求解任务可拆分多项目协同调度问题的启发式算法

生产项目计划与调度过程中任务可以被拆分为更小粒度的子任务分批次执行,实现缩短项目总工期的优化目标.针对抢占式任务可拆分多项目调度问题,从协同优化角度探讨任务拆分与重组方式,提出一个长工期任务优先拆分、长工期项目优先拆分和高资源利用率项目优先拆分3种任务拆分优先级判断规则,设计一种求解任务可拆分多项目协同调度问题的启发式算法.最后通过数值实例和仿真分析验证了所提出方法在多项目调度总工期的优化效果和求解效率.     

多启发式规则融合粒子群算法的受限项目调度

在船舶生产的现实背景上,对船舶生产过程中如何利用总装平台这一瓶颈资源建立空间资源受限项目调度的问题模型。利用空间资源和分段任务对象的特性,在最大面积优先、最长边优先、BL（Bottom-Left,一种解决布局问题的启发式规则）规则等启发式规则的基础上,提出多启发式规则融合粒子群算法的空间资源受限项目调度算法。将分段任务对象根据几何特性和拖延惩罚因子赋予不同的权值,确定其实际开始时间,再通过最长边优先和BL规则确定其空间位置。设计了具有初始解集并且能够自动识别的粒子群算法,加速其收敛以更快更优地获取分段任务对象序列。通过和其他几种主流的空间调度方法（分支界定和遗传算法）进行不同规模的实验对比,得出该算法在时间复杂度和平均资源利用率方面都有所提高。     

采用优先规则的粒子群算法求解RCPSP

优先规则是解决大规模资源受限的项目调度问题（Resource-Constrained Project Scheduling Problem,RCPSP）强有力的方法,但是单一的优先规则的往往仅在某些特定的问题上表现出良好的性能。以粒子群算法为基础,提出了基于优先规则编码的粒子群算法（Priority Rule based Particle Swarm Optimization,PRPSO）,求解资源受限的项目调度问题。该方法能够通过粒子群算法搜索优先规则和调度生成方案的组合。分别对PRPSO采用串行调度方案、并行调度方案和混合调度方案时,不同任务数和资源强度的问题实例进行了分析。通过对PSPLIB进行测试,结果表明该方法与其它基于优先规则的启发式方法相比有较低的偏差率,因而有较好的性能。     

求解学习型员工项目调度问题的HPSO算法

为提高工作效率并最小化项目工期,研究学习型员工项目调度问题的求解算法。建立相应的0-1型整数非线性规划模型,提出一种混合粒子群优化算法。该算法应用基于优先规则的启发式算法生成优良的初始粒子,引入离散型算子修正经典的粒子速度和位置方程,采用改进的前向递归算法求解粒子目标函数值。数值实验结果表明,在相同运行时间内,该算法能得到比粒子群优化算法更优的解。     

基于优先规则的空间资源约束项目调度算法

任务组占用空间资源项目调度问题需满足组内任务的序关系和人力、设备等常规资源约束,以及空间资源这一特殊资源的约束,同时任务组之间也需满足上述约束,使得该调度问题异常复杂。以船舶建造分段制造问题为背景,建立任务组占用空间资源受限的项目调度问题数学模型,基于并行调度生成方案提出基于优先规则的启发式调度算法,实现对该调度问题的综合求解。实例测试结果表明了该算法的正解性和有效性。     

施工项目调度问题的一种智能优化算法

研究了施工项目进度调度问题,提出了一种基于启发式规则和遗传算法的综合智能优化算法,并在施工项目调度问题的描述、带资源约束的施工项目调度问题的分解方法、遗传算法的编码、交叉、变异方法和解码方法等方面进行了研究。不同规模的数值计算结果表明,该算法在解决复杂工程施工项目调度问题上具有良好的性能,并能较好地适用于带时序、资源约束的施工项目调度问题。     

基于混合遗传算法的资源受限的运输任务调度

在利用串行调度启发式方法解决资源受限的运输任务调度问题（RCTTSP）的基础之上,提出了一种混合遗传算法（HGA）。该算法通过对运输任务执行优先次序进行基因编码,利用串行调度方法获得初始种群,并在遗传个体调度目标值与适应值确定的过程中使用了局部搜索启发式规则,从而充分地结合了遗传算法的全局搜索与启发式方法的局部搜索能力。首先对RCTTSP进行了描述,给出了混合遗传算法的基本原理,然后针对测试案例进行实现,并与单纯使用串行调度方法进行了比较。结果显示,该混合遗传算法能有效地改进调度效果。     

求解无等待流水调度的基于禁忌搜索的混合算法

本文提出了解决最小完工时间的无等待流水调度问题的基于禁忌搜索的混合算法。算法结合了调度规则和禁忌搜索算法的优点,首先利用调度规则构造较好的初始解,既可以加快禁忌搜索算法的收敛速度,也可以降低整个算法的运算量,使算法有更好的工程实用性;然后使用变邻域结构的禁忌搜索算法改进当前解。在保持可达性的基础上,该算法缩小了邻域规模和减少了计算时间。数值仿真实验表明,该算法是有效的。     

柔性作业车间调度分析及其启发式算法

针对多目标柔性作业车间调度问题,基于甘特图和搭积木经验进行了分析,提出了一种组合优先规则和基于此优先规则的启发式算法。组合优先规则面向完工时间、关键机床负荷和总负荷三个指标,改变规则中各数据项的比例可调整三个指标所占的比例。算法采用随机方式调整三个指标的比例,并微调最优解对应的比例,能随机产生多个高质量调度解。对比测试表明,算法求解质量更高,运行速度快,稳定,可直接用于在其他调度算法中产生初始解,或者用于动态调度。     

柔性作业车间调度问题的一种启发式算法

为了研究多目标柔性作业车间调度问题,基于甘特图和搭积木经验进行了分析,提出了一种组合优先规则和基于此优先规则的启发式算法.组合优先规则面向完工时间、关键机床负荷和总负荷三个指标,改变规则中各数据项的比例可调整三个指标所占的比例;算法采用随机方式调整三个指标的比例,并微调最优解对应的比例.能随机产生多个高质量调度解.算法...     

Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Recently, iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition, an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking, the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm, two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so, it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard’s well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately, 85 out of 120 problem instances are further improved with substantial margins.     

Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.     

改进的求解约束P-Median问题的分散搜索算法

对解决约束P-中位问题已有的分散搜索算法进行改进。通过划分中心点服务范围的新方法指派需求点以构造初始解,用基于外包矩形的局部搜索方法来提高邻域解搜索的效率,结合路径重连算法,扩展邻域解的搜索范围,来提高解的质量。实验表明此算法能够得到优化且连续的解。     

认知无线电中能效优化的子载波匹配策略

针对协作认知无线电系统中的能量效率问题,提出一种以最大化能量效率为目标的资源联合分配算法。在满足服务质量要求和功率约束的情况下,首先通过一种能量效率优先的启发式方案对子载波进行匹配,再引入基于拉格朗日对偶算法对其中的功率分配问题进行非线性优化,从而最大限度地提高整个系统的能量效率。仿真实验结果表明,所提算法的能量效率得到显著提升,验证了所提方案的有效性。     

混合决策系统启发式增量属性约简方法研究

对于混合决策系统的属性约简,现有方法主要存在动态效果不佳、复杂度过高,以及约简精度差等问题,为此,提出一种启发式增量属性约简方法。针对混合决策系统的动态波动,基于粗糙集建立了邻域关系模型,根据邻域相对差异对增量属性进行更新。同时,为进一步增强约简算法的动态适应性,引入条件熵求解相对差异。考虑到单纯利用邻域依赖虽然有利于处理样本的分布不均,但是很难获得良好的属性评估,引入粒度模型进行优化,将邻域关系采用粒度重新描述,从而细化邻域关系。利用邻域依赖性得到决策属性度量,构造启发计算,同时,通过条件和决策间的关联度,以及粒度模型的单调,求解出条件和决策共同约束下的邻域关系。再根据决策属性度量作为启发,直至单一属性对子集决策性能不再有影响,完成属性约简。基于数据集的仿真,验证了提出的启发式增量属性约简方法能够降低约简冗余度和约简长度,有效提高属性约简精度和约简时间效率。     

Minimizing the makespan in the non-preemptive job-shop scheduling with limited machine availability

This paper addresses the makespan minimization in a job-shop environment where the machines are not available during the whole planning horizon. The disjunctive graph model is used to represent the schedules and the concept of blocks is generalized to include the unavailability periods of machines. To solve the problem, we develop a taboo thresholding heuristic that uses a new block-based neighborhood function. Some sufficient conditions to eliminate the evaluation of non-improving moves are proposed. Experiments performed on existing problem instances of the literature show the efficiency of the proposed heuristic.     

参照四邻域裁剪样本的图像修复算法

针对结构纹理信息较复杂、破损尺度较大的图像修复问题,提出一种既能保持图像特征又能提高修复速度的参照四邻域裁剪样本的修复算法,将图像修复问题转化为最佳样本的检索过程。首先,提取图像结构信息,并对图像进行区域划分以缩小样本的裁剪与检索范围;其次,为了改进离差平方和（SSD）方法对块的结构信息匹配的忽视,在像素块匹配计算中引入结构对称匹配约束,有效避免了误匹配,提高了图像块匹配精度及样本搜索效率;然后,通过引入结构因子和置信度,结合传统的优先权计算,得到突出结构作用的优先级公式;最后,利用目标块与四邻域块间的重叠区域计算四邻域参照优先级,并根据四邻域提供的可靠参照信息,依据改进的块匹配方法裁剪样本集并检索最佳样本块,直至所有目标块都检索匹配到最佳样本,完成修复。实验结果表明,该算法可以很好地解决纹理模糊和结构错位等问题,在提高图像修复速度的同时,所提算法修复效果的峰值信噪比（PSNR）比其他对比算法平均提高了0.5~1 dB,使得修复后的图像更好地满足视觉连通性,同时能高效地修复一般区域,具有更好的普适性。     

Test-cost-sensitive attribute reduction on heterogeneous data for adaptive neighborhood model

Test-cost-sensitive attribute reduction is an important component in data mining applications, and plays a key role in cost-sensitive learning. Some previous approaches in test-cost-sensitive attribute reduction focus mainly on homogeneous datasets. When heterogeneous datasets must be taken into account, the previous approaches convert nominal attribute to numerical attribute directly. In this paper, we introduce an adaptive neighborhood model for heterogeneous attribute and deal with test-cost-sensitive attribute reduction problem. In the adaptive neighborhood model, the objects with numerical attributes are dealt with classical covering neighborhood, and the objects with nominal attributes are dealt with the overlap metric neighborhood. Compared with the previous approaches, the proposed model can avoid that objects with different values of nominal attribute are classified into one neighborhood. The number of inconsistent objects of a neighborhood reflects the discriminating capability of an attribute subset. With the adaptive neighborhood model, an inconsistent objects-based heuristic reduction algorithm is constructed. The proposed algorithm is compared with the \(\lambda \)-weighted heuristic reduction algorithm which nominal attribute is normalized. Experimental results demonstrate that the proposed algorithm is more effective and more practical significance than the \(\lambda \)-weighted heuristic reduction algorithm.     

Two-machine open shop scheduling with secondary criteria

This paper considers two-machine open shop problems with secondary criteria where the primary criterion is the minimization of makespan and the secondary criterion is the minimization of the total flow time, total weighted flow time, or total weighted tardiness time. In view of the strongly NP-hard nature of these problems, two polynomially solvable special cases are given and constructive heuristic algorithms based on insertion techniques are developed. A strongly connected neighborhood structure is derived and used to develop effective iterative heuristic algorithms by incorporating iterative improvement, simulated annealing and multi-start procedures. The proposed insertion and iterative heuristic algorithms are empirically evaluated by solving problem instances with up to 80 jobs.     

Vehicle routing with cross-dock selection

The vehicle routing problem with cross-dock selection is a variant of the vehicle routing problem containing spatial and load synchronization constraints by which products are transferred and processed via at least one cross-dock. This paper presents a mathematical formulation of the problem and an adaptive large neighborhood search heuristic. Computational experiments on a set of benchmark instances demonstrate the efficiency of the proposed methodology.