改进的生物地理学优化算法在混合流水车间调度中的应用

调度问题是将有限的资源分配给各项不同任务的决策过程,其目的是优化一个或多个目标,它广泛存在于当今大多数的制造和生产系统中。混合流水车间调度问题是一般流水车间调度问题的推广,更接近实际的生产过程。采用一种新型的算法--生物地理学优化算法求解混合流水车间调度问题,通过引入改进策略,增强了算法的全局搜索能力和局部搜索能力,并提高了算法的收敛速度。通过10个标准调度算例的仿真研究,并与遗传算法进行对比,验证了改进后的生物地理学优化算法在求解混合流水车间调度问题方面的优越性。     

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。     

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。     

改进的细菌觅食算法求解多目标FJSP问题

针对多目标的柔性作业车间调度问题,以最小化完工时间、最小化总成本为目标,建立问题的数学模型,提出了改进的细菌觅食算法。针对柔性作业车间调度问题的特点,提出基于拥挤距离的动态变步长因子,并在算法复制阶段引入繁殖阈和死亡阈。最后将改进的方法应用于Brandimarte算例和某化工设备加工车间调度问题上,与其他经典算法的结果比较验证了改进的细菌觅食算法的有效性。     

关于智能加工系统的动态调度研究与应用——基于一道工序的物料加工情况

研究生产加工的热点:智能加工系统的动态调度问题及应用。以RGV生产现场为例,基于一道工序的物料加工情况。建立动态调度模型,在规定时间,进行动态调度,最大限度地高效运转,满足更多的社会效益。此模型,还可推广至分发快递、挑拣货物等。     

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。     

基于约束规划的无等待混合流水车间调度问题研究

针对k-阶段等速机无等待混合流水车间最小化最大完工期的调度问题,提出基于约束规划的模型和求解策略.模型利用约束规划自然地表达问题的优化目标和约束条件.求解策略包括采用有限深度偏离搜索例程、采用限定失败次数策略、综合运用离散资源、一元资源和替代资源约束表达工件在各阶段对设备要求等.通过数值实验验证了约束规划方法的有效性.整个方法能够很好地满足实际应用中对计算效率和效果的要求.     

缩短GK400N型密炼机转子加工周期

通过介绍应用精益六西格玛的工具和方法,实施缩短GK400N型密炼机核心件转子的粗车工序时间、堆焊层加工工序时间以及优化相关加工工艺路线等措施,实现缩短GK400N转子的整个加工周期,最终实现缩短密炼机的整体生产周期,进而为保障GK400N型密炼机交货期、节约制造成本等提供强力保证。     

印染车间作业计划优化调度

由于基于统一离散时间表示的生产过程优化模型的约束和变量多,基于连续时间的批处理短期调度在近10年得到了广泛的重视和研究。本文简略介绍了生产过程调度模型的情况,通过对生产过程优化调度模型和印染生产工艺的研究,建立了基于连续时间印染生产过程优化调度MILP模型。然后,通过把数学模型转换成IL-OG OPL语言描述的模型,以浙江省某印染企业2个案例为数据,利用ILOG CPLEX进行求解,调度结果以甘特图的形式表达。结果表明印染生产连续时间MILP调度模型的有效性,优化了车间生产资源的配置。     

考虑调度操作安全平稳性的炼油化工生产过程调度优化

平稳运行是炼油化工企业本质安全运行、生产经济效益和挖潜增效的重要保证，然而由于原油供应以及产品需求多变，炼油化工生产装置的多操作模式运行已成为普遍现象，调度调整也日渐频繁。现有的研究方法未考虑到装置多模式切换等调度调整对平稳操作带来的影响，甚至会导致调度操作方案实际不可行。为此，提出一种考虑调度操作平稳性的炼油化工生产调度优化模型，以解决常规调度优化模型优化产生的调度调整易使生产波动从而导致调度方案不可行的难题。考虑调度操作平稳性的调度优化模型采用离散时间表示，通过操作模式的切换与装置加工速率的波动综合表征系统平稳性，建立以生产成本最低为目标的调度优化模型。为了验证所提出模型在解决实际工业问题时的有效性，采用Julia的JuMP包调用Gurobi求解器对典型案例进行仿真求解。案例仿真结果验证了所提出模型的正确性及可实施性。与常规调度优化相比，以过程动态为表征的调度操作的平稳性提高了10%以上。     

A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

一种求解阻塞型流水车间调度的离散人工蜂群算法(英文)

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.     

A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

ABSTRACT

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

Economic and environmental strategic water management in the shale gas industry: Application of cooperative game theory

In this work, a mixed-integer linear programming (MILP) model is developed to address optimal shale gas-water management strategies among shale gas companies that operate relatively close. The objective is to compute a distribution of water-related costs and profit among shale companies to achieve a stable agreement on cooperation among them that allows increasing total benefits and reducing total costs and environmental impacts. We apply different solution methods based on cooperative game theory: The Core, the Dual Core, the Shapley value, and the minmax Core. We solved different case studies including a large problem involving four companies and 207 wells. In this example, individual cost distribution (storage cost, freshwater withdrawal cost, transportation cost, and treatment cost) assigned to each player is included. The results show that companies that adopt cooperation strategies improve their profits and enhance the sustainability of their operations through the increase in recycled water.     

An approximate mathematical framework for resource-constrained multistage batch scheduling

A rigorous representation of the multistage batch scheduling problem is often useless to even provide a good feasible schedule for many real-world industrial facilities. In order to derive a much simpler scheduling methodology, some usual features of multistage batch plants should be exploited. A common observation in industry is that multistage processing structures usually present a bottleneck stage (BS) controlling the plant output level. Therefore, the quality of the production schedule heavily depends on the proper allocation and sequencing of the tasks performed at the stage BS. Every other part of the processing sequence should be properly aligned with the selected timetable for the bottleneck tasks. A closely related concept with an empirical basis is the usual existence of a common batch sequencing pattern along the entire processing structure that leads to define the constant-batch-ordering rule (CBOR). According to this rule, a single sequencing variable is sufficient to establish the relative ordering of two batches at every processing stage in which both have been allocated to the same resource item. This work introduces a CBOR-based global precedence formulation for the scheduling of order-driven multistage batch facilities. The proposed MILP approximate problem representation is able to handle sequence-dependent changeovers, delivery due dates and limited manufacturing resources other than equipment units. Optimal or near-optimal solutions to several large-scale examples were found at very competitive CPU times.     

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     

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     

Optimization of steel production scheduling with complex time-sensitive electricity cost

Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.     

Continuous-time scheduling formulation for multipurpose batch plants

Short-term scheduling of batch processes is a complex combinatorial problem with remarkable impact on the total revenue of chemical plants. It consists of the optimal allocation of limited resources to tasks over time in order to manufacture final products following given batch recipes. This article addresses the short-term scheduling of multipurpose batch plants, using a mixed integer linear programming formulation based on the state-task network representation. It employs both single-grid and multi-grid continuous-time representations, derived from generalized disjunctive programming. In comparison to other multigrid scheduling models in the literature, the proposed multi-grid model uses no big-M constraints and leads to more compact mathematical models with strong linear relaxations, which often results in shorter computational times. The single-grid counterpart of the formulation is not as favorable, as it leads to weaker linear relaxations than the multi-grid approach and is not capable of handling changeover time constraints.