用MC-QPSO算法求解并行流水车间调度问题

针对并行流水车间调度问题的特点,提出了一种基于多种群协同进化的改进量子粒子群算法（MC-QPSO）进行求解。首先将整个量子粒子种群分解为多个子种群,然后各个子种群独立地演化,并通过周期性共享搜索信息,以获得对自身信息的更新。最后,通过具体仿真实例进行了求解验证,结果表明,在求解并行流水车间调度问题时,基于多种群协同的量子粒子群算法,在收敛速度、寻优性能等方面,都要优于遗传算法。     

一种新的混合粒子群算法求解置换流水车间调度问题

针对粒子群算法易早熟的缺点,提出了一种结合迭代贪婪(IG)算法的混合粒子群算法。算法通过连续几代粒子个体极值和全局极值的变化判断粒子的状态,在发现粒子出现停滞或者粒子群出现早熟后,及时利用IG算法的毁坏操作和构造操作对停滞粒子和全局最优粒子进行变异,变异后利用模拟退火思想概率接收新值。全局最优粒子的改变会引导粒子跳出局部极值的约束,增加粒子的多样性,从而克服粒子群的早熟现象。同时,为了使算法能更快找到或逼近最优解,采用了循环迭代策略,在阶段优化结果的基础上,周而复始循环迭代进行求解。将提出的混合粒子群算法应用于置换流水车间调度问题,并在问题求解时与几个具有代表性的算法进行了比较。结果表明,提出的算法能够克服粒子群早熟,在求解质量方面优于其他算法。     

新型教与同伴学习粒子群算法求解作业车间调度问题

针对最小化最大完工时间的单目标作业车间调度问题,提出了新型教与同伴学习粒子群算法。通过教学阶段融合多邻域搜索,采用多样性变异策略以及同伴学习阶段采用混合学习策略三个方面的改进操作,扩大了种群的多样性,避免算法陷入局部最优,算法收敛速度和寻优性能有了显著提高。通过作业车间调度问题FT、LA系列测试实例的对比实验,验证了新型教与同伴学习粒子群算法是解决单目标作业车间调度问题的有效方法。     

求解PFSP的双种群协同学习算法

在人工蜜蜂群算法的基础上, 提出一种双种群协同学习算法.该算法根据个体适应度高低把蜜蜂群划分为 两个子群, 并重新定义子群的学习交流机制.在10个常用的基准测试函数上与其他4个常用的群体智 能算法进行比较, 比较结果表明, 所提出算法的性能有明显改进.采用双种群协同学习算法求解置换流水车间调度问题, 在一 些著名的中大规模测试问题包括21个Reeves实例和40个Taillard实例上进行 测试, 结果表明, 所提出的算法优于其他算法, 能有效解决置换流水车间调度问题.     

求解FFS问题的混合搜索机制粒子群算法

针对柔性流水车间调度(flexible flow shop scheduling,FFS)问题,提出了一种混合搜索机制粒子群算法(multi-search mechanism particle swarm optimization algorithm,MMPSO),以期获得柔性流水车间调度问题的优化解。在分析柔性流水车间调度问题特点的基础上,设计了针对该问题的粒子信息编码方案,提出了瓶颈机器消除算法以提升初始种群的质量;同时在个体极值搜索中采用NEH-Greedy搜索算法,在全体极值搜索中采用SADA(simulated snnealing disturb algorithm)搜索算法以扩大搜索范围,提高可行解质量,加快收敛速度,在算法迭代搜索过程中对全体极值进行RPA(random perturbation algorithm)操作以避免算法陷入局部最优。实验结果表明,MMPSO算法能够以较快的收敛速度获得柔性流水车间调度问题的一个较好的优化解。     

多车场多车型车辆调度问题的改进粒子群算法

多车场多车型车辆调度问题优化是物流配送中的典型NP难解问题,针对传统的粒子群算法存在收敛速度慢,易早熟收敛等问题,提出了一种改进的粒子群优化算法。该算法对种群中的粒子采用一定的概率进行柯西变异,使算法跳出局部最优解。将算法应用于多车场多车型车辆调度问题优化,算例证明该算法求解多车场多车型车辆调度问题是可行的,并且优于标准粒子群优化算法。     

粒子群算法求解具有机器灵活性的FFSP

对柔性流水车间调度问题(FFSP)进行了分析阐述,在此基础上对某饲料厂的饲料生产过程建立了具有机器灵活性的柔性流水车间调度模型,该模型中存在多台制粒机,既能加工大颗粒饲料,又能加工小颗粒饲料,但是必须在开始加工之前确定各台机器的用途,增加了柔性流水车间调度的难度。利用新型的粒子群算法以最小化最大完工时间为目标对该模型求解,为了克服粒子群算法易陷入局部极值的缺点,提出基于位置相似度的邻域结构,并对邻域内的较优粒子采用基于最大完工时间排序的学习方式进行局部搜索。实验结果表明,该方法有利于克服粒子群算法的早熟缺陷,有效地解决了饲料生产调度问题,有一定的应用价值。     

一种求解典型JSP的改进离散粒子群优化算法

针对NP-hard性质的作业车间调度问题, 设计了一种改进的离散粒子群优化算法。引入遗传算法交叉算子和变异算子来实现粒子的更新, 并将变异思想和模拟退火算法思想融入该算法中对全局最优粒子的邻域进行局部搜索, 很好地防止了算法出现早熟收敛。通过将该算法和标准粒子群优化算法用于求解典型JSP, 计算结果对比表明, 改进的算法具有很强的全局寻优能力; 就综合解的质量和计算效率而言, 改进算法优于标准粒子群优化算法。同时, 将该算法结果与文献中其他相关算法结果进行比较, 验证了该改进算法的有效性。该算法能够有效地、高质量地解决作业车间调度问题。     

基于演化策略算法的置换流水车间调度优化研究

采用演化策略算法求解置换流水车间调度问题,提出了一种基于工件次序的二维实数编码方法.设计了一种基于父代个体两点交叉互换的重组算子来生成子代个体,针对个体编码,采用局部基因随机重新生成的变异算子.计算结果说明,演化策略算法能够有效地对置换流水车间调度问题进行优化,其优化性能好于遗传算法和NEH启发式算法以及粒子群算法.     

改进离散粒子群算法求解柔性流水车间调度问题

针对以最小化完工时间为目标的柔性流水车间调度问题(FFSP),提出了一种改进离散粒子群(DPSO)算法.所提算法重新定义粒子速度和位置的相关算子,并引入编码矩阵和解码矩阵来表示工件、机器以及调度之间的关系.为了提高柔性流水车间调度问题求解的改进离散粒子群算法的初始群体质量,通过分析初始机器选择与调度总完工时间的关系,首次提出一种基于NEH算法的最短用时分解策略算法.仿真实验结果表明,该算法在求解柔性流水车间调度问题上有很好的性能,是一种有效的调度算法.     

A discrete particle swarm optimization algorithm with self-adaptive diversity control for the permutation flowshop problem with blocking

This paper proposes a discrete particle swarm optimization (DPSO) algorithm for the m-machine permutation flowshop scheduling problem with blocking to minimize the makespan, which has a strong industrial background, e.g., many production processes of chemicals and pharmaceuticals in chemical industry can be reduced to this problem. To prevent the DPSO from premature convergence, a self-adaptive diversity control strategy is adopted to diversify the population when necessary by adding a random perturbation to the velocity of each particle according to a probability controlled by the diversity of the current population. In addition, a stochastic variable neighborhood search is used as the local search to improve the search intensification. Computational results using benchmark problems show that the proposed DPSO algorithm outperforms previous algorithms proposed in the literature and that it can obtain 111 new best known upper bounds for the 120 benchmark problems.     

A discrete differential evolution algorithm for the permutation flowshop scheduling problem

Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO07, pp. 126–33] presented a new and novel discrete differential evolution algorithm for the permutation flowshop scheduling problem with the makespan criterion. On the other hand, the iterated greedy algorithm is proposed by [Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033–49] for the permutation flowshop scheduling problem with the makespan criterion. However, both algorithms are not applied to the permutation flowshop scheduling problem with the total flowtime criterion. Based on their excellent performance with the makespan criterion, we extend both algorithms in this paper to the total flowtime objective. Furthermore, we propose a new and novel referenced local search procedure hybridized with both algorithms to further improve the solution quality. The referenced local search exploits the space based on reference positions taken from a reference solution in the hope of finding better positions for jobs when performing insertion operation. Computational results show that both algorithms with the referenced local search are either better or highly competitive to all the existing approaches in the literature for both objectives of makespan and total flowtime. Especially for the total flowtime criterion, their performance is superior to the particle swarm optimization algorithms proposed by [Tasgetiren, M. F., Liang, Y. -C., Sevkli, M., Gencyilmaz, G. (2007). Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European Journal of Operational Research, 177(3), 1930–47] and [Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A. (2007). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineering, doi:10.1016/j.cie.2007.09.006]. Ultimately, for Taillard’s benchmark suite, four best known solutions for the makespan criterion as well as 40 out of the 90 best known solutions for the total flowtime criterion are further improved by either one of the algorithms presented in this paper.     

基于新邻域结构的Memetic 算法求解流水车间调度问题

流水车间调度是一类典型的生产调度问题,属于NP-难问题.针对传统的最优化方法难以求解大规模问题,提出了一个Memetic算法,在算法的局部搜索中使用一种新型的基于NEH的邻域结构,并且其邻域规模随着搜索的进行能够动态变化,可以大大提高算法的搜索能力.通过对标准Benchmark问题的测试,所得结果表明提出的基于新邻域结构的Memetic算法具有较好的性能,并且优于已有文献中的粒子群算法.     

An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion

An ILS algorithm is proposed to solve the permutation flowshop sequencing problem with total flowtime criterion. The effects of different initial permutations and different perturbation strengths are studied. Comparisons are carried out with three constructive heuristics, three ant-colony algorithms and a particle swarm optimization algorithm. Experiments on benchmarks and a set of random instances show that the proposed algorithm is more effective. The presented ILS improves the best known permutations by a significant margin.     

An improved particle swarm optimization algorithm for flowshop scheduling problem

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the “alldifferent” constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.     

A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.     

两机无等待流水车间调度问题的性质

针对两机无等待流水车间调度问题,提出目标函数最大完工时间最小化的快速算法,并给出算法的复杂度。分析两机无等待流水车间调度问题的排列排序性质,证明了两机无等待流水车间调度问题的可行解只存在于排列排序中,排列排序的最优解一定是两机无等待流水车间调度问题的最优解。最后研究了同时包含普通工件和无等待工件的两机流水车间调度问题的复杂性,为进一步研究两机无等待流水车间调度问题提供了理论依据。     

A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan

A common assumption in the classical permutation flowshop scheduling model is that each job is processed on each machine at most once. However, this assumption does not hold for a re-entrant flowshop in which a job may be operated by one or more machines many times. Given that the re-entrant permutation flowshop scheduling problem to minimize the makespan is very complex, we adopt the CPLEX solver and develop a memetic algorithm (MA) to tackle the problem. We conduct computational experiments to test the effectiveness of the proposed algorithm and compare it with two existing heuristics. The results show that CPLEX can solve mid-size problem instances in a reasonable computing time, and the proposed MA is effective in treating the problem and outperforms the two existing heuristics.     

Scatter search based particle swarm optimization algorithm for earliness/tardiness flowshop scheduling with uncertainty

Considering the imprecise nature of the data in real-world problems, the earliness/tardiness (E/T) flowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization (SSPSO) algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search (SS) algorithm into the frame of particle swarm optimization (PSO) algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution (DE) scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.