷异构反应的建模提供一种有效的方法。     

含有风电场和分布式光伏的微电网经济调度

绿色能源逐渐成为国家供能倡导的主流能源，但是随着大量的风电场和光伏电站并入到微电网中，其输出功率的波动间歇性变化给系统的经济调度带来了很多不确定性因素。鉴于此，构建出含风电场和光伏电站的微电网经济调度模型，对遗传算法（Genetic Algorithm,GA）进行自适应改进，用于对所提出的动态经济调度问题进行最优问题求解。实验结果显示，风电场和光伏电站并网后，算法给出的最优功能方案，较单一火电供电总费用降低了26.7%，较人为控制调度总费用下降了12.6%，证明了算法给出的方案能够降低微电网一次能源消费成本，获得更经济的调度方案。     

带修复操作整型编码遗传算法求解大规模机组组合问题

针对发电机组组合调度问题,提出了一种带修复操作的整型编码遗传算法(r-ICGA)。算法采用整数串的编码方式,有效减小了染色体的长度。同时引入一组新的修复操作来处理约束,将进化过程中产生的新个体修复成为可行个体。与罚函数约束处理方法相比,所提算法不引入惩罚项,避免了针对不可行解的经济负载分配子问题求解,节省了大量计算时间。将所提方法应用于六种不同规模的机组组合问题,仿真结果表明算法的搜索效率更高,求得的调度结果更好。随机组规模增大,算法所需执行时间近似线性地平缓增长,表明r-ICGA算法比其他方法更适合于求解大规模机组组合调度问题。     

分布式并行算法在长周期原油混输调度中的应用

为了有效求解长周期原油混输调度问题,提出了基于事件树的分布式并行算法。该方法把原油混输调度问题分解为码头调度子问题和厂区调度子问题,采用基于事件树的建模方法,并根据两个子问题的求解顺序提出了原油混输调度问题的分布式并行算法。本方法采用主从式并行结构,主节点把求解码头调度子问题所需的原油质量要求信息发送到各从节点,然后各从节点把与质量要求信息对应的码头调度最优解返回给主节点,通过综合比较两个子问题的解,从而得出最优的调度方案。实例计算表明,该并行算法可以有效减少问题的求解时间,特别是对不同常减压对原油质量要求不同时的长周期调度（如4周）问题,采用串行算法在48 h内都无法得到可行解,而采用此算法用3台计算机可以在25 h内得到最优解。     

Genetic algorithm for large-size multi-stage batch plant scheduling

This paper presents a heuristic approach based on genetic algorithm (GA) for solving large-size multi-stage multi-product scheduling problem (MMSP) in batch plant. The proposed approach is suitable for different scheduling objectives, such as total process time, total flow time, etc. In the algorithm, solutions to the problem are represented by chromosomes that will be evolved by GA. A chromosome consists of order sequences corresponding to the processing stages. These order sequences are then assigned to processing units according to assignment strategies such as forward or backward assignment, active scheduling technique or similar technique, and some heuristic rules. All these measures greatly reduce unnecessary search space and increase the search speed. In addition, a penalty method for handling the constraints in the problem, e.g., the forbidden changeovers, is adopted, which avoids the infeasibility during the GA search and further greatly increases the search speed.     

多产品间歇过程的分层次在线调度--一种数学规划与遗传算法的混合算法

In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.     

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.     

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usual y run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non-linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-I ) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta-tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.     

A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units

This paper presents a heuristic rule-based genetic algorithm (GA) for large-size single-stage multi-product scheduling problems (SMSP) in batch plants with parallel units. SMSP have been widely studied by the researchers. Most of them used mixed-integer linear programming (MILP) formulation to solve the problems. With the problem size increasing, the computational effort of MILP increases greatly. Therefore, it is very difficult for MILP to obtain acceptable solutions to large-size problems within reasonable time. To solve large-size problems, the preferred method in industry is the use of scheduling rules. However, due to the constraints in SMSP, the simple rule-based method may not guarantee the feasibility and quality of the solution. In this study, a random search based on heuristic rules was proposed first. Through exploring a set of random solutions, better feasible solutions can be achieved. To improve the quality of the random solutions, a genetic algorithm-based on heuristic rules has been proposed. The heuristic rules play a very important role in cutting down the solution space and reducing the search time. Through comparative study, the proposed method demonstrates promising performance in solving large-size SMSP.     

用伪并行遗传/模拟退火算法进行多流股换热器网络综合的研究

The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.     

并行多家族遗传算法解多目标优化问题

提出了一种并行多家族遗传算法,采用主从节点分布式的计算策略,并应用分解协调的思想,对Pareto前沿进行分段,将计算任务分配到局域网上的多台计算机上完成,以减少计算时间。将所提出的方法用于两个化工实际问题的求解,得到的Pareto前沿的分布均匀性和全面性均优于单个遗传算法算得的结果。解决了遗传算法与流程模拟器结合解化工过程多目标优化问题时计算耗时太长的难题。