应用混合蚁群算法求解模糊作业车间调度问题

为解决蚁群算法求解时间过长和易陷入局部最优的问题,提出了一种求解模糊作业车间调度问题的混合算法,该算法将蚁群算法用于全局搜索.为了提高搜索效率,根据作业车间调度问题解的特征,提出一种基于关键工序的邻域搜索方法,并使用此邻域搜索方法的禁忌搜索算法嵌入蚁群算法.利用禁忌搜索算法较强的局部搜索能力,提高了蚁群算法的优化能力,改善了作业车间调度问题解的质量.实验结果验证了该混合搜索算法的有效性,其优化效果优于并行遗传算法和禁忌搜索算法.     

基于遗传变邻域混合算法的带交货期的单机车间逆调度方法

针对带交货期的单机逆调度问题,建立以最小化系统调整为目标函数的单机逆调度数学优化模型;利用互补性能,采用串行、并行和嵌入等结构,将遗传算法与变邻域搜索算法相结合,设计出遗传-变邻域搜索算法、遗传-变邻域搜索交替算法和遗传-变邻域搜索协同算法3种混合算法。为产生逆调度激发机制,采用非最优调度法,将随机初始化与局部初始化进行结合,创造逆调度环境;此外,为提高算法的局部搜索能力,基于交叉变异操作等思想来构建四种搜索邻域,通过邻域结构的切换,加强局部搜索能力;最后,将提出的混合算法用于求解不同规模的问题实例,与其他算法的求解结果进行比较,证明提出的混合算法是可行的和有效的。     

置换流水车间调度粒子群优化与局部搜索方法研究

采用粒子群优化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和粒子位置的二维粒子编码方法.为提高粒子群算法的优化性能,在描述了面向置换流水车间调度问题的粒子邻域结构后,提出了三种基于粒子邻域操作的局部搜索方法,分别是基于互换操作、基于插入操作和基于逆序操作的局部搜索方法.计算结果说明,粒子群算法的优化性能好于遗传算法和NEH启发式算法.三种局部搜索算法均能有效地提高粒子群算法的优化性能,采用基于互换操作局部搜索的粒子群算法的优化性能要好于其它两种局部搜索算法.     

基于过滤定向搜索的Job-Shop调度算法及评价

对以Makespan最小为目标的Job Shop调度问题进行了研究。首先对Job Shop调度问题进行了描述,在此基础上建立了一种求解Job Shop调度问题的启发式优化算法———基于过滤定向搜索的算法,同时结合实例对算法的优化过程作了具体描述。最后通过不同规模的Benchmark实例对该算法进行了仿真评价,结果表明基于过滤定向搜索的算法搜索效率高,解的性能好,是一种有效的优化算法。     

求解并行JSP作业车间调度问题的一种混合遗传算法

针对并行JSP作业车间调度问题,将所有工件对应工序按照统一顺序编号,由蚁群算法随机构造初始解,通过重排工序法保证解的可行性;融合遗传算法的选择、交叉、变异操作,加大全局最优解的求解概率,防止陷入局部最优解。在交叉算子中采用随机设置工件固定,以及顺序交叉邻域搜索策略,使得解的多样性性均得到充分保证;实验证明,改进混合遗传算法能够有效提高并行JSP作业车间调度问题的求解。     

基于混合遗传鲸鱼优化算法的柔性作业车间自动导引车融合调度方法

针对柔性作业车间调度问题,考虑自动导引车(AGV)在车间制造过程中只参与装卸和搬运工作,提出一种实现AGV路径规划与柔性作业车间调度集成优化的融合调度模型。采用基于工序排序与机器选择两个子问题的二维向量编码方案,并在解码过程中提出基于最先服务原则的AGV安排策略。对鲸鱼优化算法进行离散化改进,针对性地设计了多种种群初始化策略,引入遗传算法的交叉、变异操作以提升鲸鱼优化算法的全局搜索能力,并嵌入局部搜索算法以达到全局搜索和局部搜索的平衡,构建了一种混合遗传鲸鱼优化算法（HGWOA）来求解该融合调度模型。通过经典测试算例验证了算法性能,并使用正交试验优化了算法参数。研究结果表明,HGWOA算法用于求解柔性作业车间AGV融合调度问题可以获得较好的效果。     

基于混合遗传算法的Job Shop问题研究与实现

遗传算法具有良好的全局搜索能力,在调度问题中得到了广泛的应用。通过对遗传算法进行改进,可以有效避免在求解过程中容易陷入局部最优域的问题。通过采用混合遗传算法,即将模拟退火算法与遗传算法结合,在种群更迭过程中引入了模拟退火操作来求解Job Shop问题。通过实验验证了混合遗传算法的特性,最终算法显示出了遗传算法较好的搜索能力和模拟退火避免过收敛的特性,改进了收敛性能。系统的运行结果满足调度要求,实现了良好的有效性和实用性。     

基于混合粒子群优化算法的置换流水车间调度问题研究

针对最大完工时间最小的置换流水车间调度问题,提出一种粒子群优化算法与变邻域搜索算法结合的混合粒子群优化（hybrid particle swarm optimization,HPSO）算法。在该混合算法中,采用NEH启发式算法进行种群初始化,以提高初始解质量。运用基于随机键的升序排列规则（ranked-or-der-value,ROV）,将连续PSO算法应用于离散置换流水车间调度问题中,提出了一种基于关键路径的变邻域搜索算法,以进一步提高算法的局部搜索能力,使算法在集中搜索和分散搜索之间达到合理的平衡。最后,运用提出的混合算法求解Taillard和Watson基准测试集,并将测试结果与一些代表算法进行比较,验证了该调度算法的有效性。     

基于混合教与学优化算法的炼钢连铸调度

根据炼钢厂的实际生产环境,建立了无等待多工艺路线的炼钢连铸模型,提出一种混合教与学优化算法求解该模型。在混合算法中,引入转换规则的教与学优化算法求解离散问题;采用变邻域搜索调整机器选择,教与学优化算法调整调度顺序的方式,将并行问题串行化。对具体实例进行测试,将人工调度方法、遗传算法、教与学优化和混合教与学优化算法的结果进行比较,验证了所提算法的可行性和有效性。     

基于JIT的面向JSSP一种启发式快速搜索算法

将准时制(JIT)生产模式与启发式搜索算法相结合,应用到作业车间调度问题(Job Shop Scheduling Problem,JSSP)上,针对目前主流的启发式搜索算法(遗传算法GA等)存在的缺陷,提出了一种面向JSSP快速任务调度搜索算法.对国际上通用的benchmark算例模拟实验表明,该算法求解速度快,解质量稳定,应用于实时调度和大规模调度问题具有一定的优势.     

[工艺规划与装配线平衡]两阶段混合算法求解集成工艺规划与调度问题

提出了求解集成式工艺规划与车间调度问题的两阶段混合算法。在工艺规划阶段,使用遗传算法为每个工件生成可选的近优工艺路线集,动态地为车间调度阶段输入已确定的工艺路线;在车间调度阶段,使用蜜蜂交配优化算法快速寻优,设计了蜂王婚飞的流程以保证算法的全局搜索能力,构建了基于不同邻域结构的工蜂培育幼蜂局部搜索策略。使用基准测试集对提出的方法进行验证,并与现有算法进行对比,计算结果证明了提出方法的有效性。     

求解柔性作业车间调度问题的两阶段参数自适应蚁群算法

针对柔性作业车间调度问题,提出了一种新的两阶段蚁群算法求解方案。在算法前期,采用细菌觅食趋化聚类技术判断蚁群所处的状态,自适应调整蚁群算法的参数,使算法快速收敛到全局最优解附近;在算法后期,利用混沌的随机性和遍历性特点来调整参数,有利于算法跳出局部最优。实验结果验证了该两阶段法的有效性。     

基于免疫遗传算法的车间调度问题的研究

根据生命科学中免疫系统的信息处理机制,在一般遗传算法的基础上,将免疫计算和改进的遗传算法(预防近亲结合的多重交叉策略)相结合,建立了一种用于车间调度的免疫遗传算法,通过接种疫苗提高抗体的适应度,通过免疫选择防止种群的退化。针对作业车间调度问题,设计了免疫遗传计算中疫苗的提取和接种方法,即基于加工机器的基因片断抽取疫苗方法和接种方法。通过作业车间调度十个典型标准问题验证,文中所述免疫遗传算法可行,较现有免疫算法、一般遗传算法及一些传统优化设计方法在收敛效率和准确性等方面有很大改进与提高。     

A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints

Process planning and scheduling are two important functions in a modern manufacturing system. Although integrating decisions related to these functions gives rise to a hard combinatorial problem, due to the impressive improvement in system performance which is resulted through this integration, developing effective methods to solve this problem is of great theoretical and practical importance. In this research, after formulating the integrated process planning and scheduling problem as a mathematical program, we propose a hybrid genetic algorithm (GA) for the problem. In the proposed algorithm, problem-specific genetic operators are designed to enhance the global search power of GA. Also, a local search procedure has been incorporated into the GA to improve the performance of the algorithm. The model considers precedence relations among job operations, based on which feasible process plans for each job can be represented implicitly. A novel neighborhood function, considering the constraints of a flexible job shop environment and nonlinear precedence relations among operations, is presented to speed up the local search process. In experimental study, the performance of the proposed algorithm has been evaluated based on a number of problems adopted from the literature. The experimental results demonstrate the efficiency of the proposed algorithm to find optimal or near-optimal solutions.     

Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems

The manufacturing industry continues to be a prime contributor and it requires an efficient schedule. Scheduling is the allocation of resources to activities over time and it is considered to be a major task done to improve shop-floor productivity. Job shop problem comes under this category and is combinatorial in nature. Research on optimization of the job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems. The neighborhoods are selected based on the strategies in the ant colony optimization with dynamic tabu length strategies in the tabu search. The inspiring source of ant colony optimization is pheromone trail that has more influence in selecting the appropriate neighbors to improve the solution. The performance of the algorithm is tested using well-known benchmark problems and is also compared with other algorithms in the literature.     

A hybrid EDA with ACS for solving permutation flow shop scheduling

This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard’s benchmark data sets demonstrate that the proposed algorithm generated high-quality solutions by comparing with the existing population-based search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.     

一种改进蚁群算法在车间作业调度问题中的研究与应用

讨论了蚁群算法在车间作业调度问题中的应用，针对传统蚁群算法求解调度问题的不足，将邻域搜索与蚁群算法结合，通过实验验证了该混合算法的有效性和优化性。     

An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines

This paper addresses the problem of no-wait two-stage flexible flow shop scheduling problem (NWTSFFSSP) considering unrelated parallel machines, sequence-dependent setup times, probable reworks and different ready times to actualize the problem. The performance measure used in this study is minimizing maximum completion time (makespan). Because of the complexity of addressed problem, we propose a novel intelligent hybrid algorithm [called hybrid algorithm (HA)] based on imperialist competitive algorithm (ICA) which are combined with simulated annealing (SA), variable neighborhood search (VNS) and genetic algorithm (GA) for solving the mentioned problem. The hybridization is carried out to overcome some existing drawbacks of each of these three algorithms and also for increasing the capability of ICA. To achieve reliable results, Taguchi approach is used to define robust parameters' values for our proposed algorithm. A simulation model is developed to study the performance of our proposed algorithm against ICA, SA, VNS, GA and ant colony optimization (ACO). The results of the study reveal the relative superiority of HA studied. In addition, potential areas for further researches are highlighted.     

Scheduling jobs under constant period-by-period resource availability to maximize project profit at a due date

In this paper, we propose a lump-sum payment model for the resource-constrained project scheduling problem, which is a generalization of the job shop scheduling problem. The model assumes that the contractor will receive the profit of each job at a predetermined project due date, while taking into account the time value of money. The contractor will then schedule the jobs with the objective of maximizing his total future net profit value at the due date. This proposed problem is nondeterministic polynomial-time (NP)-hard and mathematically formulated in this paper. Several variable neighborhood search (VNS) algorithms are developed by using insertion move and two-swap to generate various neighborhood structures, and making use of the well-known backward–forward scheduling, a proposed future profit priority rule, or a short-term VNS as the local refinement scheme (D-VNS). Forty-eight 20-job instances were generated using ProGen and optimally solved with ILOG CPLEX. The performances of these algorithms are evaluated based on the optimal schedules of the 48 test instances. Our experimental results indicate that the proposed VNS algorithms frequently obtain optimal solutions in a short computational time. For larger size problems, our experimental results also indicate that the D-VNS with forward direction movement outperforms the other VNS algorithms, as well as a genetic algorithm and a tabu search algorithm.