列队竞争算法求解多产品间歇生产过程排序问题

由于组合爆炸特性,多产品厂的排序问题很难求解大规模甚至中等规模的问题.采用一种新的随机型优化搜索算法--列队竞争算法来对该问题进行求解,引入新的选择策略和变异方法.计算表明同已有的方法相比,该方法求解效率高、收敛速度快、使用简单方便,是一种求解多产品间歇过程排序问题的有效算法,为多目的厂间歇过程排序研究提供了新思路.     

多目标零等待间歇生产过程多任务调度

间歇生产过程已经成为化工生产制造技术的基础和关键,该过程生产出大量产品来满足日常生活需要。然而经济全球化使得传统的化工生产过程产业面临严重的挑战。为了保持竞争力,每个企业必须优化生产技术,加强管理。其中调度是化工企业生产管理的核心技术。针对化工过程自身的多目标和零等待等特性,研究了间歇生产过程中的多目标零等待多任务调度,并提出该问题的模型和优化方法。通过将该问题分解成两个子问题来解决：采用非延迟非秩序混合方法来解决时间表分配问题以及用带存储的完全局部搜索解决排序问题。此外多目标占优采用基于SPEA2中的策略。通过大量算例的仿真实验证明了该算法的可行性和有效性。     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法,其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值,缺乏科学依据,从而大大影响了算法的收敛速度。针对这一问题,提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略,首先利用粒子群优化对间歇化工过程最优控制量进行求解,结果作为控制变量参数化方法初始给定轨迹,进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例,仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法，其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值，缺乏科学依据，从而大大影响了算法的收敛速度。针对这一问题，提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略，首先利用粒子群优化对间歇化工过程最优控制量进行求解，结果作为控制变量参数化方法初始给定轨迹，进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例，仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

单产品间歇化工过程设计

本文修正了单产品间歇化工过程设计的数学模型并提出该问题的启发算法。该算法包括设备尺寸计算与过程综合。将实例计算结果与文献值进行了比较，证实了方法的正确性。     

考虑环境影响的间歇过程多目标最优化设计

同时考虑费用和环境影响的间歇化工过程多目标最优化设计问题的求解,通常的做法是使用权重系数法,将其转变成单一目标来优化。但大多数情况下,这种权重系数很难确知。因此,有必要提供多个解以便于设计者作出合理的最终选择。采用多目标遗传算法和线性规划相结合的方法求解出间歇化工过程优化设计模型的非劣解集,并与不同权重系数下的单目标算例进行了比较。结果表明,用多目标遗传算法求解间歇化工过程是有效的。这为设计者在间歇化工过程最优化研究考虑环境因素的决策提供了更多的选择。     

粒子群算法求解边值固定的化工动态过程优化问题

针对化工过程系统优化中广泛存在着边值固定的动态优化问题,该问题的求解数学上还没有有效的方法,现今的方法之一是将问题转化为多目标优化问题.本文在粒子群优化(PSO)算法的基础上,提出在PSO算法中加入惩罚项,同时对局部极值与全局极值作进一步的调整,使PSO算法适用于求多目标优化问题理想有效解,该算法对多目标问题起到边优化边求理想有效解的功效;即只用一步即可求理想有效解,这使得在求解速度上大为加快.最后将其用于间歇反应器的最佳反应温度边值固定动态优化控制的实际运用中,取得良好效果.     

考虑能耗影响的多产品间歇化工过程优化排序

提出一种用于间歇生产的多产品化工厂排序的多目标优化的混合整数非线性规划(MINLP)模型,其目标函数同时考虑了总生产时间最短和能耗最小的影响,定义了关于过程能耗的影响因子及决策因子,用以对总生产时间和能耗的影响进行权衡。采用改进的模拟退火算法(SA)对具有不同决策因子和能耗影响因子情况下的算例进行了求解,结果表明,该模型能够较好地反映能耗因素在多产品厂排序问题中的影响,使排序结果达到生产时间和能耗影响的综合最优。     

有并行设备的多目的间歇生产调度

针对有并行设备的多目的间歇生产调度问题,建立了以所有订单生产步骤排序和订单各阶段所选用分配规则为决策变量的调度优化模型,并提出了列队竞争算法求解该模型的有效个体表示方法及变异操作策略。实例计算表明:所提出方法的求解效率优于文献中所报道的方法,特别在求解有多台并行设备的调度问题时能得到比文献更好的结果,表现出其求解大规模复杂多目的调度问题的潜力。     

多目的间歇化工过程最优设计——SA/LP算法

提出了具有多条生产路线的多目的间歇化工过程最优设计混合整数非线性规划(MINLP)模型,该模型允许同时设立同步、异步平行单元以及中间储罐,并允许设备尺寸离散变化.在结合模拟退火(SA)和线性规划(LP)的基础上提出了可求解上述MINLP问题的SA/LP算法,该算法结合了SA全局收敛性好和LP可处理连续变量与约束方程的优点.计算表明,上述模型与算法实施简便,得到了文献算例中未得到的全局最优解,且在计算速度、内存占用上都远远优于文献中的方法.     

考虑环境影响的多产品间歇化工过程优化排序

提出一种用于多产品厂排序的多目标优化模型 ,其目标函数同时考虑了总生产时间和废物对环境的影响 ,定义了关于废物的环境影响因子及决策因子 ,用以对总生产时间和废物及其对环境的影响进行权衡 .采用改进的模拟退火算法对具有不同决策因子和环境影响因子情况下的算例进行了求解 ,结果表明 ,该模型能够较好地反映环境因素在多产品厂排序问题中的影响 ,使排序结果达到生产时间和环境影响的综合最优     

A novel adaptive surrogate modeling‐based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations

A novel adaptive surrogate modeling‐based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed‐integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece‐wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191–4209, 2015     

Scheduling of serial multiproduct batch processes via simulated annealing

We present an in-depth study of the simulated annealing approach to the scheduling of serial multiproduct batch processes under the assumption of a permutation schedule. Four versions of the simulated annealing algorithm are studied based on two move acceptance criteria, the Metropolis algorithm and the Glauber algorithm, and two annealing schedules, the exponential schedule and the Aarts and van Laarhoven schedule. Makespan minimization is performed for unlimited intermediate storage (UIS), no intermediate storage (NIS), zero wait (ZW), finite intermediate storage (FIS) and mixed intermediate storage (MIS) flowshop problems using simulated annealing and also the idle matrix search (IMS) heuristic. Of the four versions of the simulated annealing algorithm studied, the Metropolis algorithm with the Aarts and van Laarhoven annealing schedule is found to give the best results, with all four versions giving significantly better results than the IMS heuristic. The Metropolis algorithm with the Aarts and van Laarhoven annealing schedule is studied in more detail for further comparison with the IMS heuristic in terms of the computational effort expended by the simulated annealing algorithm and the solution quality obtained.     

A hybrid system of heuristics and simulated annealing for large size scheduling problems of batch processes with pipeless intermediate storage

This paper describes a new scheduling solution for large number multi-product batch processes with complex intermediate storage system. Recently many batch chemical industries have turned their attention to a more efficient system known as a pipeless batch system. But existing plants need to change their systems to pipeless systems, piece by piece. In this case, current systems are changed to pipeless systems by way of non critical process operations such as through the use of intermediate storage. We have taken the conventional batch plant with a pipeless storage system as an objective process. Although the operation of a pipeless storage system becomes more complex, its efficiency is very high. With this system, all of the storage should be commonly used by any batch unit. For this reason, solving the optimal scheduling problem of this system with a mathematical method is very difficult. Despite the attempts of many previous researches, there has been no contribution which solves the scheduling of intermediate storage for complex batch processes. In this paper, we have developed a hybrid system of heuristics and Simulated Annealing (SA) for large multi-product processes using a pipeless storage system. The results of this study show that the performance and computational time of this method are superior to that of SA and Rapid Access Extensive Search (RAES) methods.     

ZW多产品间歇过程调度及在线调整

针对多产品间歇过程调度提出了分层递阶的Petri网建模方法，利用赋时Petri和Petri网的简化技术描述不同层次的生产问题，具有很强的模型描述能力。另外，在调度决策层可以方便地集成优化策略和在线调整算法，使问题求解更加灵活。     

改进的自适应模拟退火算法及其在过程综合中的应用

为有效解决化工过程综合中的MINLP问题，针对连续变量的模拟退火算法搜索慢的缺点，提出了一种改进的自适应模拟退火算法（Adaptive Simulated Algorithms,ASA)，采取自适应调整温度和搜索步长两种策略，大大加快搜索速度，提高最优解的质量。实算结果充分体现了所提出算法的优点，并很好地应用于化工过程综合问题。     

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.     

Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns

The simultaneous consideration of economic and environmental objectives in batch production scheduling is today a subject of major concern. However, it constitutes a complex problem whose solution necessarily entails production trade‐offs. Unfortunately, a rigorous multiobjective optimization approach to solve this kind of problem often implies high computational effort and time, which seriously undermine its applicability to day‐to‐day operation in industrial practice. Hence, this work presents a hybrid optimization strategy based on rigorous local search and genetic algorithm to efficiently deal with industrial scale batch scheduling problems. Thus, a deeper insight into the combined environmental and economic issues when considering the trade‐offs of adopting a particular schedule is provided. The proposed methodology is applied to a case study concerning a multiproduct acrylic fiber production plant, where product changeovers influence the problem results. The proposed strategy stands for a marked improvement in effectively incorporating multiobjective optimization in short‐term plant operation. © 2012 American Institute of Chemical Engineers AIChE J, 59: 429–444, 2013