Solving mixed integer nonlinear programming problems with line-up competition algorithm

Line-up competition algorithm (LCA), a global optimization algorithm proposed recently, is applied to the solution of mixed integer nonlinear programming (MINLP) problems. Through using alternative schemes to handle integer variables, the algorithm reported previously for solving NLP problems can be extended expediently to the solution of MINLP problems. The performance of the LCA is tested with several non-convex MINLP problems published in the literature, including the optimal design of multi-product batch chemical processes and the location-allocation problem. Testing shows that the LCA algorithm is efficient and robust in the solution of MINLP problems.     

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.     

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.     

Novel continuous-time formulations for scheduling multi-stage batch plants with identical parallel units

Scheduling production optimally in multi-stage multi-product plants is a very difficult problem that has received limited attention. While the case of non-identical parallel units has been addressed, the case of identical parallel units is equally worthy of attention, as many plants are or can be approximated as such. In this paper, we construct and compare several novel MILP formulations for the latter. In contrast to the existing work, we increase solution efficiency by considering each stage as a block of multiple identical units, thereby eliminating numerous binary variables for assigning batches to specific units. Interestingly, a novel formulation using an adjacent pair-wise sequencing approach proves superior to slot-based formulations. Furthermore, we develop heuristic variations of our proposed formulations to address moderate-size problems. A novel heuristic strategy inspired from list scheduling algorithms seems to be efficient for moderate-size problems and scales well with problem size.     

Study on Optimal Strategy of Grade Transition in Industrial Fluidized Bed Gas-Phase Polyethylene Production Process

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process. The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables, such as polymerization temperature, the ratio of hydrogen to ethylene, the ratio of co-monomer to ethylene, feed rate of catalyst, and bed level. A new performance index, the ratio of melt flow (MFR), is included in the objective function, for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resin. It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product. This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variab     

锅炉蒸汽系统多操作周期的优化调度

针对锅炉蒸汽系统必须满足外界蒸汽的需求发生周期性变化的情况,提出了每个周期的操作费用和周期之间锅炉启动和停运的转运费用的优化调度问题.针对此混合整数非线性规划问题,提出了用列队竞争算法和动态规划法分步求解的计算方法,所需的计算时间仅与周期数成正比,而且能得到全局最优解.两个实例说明了该方法的有效性.     

基于流程模拟器和列队竞争算法的精馏操作优化

精馏是高耗能的操作单元,对其进行操作优化是企业实现节能降耗、提高经济效益的有效手段。本文提出列队竞争算法与流程模拟软件相结合的精馏操作优化方法,通过MATLAB-Aspen Plus的接口工具调用Aspen Plus模拟精馏操作过程,得到目标函数值,使用列队竞争算法对其进行操作条件的优化。将所提出的方法应用于某石化企业催化裂化主分馏塔的操作优化,其中目标函数值同时考虑了热能利用与产品分布,优化求解得到了总体效益最大的优化操作方案。     

A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing

The unbalanced assignment problem (UAP) is to optimally resolve the problem of assigning n jobs to m individuals (m < n), such that minimum cost or maximum profit obtained. It is a vitally important Non-deterministic Polynomial (NP) complete problem in operation management and applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn) time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.     

Extending the resource task network for industrial applications

The discrete time resource-task network (RTN) model is a generalized mixed-integer linear programming model used in scheduling optimization problems. This paper presents several extensions to the RTN that have been used at The Dow Chemical Company. One RTN extension allows for more realistic demand fulfillment: customer orders can be filled in their entirety at a distinct time point, rather than over several time periods. Modifications are proposed that allow tasks to interact not just with resources, but also with the bounds on those resources, which allows for an efficient method of modeling storage. The concept of multiple extents is introduced to extend the functionality of a single task and thereby reduce the overall size of the model and improve computation time. An alternative formulation of the RTN is also introduced in spatial rather than temporal coordinates, which allows applicability to a different class of problems such as payload optimization.     

全局优化搜索新算法--列队竞争算法(Ⅰ)解非线性和混合整数非线性规划问题

提出了一种称为列队竞争算法(LCA)的群体搜索算法,该算法在进化过程中始终保持着独立并行进化的家族,通过家族内部的生存竞争和家族间的地位竞争这两种不同的竞争方式,使群体快速进化到最优或接近最优的区域.根据家族的目标函数值大小排列成一个列队,按列队中家族地位的不同分配不同的搜索空间,使局部搜索与全局搜索达到均衡,同时,应用逐步收缩搜索空间技术加速收敛速度.数值计算表明,列队竞争算法的搜索效率优于遗传算法和模拟退火法等算法.     

热集成变压精馏分离乙二胺-水共沸体系的模拟优化

针对乙二胺-水共沸物组成对压力较为敏感的特性,采用部分热集成变压精馏工艺分离该共沸物。先利用Aspen Plus软件对该工艺进行稳态模拟,再以理论塔板数、进料位置、回流比为优化变量,水和乙二胺的纯度为约束,以年度总费用（total annual cost,TAC）为目标函数建立乙二胺-水共沸体系分离系统的优化设计模型。采用列队竞争算法对该分离过程主要工艺参数进行优化,得到了变压精馏分离乙二胺-水体系的最佳工艺操作参数及设备参数。模拟结果表明,利用算法对多变量进行同时优化可得到更具经济效益的分离系统,与传统优化结果相比,可降低TAC约7.31%。在此基础上,对高压塔的操作压力进行优化分析,将其由2atm提升至4atm（1atm=101325Pa）,并对流程其他参数进行优化,可显著降低TAC约24.62%。进一步,采用部分热集成比普通变压双塔精馏降低TAC约21.87%     

智能约束处理技术综述

首先时函数约束优化问题进行数学描述,分析其求解难点,然后综述当今主流的一些智能约束处理技术.包括罚函数法、基于排序的方法、基于可行性规则的方法、基于多目标的方法、特殊算子、基于译码器的方法、基于文化算法的技术、修补技术以及混合策略等,最后提出了有待进一步研究的几个方面.     

全局优化搜索新算法──列队竞争算法(Ⅱ)解网络综合问题

本文给出了列队竞争算法解组合优化问题的框架和确定变异领域的两条原则 .对管路网络综合问题和换热网络综合问题确定了相应的变异领域 ,用列队竞争算法分别解这两个网络综合问题 ,所得到的最优解优于文献报道的结果 .     

工业流化床乙烯共聚合生产过程中牌号切换的最优化研究

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process.The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables,such as polymerization temperature,the ratio of hydrogen to ethylene,the ratio of co-monomer to ethylene,feed rate of catalyst,and bed level.A new performance index,the ratio of melt flow(MFR),is included in the objective function,for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resion.It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product.This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variables due to the non-linearity of the system.The grade transition model is extended to a high space time yield(STY)Process with the so-called condensed model operation.In the end,an optimization strategy for multi-product transition is proposed with two-level optimization of the objective function J(x,u) on the the basis of the optimal grade transition model.A sequential transition of six commercial polyethylene grades in illustrated for an optimal multi-product operation.     

基于全流程模拟的催化裂化装置多目标优化

应用流程模拟软件Petro SIM对催化裂化装置进行了全流程模拟,采用平均影响因子法从15个独立可调的变量中筛选出7个影响产品分布的主要优化变量,以各产品质量指标为约束,建立了液化石油气和汽油产量最大化的双目标优化模型,应用多目标列队竞争算法对该优化模型求解,所得Pareto最优解集揭示了液化石油气、汽油和柴油产量的分布规律. 根据市场对不同产品需求的变化,确定了5种优化操作方案,调整催化裂化装置的多产品方案.