Fuzzy flexible resource constrained project scheduling based on genetic algorithm

Both fuzzy temporal constraint and flexible resource constraint are considered in project scheduling. Inorder to obtain an optimal schedule, we propose a genetic algorithm integrated with concepts on fuzzy set theory aswell as specialized coding and decoding mechanism. An example demonstrates that the proposed approach can assistthe project managers to obtain the optimal schedule effectively and make the correct decision on skill training before aproject begins.     

Optimization of project payment schedules with Nash equilibrium model and genetic algorithm

To minimize the deviations of the net present values of project payment for both the owner and the client and optimize project payment schedules, a Nash equilibrium model based on game theory was set up and a genetic algorithm was developed to work out the Nash equilibrium solution with a two-stage backward inductive approach that requires the client responds to the owner’s payment schedule with an activity schedule so as to maximize the client’s net present value of cash flows. A case study demonstrated that a payment schedule at the Nash equilibrium position enables both the owner and the client to gain their desirable interests, thus is a win-win solution for both parties. Despite the computation time of the proposed algrithm in need of improving, combining Nash equilibrium and genetic algorithm into a complete-information dynamic-game model is a promising method for project management optimization.     

Genetic algorithm for scheduling reentrant jobs on parallel machines with a remote server

This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The first and last operations are performed by the same primary machine, implying the reentrance, and the second operation is processed on the single server machine. The order of jobs is predetermined in our context. The challenge is to assign jobs to the primary machines to minimize the makespan. We develop a genetic algorithm（GA） to solve this problem. Based on a simple strategy of assigning jobs in batches on the parallel primary machines, the standardized random key vector representation is employed to split the jobs into batches. Comparisons among the proposed algorithm, the branch and bound（BB） algorithm and the heuristic algorithm, coordinated scheduling（CS）, which is only one heuristic algorithm to solve this problem in the literature, are made on the benchmark data. The computational experiments show that the proposed genetic algorithm outperforms the heuristic CS and the maximum relative improvement rate in the makespan is 1.66%.     

作业车间调度问题解的不可行性检测算法和快速修复算法

为了判别作业车间调度问题的解的可行性,提出了一种基于图论的启发式判别算法,并通过实例验证了方法的正确性.提出了普适于作业车间调度问题的快速修补新算法,可以对于作业车间调度问题的不可行解进行修正使之变成可行解.判别算法和修补算法在最不利情形下的计算复杂性均为O(n),判别算法在最有利情形下的计算复杂性为O(2 |J|+|...     

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

Intheprojectmanagement,theschedulingproblemforshorteningthedurationundertheconstraintofre sourcesiscalledresourceconstrainedprojectschedulingproblem (RCPSP) [1] .Withtheincreasingcompetitionwithinglobalmarket ,thereisanurgentneedforenter priseintegration .Asanexample ,modernbusinessesnolongercompeteasindividualcompaniesbutratherassup plychains .Thus ,thesuccessofasinglecompanyde pendsonitsabilitytomanageandintegratenetworksofvariousbusinessrelationshipsinwhichcertainactivitiesarelinkedtoothe…     

Solving Hitchcock''s transportation problem by a genetic algorithm

Genetic algorithms (GAs) employ the evolutionary process of Darwin's nature selection theory to find the solutions of optimization problems. In this paper, an implementation of genetic algorithm is put forward to solve a classical transportation problem, namely the Hitchcock's Transportation Problem (HTP), and the GA is improved to search for all optimal solutions and identify them automatically. The algorithm is coded with C and validated by numerical examples. The computational results show that the algorithm is efficient for solving the Hitchcock's transportation problem.     

动态车辆调度系统的滚动时域调度算法

提出了一类特殊的动态车辆调度问题--动态独占性带时间窗口装卸货问题.给出了问题的数学描述,分析了其静态性质,并把问题简化为不对称带时间窗口旅行商问题.提出了求解该动态问题的滚动时域调度算法,通过时域的不断滚动,不仅可以跟踪问题的动态变化,还由于每次滚动只对部分客户进行处理,可以减少问题的求解时间.并分析了算法的3个要素:当前客户窗口、当前客户窗口的调度和滚动策略.测试结果验证了算法在求解动态车辆调度问题中的有效性.     

基于遗传机制的蚁群算法求解连续优化问题

A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem.Each component has a seed set.The seed in the set has the value of component,trail information and fitness.The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed.The genetic method is used to form new solutions from the solutions got by the ants.Best solutions are selected to update the seeds in the sets and trail information of the seeds.In updating the trail information,a diffusion function is used to achieve the diffuseness of trail information.The new algorithm is tested with 8 different benchmark functions.     

求解0-1背包问题的遗传算法

提出了一种求解0-1背包问题的遗传算法,该算法首先设计出基于适应度的自适应变异策略,提高了变异的科学性和新算法的搜索能力;然后提出了基于单位价值信息和满足约束最大化的双优化策略,提高了求解的质量.3个0-1背包问题的仿真实验表明:与已有的HGA算法和GGA算法相比,新算法在求解质量上具有一定优势.     

遗传算法的研究

遗传算法是一种基于生物自然选择与遗传机理的随机搜索与优化方法．近年来，由于遗传算法求解复杂优化问题的巨大潜力及其在工业工程领域的成功应用，这种算法受到了国内外学者的广泛关注．本文介绍了遗传算法的研究现状和基本原理，概述了它的理论和技术，并对遗传算法的性能作了分析．以推销员旅行问题为例来展示遗传算法在求解复杂优化问题上的优越性．     

基因群进化受控的自然遗传算法

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一种求解不等式约束优化问题的新算法

针对基本遗传算法收敛速度缓慢现象，提出了基于多父体杂交和按较差个体顺序变异的改进策略，从而加快收敛速度，提高了算法性能，通过验算表明，改进后的算法，能够迅速找到全局最优解。     

基于遗传算法的阵列相位误差校正

针对声纳水听器阵列比较突出的相位误差,研究了一种基于遗传算法的阵列校正方法。该方法利用无相位误差时阵列协方差矩阵具有Toeplitz结构的特性构造代价函数,并以此作为遗传算法中进化个体的适应度函数,由算法搜索的最优个体得到阵元相位误差的估值。计算机仿真结果表明,该方法可以明显地减小阵列相位误差对MUSIC算法DOA估计结果的影响。     

一类基于人工免疫系统与极值优化的混合算法求解流水作业调度问题

The permutation flowshop scheduling problem (PFSP) is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system (AIS) with a strong local search ability of extremal optimization (EO) algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.     

一种有效的多目标混合遗传算法

设计了一种基于支配关系下的局部搜索方法，将此局部搜索方法嵌入到多目标遗传算法中，从而提出一种有效的求解多目标优化问题的混合遗传算法。为加速遗传算法在全局优化问题上的收敛性，发挥传统数值优化算法在计算速度与计算精度上的优势，在遗传算法中镶嵌一个多目标线搜索算子。线搜索算子与遗传算法中的选择算子、交叉算子和变异算子共同作用，使全局搜索和局部搜索都能够很好的实现。数值实验表明，该混合遗传算法能求得问题的数量更多、分布更广的Pareto最优解。     

一种改进的多值遗传算法研究

该文通过在多值遗传算法中引入模式基因来引导种群的搜索方向,算法在运行过程中不断搜索基因的各种组合,是一种有导向性的组合,模式基因的提取和利用加强了基因组合的导向性,有效提高算法的搜索效率。仿真实验表明了改算法的有效性。     

基于遗传算法的网格简化研究

针对三角面网格提出了一种新的网格简化方法,简化过程主要包括网格删除和网格重构。根据需建立的数量比例权重来进行冗余网格删除工作,然后利用遗传算法建立修正适应度函数来重构网格,达到三角网格数量的精简与形状匹配最优化目标。最后通过一实例讨论与对比分析,验证了该方法的有效性和准确性。     

一种新型免疫遗传算法在图像增强上的应用

将一种新型免疫遗传算法应用到图像的增强处理中,利用其快速搜索能力,对给定测试图像,自适应地变异、搜索、直至最终确定变换函数的最佳参数α,β值,从而实现图像的增强.与穷举法相比,大大节约了求解的时间和计算的复杂度,并通过对自然图像的仿真实验论证该方法的有效性.     

Eucalyptus中基于能量消耗的调度算法研究

能量消耗是云计算研究中一个十分重要的问题,介绍了开源云项目Eucalyptus,分析了其核心调度算法及在考虑能量消耗的应用场景中存在的问题,利用虚拟机在线迁移技术提出了基于能量消耗的调度算法。实验证明,基于能量消耗的调度算法性能优于Eucalyptus现有的核心调度算法。最后总结了需要进一步提高的方面。     

多解组合优化问题的改进混合遗传算法

通过引入小生境技术、最优保存策略和近优淘汰策略,设计了用于求多解组合优化问题的混合遗传算法,用所设计的混合遗传算法对某工厂实际问题进行了计算,获得了许多最优解,结果非常令人满意。