Heuristic search algorithms for lot streaming in a two-machine flowshop

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling.     

A hybrid genetic algorithm for solving no-wait flowshop scheduling problems

A hybrid genetic algorithm is proposed in this paper to minimize the makespan and the total flowtime in the no-wait flowshop scheduling problem, which is known to be NP-hard for more than two machines. The Variable Neighborhood Search is used as an improvement procedure in the last step of the genetic algorithm. First, comparisons are provided with respect to several techniques that are representative of the previous works in the area. Then, we compare the results given by three proposed algorithms. For the makespan criterion as well as for the total flowtime, the computational results show that our algorithms are able to provide competitive results and new best upper bounds.     

基于混合遗传算法的柔性作业车间调度研究

为更有效地求解柔性作业车间调度问题,提出一种混合遗传算法(蚁群-遗传算法)。在分层法的基础上,首先采用蚁群算法解决工艺路线选择问题,再通过遗传算法解决传统的作业车间调度问题。在混合遗传算法求解过程中,不断地在前期优化中获取调度知识,用于指导后期的优化过程。通过标准案例测试,验证了混合遗传算法对于解决柔性作业车间调度问题的有效性。     

A multi-objective electromagnetism algorithm for a bi-objective hybrid no-wait flowshop scheduling problem

This article studies multi-objective hybrid no-wait flowshop scheduling problems to minimize both makespan and total tardiness. This article mathematically formulates the problem using an effective multi-objective mixed integer linear programming models. Since the problem is NP-hard and it is difficult to find an optimal solution in a reasonable computational time, an efficient multi-objective electromagnetism algorithm (MOEA) is presented as the solution procedure. Electromagnetism algorithm is known as a flexible and effective population-based algorithm utilizing an attraction/repulsion mechanism to move the particles towards optimality. MOEA is carefully evaluated for its performance against multi-objective immune algorithms and the adaptation of a well-known multi-objective simulated annealing in the relevant literature by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.     

模具电火花加工车间的非同等并联机混合遗传算法

针对模具企业电火花加工车间的生产调度特点,提出了一种非同等并联机的调度数学模型.与此同时,建立了一种遗传与模拟退火的混合算法,并应用于模具企业电火花加工车间,实际算例说明了该数学模型和求解方法的可靠性和有效性.     

An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines

In this paper, we address the hybrid flow shop scheduling problems with unrelated parallel machines, sequence-dependent setup times and processor blocking to minimize the makespan and maximum tardiness criteria. Since the problem is strongly NP-hard, we propose an effective algorithm consisting of independent parallel genetic algorithms by dividing the whole population into multiple subpopulations. Each subpopulation will be assigned with different weights to search for optimal solutions in different directions. To further cover the Pareto solutions, each algorithm is combined with a novel local search step and a new helpful procedure called Redirect. The proposed Redirect procedure tries to help the algorithm to overcome the local optimums and to further search the solution space. When a population stalls over a local optimum, at first, the algorithm tries to achieve a better solution by implementing the local search step based on elite chromosomes. As implementing the local search step is time-consuming, we propose a method to speed up the searching procedure and to further increase its efficiency. If the local search step failed to work, then the Redirect procedure changes the direction and refreshes the population. Computational experiments indicate that our proposed improving procedures are thriving in achieving better solutions. We have chosen two measures to evaluate the performance of our proposed algorithms. The obtained results clearly reveal the prosperity of our proposed algorithm considering both measures we have chosen.     

两机零等待流水车间调度问题的启发式算法

为实现两机零等待流水车间调度问题的总流程时间最小化,结合问题的结构信息提出了一种快速求解近优解的启发式算法。在该类问题中,工件在每台机器上的操作包括调整、加工和移除3部分,且调整和移除时间都与工件的加工时间相互分离。首先分析了该类问题的优化性质,结合优化性质进而构造出求解算法。在中小规模和大规模问题上,将启发式算法的结果分别与最优解和最优解的下界值进行了比较。大量数值计算实验表明了该算法的有效性和解决大规模实际问题的潜力。     

多目标同顺序流水作业的局部搜索算法

针对求解最小化最大完工时间和总流程时间的多目标同顺序流水作业问题,提出了一个多目标局部搜索算法。针对两个目标,用现有的构造性算法生成两个解,作为该算法的初始解,然后从这两个初始解出发,以贪婪的方式求出新的Pareto最优解集,持续改进Pareto前沿。选择新的Pareto解的条件是该解既不被原解支配,也不被产生原解的解所支配,同时对某个目标改进最大。当所有解都陷入局部极小时,扰动已得到的Pareto解集,然后从扰动后的解集出发重新搜索。初始解和选择新的Pareto解的方法对算法性能有显著的影响。在基准问题上,与已有文献中的算法比较,结果表明所提算法的总体性能更优,特别是对较大规模的问题,此差异更具有显著性。     

带多处理器任务的动态混合流水车间调度问题

研究了具有多处理器任务的混合流水车间调度问题,且考虑相邻两阶段之间的运输时间、机器故障和工件动态到达的实际生产特征。由于该问题不但求解非常复杂,对它的不同部分的简化还会使其变成其他不同的典型调度问题,探讨该类问题的近似解法具有挑战性和广义性。据此分别采用结合次梯度算法的拉格朗日松弛算法、结合次梯度和bundle算法的交替算法（交替S＆B算法）的拉格朗日松驰算法进行求解。对多达100个工件的问题进行测试,结果表明,所设计的算法能够在合理的CPU时间内产生较好的时间表。     

An improved genetic algorithm for robust permutation flowshop scheduling

In order to deal with uncertainties, a robust schedule for M-machine permutation flowshop is proposed. The presented robust schedule is aimed to maximize the probability of ensuring that makespan will not exceed the expected completion time. An improved genetic algorithm (GA) with a new generation scheme is developed, which can preserve good characteristics of parents in the new generation. Experiments are performed to get robust schedules for well-known Car and Rec permutation flowshop problems, taken from OR library. The schedules obtained from the improved GA are compared with the schedules formed by well-known heuristic in literature. Computational results show that the permutation flowshop schedules obtained from improved GA are robust to produce an affirmative percentage increase in the probability of getting makespan less than expected completion time.     

基于改进遗传算法的柔性作业车间调度

针对柔性作业车间调度问题(FJSP)的特点和发展现状,提出一种基于基本遗传算法的改进算法。构建了一种新的染色体表达方案,将染色体分为工序染色体部分和机床染色体部分。通过加权处理设计了适应度函数,将多目标优化问题转变为线性优化问题。针对改进的染色体表达方案,重新设计了种群初始化算法,采用复制、交叉,以及变异操作策略优化调度方案。通过实例验证了该算法对FJSP的优化过程,试验结果表明了该算法的可行性和有效性。     

具有柔性加工时间的机器人制造单元调度问题改进遗传算法

为克服传统遗传算法在求解具有柔性加工时间的机器人制造单元调度问题时易出现早熟收敛、冗余迭代等缺陷,提出了改进遗传算法。该算法采用基于工件搬运顺序的染色体编码,并根据调度问题特征,设计构造型启发式算法来生成初始种群,避免了大量不可行染色体的产生,提高了后续操作的优化质量。同时,在交叉变异操作中引入局部邻域搜索,通过对子代邻域的局部寻优提高了算法的收敛速度。最后,分别应用该算法和传统遗传算法求解六个基准案例,实验结果验证了该算法的有效性。     

A novel hybrid genetic algorithm for the open shop scheduling problem

In this paper, a hybrid genetic algorithm is proposed for the open shop scheduling problem with the objective of minimizing the makespan. In the proposed algorithm, a specialized crossover operator is used that preserves the relative order of jobs on machines and a strategy is applied to prevent from searching redundant solutions in the mutation operator. Moreover, an iterative optimization heuristic is employed which uses the concept of randomized active schedules, a dispatching index based on the longest remaining processing time rule and a lower bound to further decrease the search space. Computational results show that the proposed algorithm outperforms other genetic algorithms and is very competitive with well-known metaheuristics available in the literature.     

An effective hybrid genetic algorithm for the job shop scheduling problem

From the computational point of view, the job shop scheduling problem (JSP) is one of the most notoriously intractable NP-hard optimization problems. This paper applies an effective hybrid genetic algorithm for the JSP. We proposed three novel features for this algorithm to solve the JSP. Firstly, a new full active schedule (FAS) procedure based on the operation-based representation is presented to construct a schedule. After a schedule is obtained, a local search heuristic is applied to improve the solution. Secondly, a new crossover operator, called the precedence operation crossover (POX), is proposed for the operation-based representation, which can preserve the meaningful characteristics of the previous generation. Thirdly, in order to reduce the disruptive effects of genetic operators, the approach of an improved generation alteration model is introduced. The proposed approaches are tested on some standard instances and compared with other approaches. The superior results validate the effectiveness of the proposed algorithm.     

运输能力有限混合流水车间调度的改进拉格朗日松弛算法

为改善在制品库存和能耗问题,研究了从钢铁实际生产环境提炼出的运输能力有限的动态混合流水车间调度问题.将运输机视为虚拟机器,可将原问题转换成与其等价的不考虑运输能力但在偶数阶段机器有不可用时间段的动态混合流水车间调度问题,其中机器不可用时间段取决于其运送的工件.对转换后的问题建立数学模型,提出基于阶段分解的拉格朗日松弛算法进行求解,该算法将优先级约束松弛到目标函数中,将拉格朗日松弛问题分解为多个阶段级子问题,进而设计了动态规划求解这些带任意权重和机器不可用时间段的并行同构机调度子问题.对不同问题规模的测试结果表明,所提算法能够在较短的运行时间内获得满意的近优解.     

自适应混合EDA求解一类三阶段装配流水线调度问题

针对生产过程中广泛存在的一类三阶段装配流水线调度问题,即带序相关设置时间的三阶段装配流水线调度问题,提出一种自适应混合分布估计算法,用于最小化平均完成时间和最大延迟时间的加权和。提出初始种群和初始概率分布模型生成机制,使概率分布模型能适当地积累较多优质解的信息,以提高AHEDA在进化初期的搜索能力。设计了基于信息熵的概率分布模型自适应更新机制和保留优良模式的新种群采样生成方法,增强了算法的全局搜索能力。引入基于Insert的邻域搜索来增强算法的局部搜索能力。最后通过仿真实验和算法比较验证了AHEDA的有效性。     

批量流水线调度问题的混合离散蛙跳算法

研究了以提前/拖后惩罚指标为目标的批量流水线调度问题,给出了该问题的数学模型以及小批量的调整策略.根据蛙跳算法的原理,采用基于工序的编码方式并利用两点交叉操作设计了新的位置生成公式,提出了解决该问题的离散蛙跳算法.为进一步增强算法的开发能力和效率,结合扰动策略、模拟退火概率接受准则和插入邻域搜索对该算法进行改进.对随机生成的实例进行了广泛的试验,结果表明了所提算法的高效性.     

求解生产批量计划问题的改进量子进化算法

针对遗传算法和粒子群算法在求解生产批量计划问题中易陷入局部最优解的问题,提出了改进的量子进化算法.对各周期项目计划产量的决策变量进行基于概率幅的量子比特个体编码,在迭代求解的过程中通过约束违反度比较个体的支配关系,有效指导种群向合理解进化,并根据当前迭代次数动态调整旋转角机制控制基因位的坍塌速度,在进化后期尽量保留最优个体的基因信息以提高算法的收敛速度和求解精度.实验结果表明了该算法的有效性.     

An adaptive genetic algorithm with multiple operators for flowshop scheduling

Genetic algorithms (GAs) are a class of effective parallel searching algorithms inspired by the idea of “survival of the fittest”, which has been successfully applied to a variety of problems, especially in the fields of manufacturing and scheduling. However, it is reported that traditional GAs often suffer from the weaknesses of premature convergence as well as parameter and operator dependence. So far, many improved methods with adaptive parameters or hybrid structures have been proposed, but there is little literature considering the adaptive control of genetic operators. In this paper, an adaptive GA (AGA) with multiple operators is proposed for flowshop scheduling, which is a typical NP-hard optimisation problem with many industrial applications and has been widely studied in both academic and engineering fields. In AGA, multiple different genetic operators are employed in an adaptive hybrid way to enhance the exploration and exploitation abilities so as to prevent premature convergence and achieve superior performance. It especially important to stress that the utilising ratio of each operator for hybridisation is adaptively and dynamically controlled during the evolutionary searching process. Simulation results based on benchmarks demonstrate the effectiveness of AGA by contrast with traditional GAs. And the effect of the adaptive control of the operator and the effects of some parameters on the optimisation performance are discussed as well.