批处理过程优化调度研究综述——2.MILP调度

详细阐述MILP建模的关键技术和现有的有关参考文献,同时给出MILP调度实施框架和已经报道的成功应用。在此基础上,指出进一步的研究方向。     

抗生素发酵过程优化调度模型的研究

对抗生素多罐并行发酵过程进行了分析,将任务、设备和事件之间的分配关系表达为两类0-1变量,建立了一个基于连续时间的抗生素多罐并行发酵过程优化调度的M ILP(混合整数线性规划)模型。该模型整数变量少,求解速度快,并给出了最短生产时间和罐批最优生产序列。最后以头孢菌素发酵过程为例证明了此调度模型的可行性和有效性。     

多产品多级连续化工过程生产调度的研究

对多产品多级连续化工过程的生产调度问题进行了研究,考虑了设备清洗和设备生产效率下降对生产过程的影响.依据该过程具有组合和连续性特点,将该调度模型归结为一混合整数非线性规划问题(MINLP),以利润最大为目标,采用改进的遗传算法对该模型进行了求解.用一个三产品两级连续过程的例子说明了该算法的有效性.     

基于TPN模型的间歇过程GA优化调度方法研究

为了建立间歇工业过程的优化调度策略,研究了采用TPN(赋时Petri网)建立其生产调度模型的方法,并提出了基于GA(遗传算法)对此TPN调度模型进行优化的技术。论文引入了一个青霉素发酵间歇过程的仿真平台,以此为实例对象,讨论了TPN调度建模及GA优化的具体步骤,给出了满意的优化调度计算结果。     

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.     

An MILP Model for Optimal Scheduling of the Lubricant Production Plant

In this article we are developing a model that can be used for determining the optimal production schedule in a lubricant production plant. The model includes all the main stages in the lubricant production process, contains both continuous and binary variables, and results in the formulation of a mixed integer linear programming (MILP) problem that is solved using standard optimization techniques. The model can be easily adapted to any lube production facility, thus providing a valuable tool to refineries in their effort to automate the production scheduling process. The proposed tool can save valuable time and resources by eliminating the time-consuming search for a feasible production plan that production engineers go through in order to meet production demands.     

Modeling and Optimization for Short-term Scheduling of Multipurpose Batch Plants

In the past two decades, short-term scheduling of multipurpose batch plants has received significant attention. Most scheduling problems are modeled using either state-task-network or resource-task-network (RTN) process representation. In this paper, an improved mixed integer linear programming model for short-term scheduling of multipurpose batch plants under maximization of profit is proposed based on RTN representation and unit-specific events. To solve the model, a hybrid algorithm based on line-up competition algorithm and linear programming is presented. The proposed model and hybrid algorithm are applied to two benchmark examples in literature. The simulation results show that the proposed model and hybrid algorithm are effective for short-term scheduling of multipurpose batch plants.     

Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed‐integer dynamic optimization problem or a large‐scale mixed‐integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed‐integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2379–2406, 2013     

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。     

A general framework for process scheduling

Existing methods for process scheduling can be broadly classified as network‐based or sequential. The former are used to address problems where different batches of the same or different tasks are freely mixed or split, whereas the latter are used to address problems where batch mixing/splitting is not allowed. A framework is proposed that allows us to: (1) express scheduling problems in facilities that consist of network and sequential, as well as continuous subsystems, (2) formulate mixed‐integer programming (MIP) scheduling models for such problems, and (3) solve the resulting MIP formulations effectively. The proposed framework bridges the gap between network and sequential approaches, thereby addressing the major formulation challenge in the area of process scheduling, namely, the development of a framework that can be used to address a wide spectrum of problems. © 2010 American Institute of Chemical Engineers AIChE J, 57: 695–710, 2011     

基于知识和多代理的间歇生产过程智能调度

针对间歇生产过程柔性操作和动态变化的特征 ,提出综合应用基于知识的调度算法和多代理技术实现间歇生产过程智能调度的方法 ,建立间歇过程智能调度的结构模型 ,并以一间歇过程示例的仿真研究说明所提出方法的思路和可行性     

间歇化工过程和模型优化调度

Chemical batch processes have become significant in chemical manufacturing.In these processes,large numbers of chemical products are produced to satisfy human demands in daily life.Recently,economy globalization has resulted in growing worldwide competitions in traditional chemical process industry.In order to keep competitive in the global marketplace,each company must optimize its production management and set up a reactive system for market fluctuation.Scheduling is the core of production management in chemical processes.The goal of this paper is to review the recent developments in this challenging area.Classifications of batch scheduling problems and optimization methods are introduced.A comparison of six typical models is shown in a general benchmark example from the literature.Finally,challenges and applications in future research are discussed.