基于后悔值准则的个性化产品鲁棒调度

个性化产品的生产过程具有非重复性,致使工序的加工时间不确定且难以估计其概率信息。因此,传统的确定调度和随机调度方法不再适用。采用最小化最大后悔值的鲁棒优化方法,研究变速平行机加工环境下个性化产品的生产调度问题。首先,采用区间情景描述不确定的加工时间,构建基于后悔值准则的个性化产品鲁棒调度模型;其次,证明任意调度方案带来的最大后悔值可通过求解一个指派问题得到;然后,提出基于混合整数规划和迭代松弛过程的两种精确算法获取最优解;最后,通过仿真实验评估两种精确算法的有效性,结果表明基于混合整数规划的精确算法明显优于迭代松弛算法,并且可以快速求解中小规模的调度问题。     

混合工作日历下批量生产柔性作业车间多目标调度方法

提出了一种混合工作日历下批量生产柔性作业车间多目标调度方法。考虑设备的混合工作日历约束,构建了以生产周期最短、制造成本最低为优化目标的批量生产柔性作业车间多目标调度模型。设计了一种带精英策略的非支配排序遗传算法(NSGA II)求解该模型。算法中,采用“基于工序和设备的分段编码”方式分别对工序和设备进行编码;采用“基于工序和设备的分段交叉和变异方式”进行交叉和变异操作,采用“遗传算子改进策略”保证交叉、变异后子代个体的可行性;解码操作采用“基于平顺移动的原理”和“基于工作日历的时间推算技术”推算工序的调整开始、调整结束、加工开始和加工结束时刻。最后,通过案例分析验证了所提方法的有效性。     

基于自适应遗传算法的模具生产调度研究

研究了一种基于自适应遗传算法的模具企业车间作业调度算法,建立了调度问题的数学模型,给出了具体的遗传操作算子设计方法.针对离散型模具生产的工艺特征,设计了生产单元分配方法,将生产单元作为调度对象.该算法改进了传统的基于工序的编码方法,给出了一种动态调整交叉概率和变异概率的计算方法.对实际模具企业的生产调度求解结果表明,该...     

集装箱码头岸吊作业调度建模及调度策略研究

岸吊作业调度对集装箱码头整体运营效率具有重要影响.综合考虑岸吊实际作业中的特有约束,包括预定义顺序约束、依赖于作业次序的设备调整时间、岸吊干涉约束,建立岸吊作业调度问题的混合整数规划模型.针对混合装卸模式,使用启发式算法生成预定义作业顺序,在此基础上采用基于连续贝作业策略的启发式算法对问题进行求解.数据实验结果显示基于SPT规则和连续贝作业的启发式算法能有效利用混合装卸带来的时间节省,减少设备调整时间,对实际岸吊作业调度具有指导意义.     

基于改进遗传算法的偏柔性作业车间调度

偏柔性作业车间调度是生产管理中的重要问题。由于模型和计算的复杂性,传统优化方法往往难以得到最优解。采用改进遗传算法求解偏柔性作业车间的调度问题,设计相应的编码方法,利用所生成的染色体以及通过遗传操作得到的染色体生成可行的调度方案。基于工序串和机器串的编码方法,采用精英解保留策略、轮盘赌选择策略和基于划分集的交叉策略,提出基于均匀分布试验的变异法则,引入贪婪式解码方法对偏柔性作业车间调度进行求解。实例仿真表明,该算法在求解偏柔性作业车间调度方面具有良好的效率和优越性。     

面向生产调度的订货量分配问题研究

为研究多品种批量制造环境下由于供应商交货数量不确定造成物料不齐套进而导致生产计划不可行的问题,以多个供应商和单个制造商组成的二级供应链为背景,提出面向生产过程的供应商选择与订货量分配模型。以包含订货、采购、库存以及拖期惩罚成本的期望总成本最小化为目标,在传统供应商能力限制、订货数量区间要求以及产品生产调度约束的基础上,考虑允许供应商延期交货且拖期时间依赖供应商可靠度的情形,建立了相应的混合整数随机规划模型。针对所研究问题的复杂性及模型特点,采用基于局部搜索和变异机制的改进离散粒子群优化算法对模型进行求解,结合具体交货情景下的工程实例对模型可行性进行了验证,通过与其他方法进行比较,表明所提算法的有效性。     

地震作用下主动减震结构的时滞离散最优控制

对地震作用下线性时滞结构的离散最优控制方法进行研究,按时滞量为采样周期的整数倍和非整数倍两种情况,将时滞连续控制系统离散为形式上不包含时滞的标准离散形式,采用连续时间形式的性能指标函数,按照离散最优控制方法进行控制设计.所得出的控制律表达式中,除了含有当前的状态反馈外,还包含有前若干步控制项的组合.最后结合一个三自由度建筑结构模型进行仿真计算,结果表明,算法中采用连续时间形式的性能指标函数优于离散形式的性能指标函数,此方法易于保证控制系统的性能和稳定性,而且可以看到,控制系统的时滞量并非越短越好.     

An integrated scheduling algorithm with device dependence deferred constraint

针对目前综合调度中没有考虑设备有关延迟约束(DDC)影响调度效果的问题,提出了存在设备有关延迟约束的综合调度的算法.该算法在标准工序、设备有关延迟工序(DDP)和包含设备有关延迟约束的加工工艺树等概念的基础上,将等待延迟时间转化为设备有关延迟工序,使存在DDC的调度问题转变为较易解决的一般综合调度问题,再用拟关键路径法( ACPM)确定工序的调度次序,最后用前沿贪心规则确定工序的开始时间.为了使设备有关延迟工序影响的工序尽早开始,进一步提出了设备有关延迟工序配合调整的策略.实验表明,该调度算法能够有效解决存在设备有关延迟约束的综合调度问题,可在不提高算法复杂度的前提下,提高调度结果的精度并减少产品总的加工时间.     

面向订单的生产计划与调度的综合优化

研究了一个多订单环境下的生产计划与调度集成优化问题,以实现准时生产为目标,综合考虑产品装配结构约束的订单任务计划与订单产品零部件的加工调度,采用直接面向客户订单的工序调度模式建立了计划和调度的综合优化整数规划模型.设计了带精英策略的蚁群算法作为该数学模型的求解方法,并通过对比试验为该算法选取最佳的搜索参数.实例仿真结果表明,所建模型的正确性以及蚁群算法求解该问题的可行性和有效性.     

基于最早完工时间疫苗的免疫遗传算法求解FJSLP

在生产过程中,车间作业调度的主要任务是确定工件在各台设备上的加工顺序,合理的调度方案能提高设备的利用率和企业的效益。针对柔性车间分批调度问题,采用免疫遗传算法进行求解。在疫苗技术方面,依据工件工序的加工信息,选择工件工序所能最早完工的机器作为疫苗,对相应工件个体机器码进行接种。通过对案例的测试,结果表明所采取的方法能求得更好的调度方案,减少作业总流程时间。     

Design of predictable production scheduling model using control theoretic approach

As one of the most important planning and operational issues in manufacturing systems, production scheduling generally deals with allocating a set of resources over time to perform a set of tasks. Recently, control theoretic approaches based on nonlinear dynamics of continuous variables have been proposed to solve production scheduling problems as an alternative to traditional production scheduling methods that deal with decision-making components in discrete nature. The major goal of this paper is to improve predictability and performance of an existing scheduling model that employs the control theoretic approach, called distributed arrival time controller (DATC), to manage arrival times of parts using an integral controller. In this paper, we first review and investigate unique dynamic characteristics of the DATC in regards to convergence and chattering of arrival times. We then propose a new arrival time controller for the DATC that can improve predictability and performance in production scheduling. We call the new mechanism the double integral arrival-time controller (DIAC). We analyse unique characteristics of the DIAC such as oscillatory trajectory of arrival times, their oscillation frequency, and sequence visiting mechanism. In addition, we compare scheduling performance of the DIAC to the existing DATC model through computational experiments. The results show that the proposed system can be used as a mathematical and simulation model for designing adaptable manufacturing systems in the future.     

A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion

In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly, a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly, some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA, thus providing an alternative solution approach to the literature that the RKGA can be well suited.     

Auction-based approach to resolve the scheduling problem in the steel making process

Steel production is an extremely complex process and determining coherent schedules for the wide variety of production steps in a dynamic environment, where disturbances frequently occur, is a challenging task. In the steel production process, the blast furnace continuously produces liquid iron, which is transformed into liquid steel in the melt shop. The majority of the molten steel passes through a continuous caster to form large steel slabs, which are rolled into coils in the hot strip mill. The scheduling system of these processes has very different objectives and constraints, and operates in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. The steel making process, which includes steel making followed by continuous casting, is generally the main bottleneck in steel production. Therefore, comprehensive scheduling of this process is critical to improve the quality and productivity of the entire production system. This paper addresses the scheduling problem in the steel making process. The methodology of winner determination using the combinatorial auction process is employed to solve the aforementioned problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way of enhancing the efficiency of allocating the assets. In this paper, the scheduling problem in steel making has been formulated as a linear integer program to determine the scheduling sequence for different charges. Bids are then obtained for sequencing the charges. Next, a heuristic approach is used to evaluate the bids. The computational results show that our algorithm can obtain optimal or near-optimal solutions for combinatorial problems in a reasonable computation time. The proposed algorithm has been verified by a case study.     

A multi-objective scatter search for the ladle scheduling problem

We consider the ladle scheduling problem, which can be regarded as a vehicle routing problem with semi-soft time windows and adjustment times. The problem concerns allocating ladles to serve molten steel based on a given steelmaking scheduling plan, and determining the modification operations for the empty ladles after the service process. In addition, combining the controllable processing time of molten steel, the other aspect of the problem is to determine the service start times taking into consideration the technological constraints imposed in practice. We present a non-linear mathematical programming model with the conflicting objectives of minimising the occupation ratio of the ladles and maximising the degree of satisfaction with meeting the soft windows. To solve the multi-objective model, we develop a new scatter search (SS) approach by re-designing the common components of SS and incorporating a diversification generator, a combination method and a diversification criterion to conduct a wide exploration of the search space. We analyse and compare the performance of the proposed approach with a multi-objective genetic algorithm and with manual scheduling adopted in practical production using three real-life instances from a well-known iron–steel production plant in China. The computational results demonstrate the effectiveness of the proposed SS approach for solving the ladle scheduling problem.     

Decomposition in automatic generation of Petri nets for manufacturing system control and scheduling

Despite the efforts in developing Petri net models for manufacturing control and scheduling, the generation of Petri net models cannot be automated for agile manufacturing control and scheduling without difficulties. The problems lie in the complexity of Petri net models. First of all, it is difficult to visualize the basic manufacturing process flow in a complex Petri net model even for a Petri net modelling expert. The second problem is related to the complexity of using Petri net models for manufacturing system scheduling. In this paper, a decomposition methodology in automatic generation of Petri nets for manufacturing system control and scheduling is developed. The decomposition methodology includes representing a manufacturing process with the Integrated Definition 3 (IDEF3) methodology, decomposing the manufacturing process based on the similarity of resources, transforming the IDEF3 model into a Petri net control model, and aggregating sub Petri net models. Specifically, a sequential cluster identification algorithm is developed to decompose a manufacturing system represented as an IDEF3 model. The methodology is illustrated with a flexible disassembly cell example. The computational experience shows that the methodology developed in this paper reduces the computational time complexity of the scheduling problem without significantly affecting the solution quality obtained by a simulated annealing scheduling algorithm. The advantages of the methodology developed in this paper include the combined benefits of simplicity of the IDEF3 representation of manufacturing processes and analytical and control properties of Petri net models. The IDEF3 representation of a manufacturing process enhances the manmachine interface.     

Scheduling multi-purpose batch plants with junction constraints

There is an increasing trend in the chemical process industry to operate flexible batch plants because of their capability to manufacture multiple products simultaneously by sharing the same process resources. In this paper, the scheduling of multi-purpose batch chemical plants with junction (header) constraints is considered. A mixed-integer non-linear model for the scheduling of multi-purpose batch chemical plants is formulated that considers the connection between equipment sets, transfer times, variable batch sizes, alternative process plans, and batch merging. Because of the computational time complexity of the batch scheduling problem, a heuristic scheduling algorithm that minimizes the total tardiness is developed lo solve the model.     

Algorithm for a class of constrained weight least square problems and applications

An algorithm, based on ordinal optimisation (OO) and sensitive theories, is presented to solve a class of constrained weight least square problems with continuous and discrete variables. the proposed algorithm can cope with an enormous amount of computational complexity problems and has a high probability of obtaining a good enough solution according to the oo theory. this method has some advantages, such as computational efficiency, numerical stability and the superiority of the good enough solution. the proposed algorithm is explicit, compact and easy to program. test results demonstrate that the proposed approach is more computational-efficient than other existing approaches for solving constrained-state estimation problems with continuous and discrete variables on the ieee 30-bus and the ieee 118-bus systems.     

一种多自由度阻尼体系动力问题的高阶分析方法

传统动力时程直接积分法多采用低阶数值格式,需要选择非常小的时间步距才能获得满足精度要求的动力分析结果.该文将结构动力时程分析的积分求微法推广至多自由度情形,发展了一种具有较高计算效率的多自由度阻尼体系的动力时程高阶分析方法.将相邻的ρ个时步组成一个待求解时段,基于多自由度体系动力响应积分解,以精细积分法结合秦九韶算法计...     

Synchronization of yard truck scheduling and storage allocation in container terminals

Yard truck scheduling and storage allocation, as two separate subproblems in port operations, have been extensively studied in the past decades. However, from the operational point of view, they are highly interdependent. This article proposes an integer programming model in which yard truck scheduling and storage allocation problems are formulated as a whole for heterogeneous import containers. Different stacking times at yard blocks is modelled as well. The objective of the proposed model is to reduce the congestion and waiting time of yard trucks in the terminal so as to decrease the makespan of discharging containers. Owing to the inherent computational complexity, a genetic algorithm and a greedy heuristic algorithm have been designed. Computational experiments show that the proposed genetic algorithm and greedy algorithm are both effective in solving the studied problem.     

Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown

In the steelmaking-continuous casting (SCC) production process, machine breakdown is one of the most common disturbances which may make the current schedule unrealisable. Existing rescheduling models for machine breakdown only employ one constraint that charges cannot be processed on this machine in its failure period. However, this method is effective for steelmaking furnace breakdown and refining furnace breakdown but invalid for continuous caster breakdown. Due to the production characteristics of continuous caster, reallocating a casting order and a continuous caster for each unfinished charge on the broken down continuous caster is necessary before making a new schedule. Different reallocation strategies have different impacts on charge’s processing time and processing stage route in the dynamic scheduling process. Therefore, SCC dynamic scheduling for the continuous caster breakdown is different from the other machines. In this paper, the impacts of these strategies are studied, and a dynamic scheduling model which can be used to generate a new schedule for each strategy is built. To obtain a high-quality solution in acceptable computational time for this model with NP-hard feature, a hybrid algorithm featuring a genetic algorithm combined with a general variable neighbourhood search is developed based on the problem-specific characteristics. Computational experiments on practical production data show that the proposed rescheduling method is effective for SCC dynamic scheduling with continuous caster breakdown.